Heating area dynamic distribution control method and device, electric fire stove and storage medium

Abstract

The invention relates to the technical field of heating area dynamic distribution control, and discloses a heating area dynamic distribution control method and device, an electric fire stove and a storage medium, and the method comprises the steps: obtaining an external power supply capability parameter in real time as a constraint, and generating a heat demand index of each heating zone in combination with a cooking task portrait corresponding to a pot, and the partition power is calculated by taking minimization of the thermal demand deviation as an optimization target. The intelligent energy distribution system has the advantages that intelligent energy dynamic distribution under the condition that power supply is limited is achieved, cooking interruption or overload caused by insufficient power is effectively avoided, and stable operation of the system is ensured; limited electric energy is preferentially and accurately matched to key heat requirements of different cooking stages and food types, the success rate and effect of multi-task concurrent cooking are improved, invalid energy consumption is reduced, and therefore the cooking quality and the overall energy efficiency are both considered in the limited power supply environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of heating area dynamic allocation control, and in particular to a heating area dynamic allocation control method and device, an electric stove and a storage medium. BACKGROUND

[0002] At present, the multi-area heating control of the electric stove mainly focuses on the pot detection and the basic power distribution, and lacks intelligent adaptability under the condition of external power supply capacity fluctuation or limitation. The traditional scheme usually assumes sufficient power supply and adopts a fixed or simple proportional distribution strategy, which is easy to cause the decline of heating efficiency, the interruption of cooking process or the uneven heating of food when the available power is insufficient. With the increasing complexity of household electricity load and the popularity of new energy power supply scenarios, how to maintain the stability and efficiency of the cooking process under limited power supply capacity has become a key technical bottleneck to improve the intelligent degree and user experience of the electric stove. SUMMARY

[0003] Therefore, it is necessary to propose a heating area dynamic allocation control method, device, electric stove and storage medium for the existing heating area dynamic allocation control problem.

[0004] A heating area dynamic allocation control method, the method comprising: obtaining a current external power supply capacity parameter for supplying power to a specified electric stove; wherein the current external power supply capacity parameter comprises at least one of the available maximum power and the available energy quota; obtaining the target partitions of each pot on the cooking surface to obtain a target partition set; obtaining the current cooking object information of each pot and generating the current cooking task image corresponding to each pot; wherein the current cooking object information comprises at least any one of the food category, the cooking stage, the target temperature interval, and the uniformity requirement level; According to the target partition set and each current cooking task image, the heat demand index of each target partition is calculated; wherein the heat demand index comprises at least one of the temperature fluctuation tolerance, the temperature fluctuation tolerance, and the temperature fluctuation tolerance. Under the constraint of the current external power supply capacity parameter, the partition power parameter of each target partition is calculated in a way of minimizing the heat demand deviation cost; According to the partition power parameter, the current working parameter of each target partition is set.

[0005] Further, the step of obtaining the current cooking object information of each pot and generating the current cooking task image corresponding to each pot comprises: obtaining the food information of each pot, and obtaining the food image in each pot through a preset camera; According to the food information of each pot and the food features obtained through food image recognition; According to the food features, a corresponding cooking food image of each pot is generated.

[0006] Further, after the step of setting the current working parameters of each target partition according to the partition power parameters, the method further comprises: Periodically monitoring real-time cooking information of each target partition; wherein, the real-time cooking information comprises real-time working parameters, real-time external power supply capability parameters and real-time cooking object information; Comparing the real-time cooking information with current cooking information to obtain a comparison result; wherein, the current cooking information comprises current working parameters, current external power supply capability parameters and current cooking object information; Judging whether there is any change threshold exceeding a preset change condition set in the comparison result; If there is any change threshold exceeding a preset change condition set in the comparison result, the working parameters of each target partition are reset according to the real-time cooking information.

[0007] Further, the heat demand index comprises minimum power to reach the target temperature interval and power fluctuation range, and in the step of calculating the heat demand index of each target partition according to the target partition set and each current cooking task image, the step of calculating the minimum power to reach the target temperature interval and the power fluctuation range comprises: Obtaining the thermal inertia coefficient, unit power temperature rising coefficient and heat dissipation coefficient of each current cooking task image to form a partition heat response parameter set corresponding to the target partition; According to the partition heat response parameter set, calculating the minimum power required for the partition to reach the target temperature interval and the allowed power fluctuation range.

[0008] Further, the step of calculating the partition power parameters of each target partition in a manner of minimizing heat demand deviation cost under the constraint of satisfying the current external power supply capability parameters comprises: Inputting the heat demand index, the current external power supply capability parameters and the target partition set into a preset initial model to obtain a target model; wherein, the initial model is a mathematical model; Solving the target model in a manner of minimizing heat demand deviation cost to obtain the partition power parameters of each target partition.

[0009] Further, after the step of setting the current working parameters of each target partition according to the partition power parameters, the method further comprises: detecting whether there is a target subzone without heating work in each of the target subzones; If there is a target subzone without heating work in each of the target subzones, the target subzone without heating work is excluded from the target subzone set, and the subzone power parameters of each target subzone are regenerated.

[0010] Further, the step of obtaining the target subzone of each pot on the cooking surface to obtain the target subzone set comprises: detecting the coverage boundary of each pot by an infrared detection method; calculating the coverage area based on the coverage boundary of each pot; regarding the area with a coverage area greater than a set area as a target subzone.

[0011] A heating area dynamic allocation control device, the device comprises: A first obtaining module is configured to obtain a current external power supply capability parameter of a specified electric heating stove; wherein the current external power supply capability parameter comprises at least one of available maximum power and available energy quota; A second obtaining module is configured to obtain a target subzone of each pot on the cooking surface to obtain a target subzone set; A third obtaining module is configured to obtain current cooking object information of each pot and generate a current cooking task image corresponding to each pot; wherein the current cooking object information comprises at least one of food category, cooking stage, target temperature interval, and uniformity requirement level; A first calculation module is configured to calculate a heat demand index of each target subzone according to the target subzone set and each current cooking task image; wherein the heat demand index comprises at least one of temperature rise rate demand, temperature holding demand, and temperature fluctuation tolerance; A second calculation module is configured to calculate a subzone power parameter of each target subzone in a manner of minimizing heat demand deviation cost under the constraint of the current external power supply capability parameter; A setting module is configured to set a current working parameter of each target subzone according to the subzone power parameter.

[0012] An electric stove comprises a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the following steps: obtain a current external power supply capability parameter of a specified electric heating stove; wherein the current external power supply capability parameter comprises at least one of available maximum power and available energy quota; obtain a target subzone of each pot on the cooking surface to obtain a target subzone set; Obtain current cooking object information of each of the cookware and generate a current cooking task image corresponding to each of the cookware; wherein the current cooking object information at least includes any one of food category, cooking stage, target temperature interval, and demand level for uniformity; According to the target partition set and each of the current cooking task images, calculate a heat demand index of each target partition; wherein the heat demand index at least includes one of temperature rise rate demand, temperature holding demand, and temperature fluctuation tolerance; Under the constraint of satisfying the current external power supply capability parameter, calculate a partition power parameter of each target partition in a manner of minimizing heat demand deviation cost; Set a current working parameter of each of the target partitions according to the partition power parameter.

[0013] A computer readable storage medium stores a computer program, when the computer program is executed by a processor, the processor executes the following steps: Obtain a current external power supply capability parameter for supplying power to a specified electric heating stove; wherein the current external power supply capability parameter at least includes one of available maximum power and available energy quota; Obtain target partitions of each of the cookware on the cooking surface to obtain a target partition set; Obtain current cooking object information of each of the cookware and generate a current cooking task image corresponding to each of the cookware; wherein the current cooking object information at least includes any one of food category, cooking stage, target temperature interval, and demand level for uniformity; According to the target partition set and each of the current cooking task images, calculate a heat demand index of each target partition; wherein the heat demand index at least includes one of temperature rise rate demand, temperature holding demand, and temperature fluctuation tolerance; Under the constraint of satisfying the current external power supply capability parameter, calculate a partition power parameter of each target partition in a manner of minimizing heat demand deviation cost; Set a current working parameter of each of the target partitions according to the partition power parameter.

[0014] The beneficial effects of the present application: by acquiring external power supply capability parameters in real time as constraints, combining the corresponding cooking task image generation of the pot to generate heat demand indicators of each heating partition, and taking the minimum heat demand deviation as the optimization target to calculate the partition power, intelligent energy dynamic allocation under the condition of limited power supply is realized, effectively avoiding the cooking interruption or overload caused by insufficient power, and ensuring the stable operation of the system; by preferentially and accurately matching the limited electric energy to the key heat demand of different cooking stages and food types, the success rate and effect of multi-task concurrent cooking are improved; at the same time, the systematic optimization scheduling reduces the invalid energy consumption under the premise of ensuring the heating uniformity and temperature stability, so as to balance the cooking quality and overall energy efficiency in the limited power supply environment. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0016] Among them: Figure 1 It is an application environment diagram of the heating area dynamic allocation control method in an embodiment; Figure 2 It is a flow chart of the heating area dynamic allocation control method in an embodiment; Figure 3 It is a structural block diagram of the heating area dynamic allocation control device in an embodiment; Figure 4 It is a structural block diagram of the electric stove in an embodiment. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0018] Figure 1 It is an application environment diagram of the heating area dynamic allocation control in an embodiment. Refer to Figure 1The heating area dynamic allocation control method is applied to a heating area dynamic allocation control system. The heating area dynamic allocation control system comprises a terminal 110 and a server 120. The terminal 110 and the server 120 are connected through a network, and the terminal 110 can be a desktop terminal or a mobile terminal, and the mobile terminal can be at least one of a mobile phone, a tablet computer, a notebook computer, etc. The server 120 can be implemented by an independent server or a server cluster composed of multiple servers.

[0019] As shown in Figure 2 In one embodiment, a heating area dynamic allocation control method is provided. The method can be applied to a terminal or a server, and the embodiment is exemplified by application to a terminal. The heating area dynamic allocation control method specifically comprises the following steps: S1: Obtain a current external power supply capability parameter of a specified electric heating stove; wherein the current external power supply capability parameter comprises at least one of available maximum power and available energy quota; S2: Obtain target partitions of each pot on the cooking surface to obtain a target partition set; S3: Obtain current cooking object information of each pot and generate a current cooking task image corresponding to each pot; wherein the current cooking object information comprises at least one of food category, cooking stage, target temperature interval, and uniformity requirement level; S4: Calculate a heat demand index of each target partition according to the target partition set and each current cooking task image; wherein the heat demand index comprises at least one of temperature rise rate demand, temperature holding demand, and temperature fluctuation tolerance; S5: Under the constraint of the current external power supply capability parameter, calculate a partition power parameter of each target partition in a manner of minimizing heat demand deviation cost; S6: Set a current working parameter of each target partition according to the partition power parameter.

[0020] As described in step S1 above, the current external power supply capability parameters are obtained, including the maximum available power and the available energy quota, etc. These parameters are used to evaluate the heating capacity of the electric stove under the current power supply conditions. Specifically, the maximum power refers to the highest power value that the electric stove can safely use within a certain time, which directly affects the number of simultaneously heated pots and the power configuration of the partitions. The available energy quota refers to the maximum energy limit allowed by the power grid or energy management system during use, which is affected by external power demand and may also depend on the characteristics of the power source (e.g., whether the home uses solar or other renewable energy). By monitoring these power supply capability parameters, the system can accurately determine the power distribution of the heating zones based on the actual situation, laying a reliable foundation for subsequent dynamic energy allocation control.

[0021] As described in step S2 above, the target partition set of the pots is obtained by identifying the physical location of the pots and the heating partitions designed on the stove surface. The system needs to number each heating area on the stove surface and accurately identify the area covered by each pot, taking the covered area as the target partition of the corresponding pot. Therefore, according to the size, shape, and design style of the pot, the size of the corresponding target partition is different. Specifically, it can be identified by infrared or by shooting images.

[0022] As described in step S3 above, the current cooking task image corresponding to the pot is generated. The cooking object information of each pot is obtained, including at least the food category, the cooking stage, the target temperature interval, and the demand level for heating uniformity. These information can be obtained through user input, pre-set recipes, or intelligent sensors. For example, different food categories (such as meat, vegetables, rice, etc.) have different temperature requirements during cooking, and the required power and heating method are also quite different at different cooking stages. By integrating these cooking object information, the system can understand the heating requirements for the pot under the current conditions and form a cooking task image. In this way, the subsequent heat demand index calculation will be based on a more accurate foundation, ensuring that the final energy allocation is more in line with the actual needs of the user.

[0023] As described in step S4 above, the heat demand index of each target partition is calculated. The heat demand index is a key parameter for evaluating the power required by each target partition under the current cooking conditions. It may include multiple dimensions such as temperature rise rate demand, temperature maintenance demand, and temperature fluctuation tolerance. In other words, the system needs to set the heating strategy for each target partition according to the user's required cooking effect. For example, if a target partition is cooking food that needs to be heated as quickly as possible, the system will increase the heat demand of that partition accordingly. Conversely, if the target partition is cooking food that needs to be kept at a constant temperature, the heat demand index may be set lower.

[0024] As described in step S5 above, the partition power parameters are calculated based on minimizing the cost of heat demand deviation. After calculating the heat demand index, the system needs to calculate the partition power parameters of each target partition by minimizing the cost of heat demand deviation, under the constraint of satisfying the current external power supply capacity parameters. This process is implemented through optimization algorithms, such as neural network models or mathematical models. This application preferably uses a non-integer programming model, focusing on how to satisfy the heat demand of multiple target partitions as much as possible under given power supply constraints. A cost function can be set to reflect the difference between the heat demand index and the actual power allocation. Specifically, when there is a deviation between the actual temperature of the target partition and the set target temperature, the system will adjust the power output according to the magnitude of the deviation. In this process, the system automatically optimizes the power allocated to each target partition to achieve the minimum deviation of heat demand in each partition while ensuring that the total power does not exceed the constraint of external power supply capacity. The non-integer programming model uses mathematical optimization techniques to help the system solve for the optimal power allocation scheme under given conditions and constraints, so as to maximize the cooking effect and improve energy efficiency. The core purpose of the non-integer programming model is to find the optimal values ​​of a set of decision variables so that the value of the objective function is minimized or maximized. For electric stoves, the objective function is typically to minimize the heat demand deviation, that is, to make the actual power output as close as possible to the heat demand index of each target zone, thereby optimizing the cooking effect. The model needs to satisfy a series of constraints, such as: Resource constraints: For example, the total power cannot exceed the external power supply capacity parameters.

[0025] Operational constraints: The power output range of each zone must be no less than the set minimum power and must not exceed its maximum power.

[0026] Temperature constraints: Ensure that the heating of each target zone can reach or be maintained within the set target temperature range. These constraints are expressed as inequalities or equations through mathematical models to ensure the practical feasibility of the solution results.

[0027] Decision variables typically represent the power output value for each target partition. In the model, these variables may be continuous (e.g., allowing for fine-tuning of power) or non-integer (e.g., the power scheduling of an electric stove is not necessarily based on integer units) to improve flexibility and adapt to different heating needs.

[0028] Solve the established non-integer programming model using optimization algorithms. Commonly used algorithms include: Simplex method: It is particularly suitable for linear programming problems with continuous variables.

[0029] Branch and bound method: applicable to nonlinear programming problems involving integer variables.

[0030] Heuristic algorithms, such as genetic algorithms or particle swarm optimization, are used for complex scenarios that are difficult to solve using traditional methods.

[0031] Through optimization, the system uses an iterative algorithm to gradually approach the optimal solution. Each iteration evaluates the current power allocation scheme, calculates the objective function value, and checks the constraint satisfaction. When termination conditions are met (such as the number of iterations or convergence of the objective function), the final output solution represents the optimal power parameters for each target zone. The calculated results are fed back to the electric stove control system to adjust the operating parameters of each target zone. The system can continuously update the model based on real-time operating status and periodically recalculate optimizations to adapt to dynamically changing cooking demands and external power supply conditions.

[0032] As described in step S6 above, the current operating parameters for each target zone are set based on the calculated zone power parameters. This is a crucial step in achieving intelligent control of the electric stove. By distributing the required power level to each target zone, the system ensures that they begin operation and heating according to the set parameters. The operating parameters include specific power output, heating time, and gradient adjustment. The system updates and adjusts these parameters in real time based on the type of cookware, the cooking requirements of the food, and the external power supply capacity to maintain efficient heat utilization. Through these operating parameters, the electric stove can achieve precise heat control, ensuring satisfactory cooking results while maximizing energy savings. It should be noted that these current operating parameters are constructed based on existing information; if changes occur subsequently, real-time data needs to be obtained to reset the parameters.

[0033] In one embodiment, step S3, which involves obtaining the current cooking object information of each of the cookwares and generating a current cooking task profile corresponding to each cookware, includes: S301: Obtain food information for each pot and acquire food images in each pot through a preset camera; S302: Food features identified based on food information and food images from various cookware; S303: Generate a cooking food image corresponding to each cookware based on the food characteristics.

[0034] As described in step S301 above, food information and images of each cookware are acquired. Food information includes basic parameters such as food type, weight, required cooking time, and expected doneness. This information can be obtained by the user inputting or selecting preset recipes on the device interface, or through direct user interaction. In addition, the system uses a designated camera to capture real-time images of the food inside the cookware. This image acquisition not only confirms the type of food in the cookware but also provides more intuitive food characteristics, such as color, shape, and volume. By combining this visual information, the system can gain a more comprehensive understanding of the current cooking status, providing a solid data foundation for subsequent food feature analysis and the generation of cooking task profiles.

[0035] As described in step S302 above, food features are obtained based on food information and image recognition. The acquired food images are processed using image recognition technology, and combined with previously obtained food information, relevant food features are extracted. Specifically, the acquired food images are first processed, including denoising, normalization, and feature extraction. Computer vision algorithms (such as convolutional neural networks, CNNs, etc.) are applied to classify, label, and discriminate the food features. The system can identify the specific type of food, such as whether it is meat or vegetables, and extract other features, including the color intensity, texture, and shape of the food. These features are crucial for understanding the cooked state of food, as different foods exhibit different appearances and textures at different cooking stages. By combining food information with features obtained from image recognition, the system can construct a relatively comprehensive food feature database, providing detailed basis and support for generating cooking task profiles in subsequent steps.

[0036] As described in step S303 above, a cooking food profile is generated for each cookware based on the food characteristics. This profile not only provides a comprehensive description of the food being cooked but also reflects various dynamic changes during the cooking process. The generated cooking food profile includes information in multiple dimensions, such as food category, required heating temperature range, current cooking stage (e.g., preheating, boiling, stewing), time and energy consumption requirements, etc. As the cooking process progresses, the system needs to periodically update the cooking food profile, continuously acquire changes in the food within the cookware, and adjust the heating strategy accordingly. Using data mining and machine learning algorithms, the extracted food features are associated with recipes and cooking rules, assigning each food feature a corresponding weight to accurately reflect the heat requirements of different foods at different stages. After comprehensively establishing these cooking task profiles, the system can dynamically and specifically adjust the power of the heating area based on this profile, ultimately ensuring optimized cooking results and higher energy efficiency.

[0037] In one embodiment, after step S6 of setting the current operating parameters of each target partition according to the partition power parameters, the method further includes: S701: Periodically monitor the real-time cooking information of each of the target zones; wherein, the real-time cooking information includes real-time operating parameters, real-time external power supply capability parameters, and real-time cooking object information; S702: Compare the real-time cooking information with the current cooking information to obtain a comparison result; wherein, the current cooking information includes the current working parameters, the current external power supply capability parameters, and the current cooking object information; S703: Determine whether any of the comparison results exceed any one of the preset change thresholds in the set of change cases; S704: If any of the comparison results exceeds any one of the preset change thresholds, then the working parameters of each target partition are reset according to the real-time cooking information.

[0038] As described in step S701 above, the system periodically monitors the real-time cooking information of each target zone. This allows for timely capture of changes during the cooking process and adjustments to heating parameters accordingly. Real-time cooking information includes several key pieces of information: real-time operating parameters, real-time external power supply capacity parameters, and real-time cooking object information. Real-time operating parameters typically refer to the current power output, temperature, and operating status of each zone, helping to assess the current heating effect and its deviation from the expected target. Real-time external power supply capacity parameters refer to the maximum available power and energy quota obtained by the system at a specific moment; this information directly affects the power allocation strategy of each target zone. Real-time cooking object information involves the state of the food inside the pot, temperature changes, and its doneness. This monitoring stage is typically accomplished using embedded sensors. The frequency of information collection can be set according to actual needs to ensure the real-time nature and validity of the data, thus providing a reliable information foundation for subsequent comparisons and adjustments.

[0039] As described in step S702 above, real-time cooking information is compared with current cooking information to assess the difference between the actual operating state of the cookware and the expected effect, thereby forming a comparison result. Current cooking information typically includes set operating parameters, external power supply parameters, and expected information about the object being cooked. By comparing real-time data with these set values, the system can determine whether the actual effect of each target zone meets the predetermined cooking target. This comparison process can be performed using mathematical models, threshold determination, etc. For example, the system will check whether the real-time temperature reaches the expected target value according to a pre-set reasonable range, ensuring that the food is cooked within the specified time and temperature. The result of this step provides necessary information support for subsequent adjustments, ensuring that the system can sensitively react promptly to any discrepancies in the cooking process. The comparison result typically includes the following aspects: a comparison between actual power output and expected power; the system compares the real-time power output of each target zone with the previously set zone power parameters. This comparison helps the system determine whether each zone is heating according to the set power. If the actual power of a zone is significantly lower than the expected value, this indicates that the zone may have insufficient heating. Comparison of Real-Time Temperature and Target Temperature: The system needs to monitor the real-time temperature of the food in the target zone and compare it with the set target temperature. This comparison determines whether the cooking state of the food meets expectations. For example, if the actual temperature is lower than the target temperature, the power output needs to be increased; conversely, the power may need to be reduced or the heating method adjusted. Comparison of Heat Demand Indicators and Actual Performance: This includes comparing previously calculated heat demand indicators (such as the minimum required power and power fluctuation range) with the data obtained from real-time monitoring. The system checks the magnitude of the heat demand deviation to ensure that the heating effect of each target zone achieves the ideal cooking effect. Comparison of Power Supply Capacity and Actual Demand: The system also monitors changes in the current external power supply capacity and compares it with the target output power demand. This ensures that the electric stove operates within the power supply conditions and prevents poor heating conditions due to insufficient power supply. If it is found that the power supply capacity is insufficient to meet the zone demand, the system will make corresponding adjustments to avoid overload. System Status Monitoring and Anomaly Judgment: The system may also record some key status information, such as burning, sound, smell, etc., and compare these real-time feedbacks with the normal operating status of the system. This helps the system to more comprehensively assess the safety and suitability of the cooking process, and to react and make adjustments in a timely manner.

[0040] As described in step S703 above, the system determines whether any of the comparison results exceed preset change thresholds. These preset change thresholds may be upper and lower limits set for the degree of change in operating parameters (such as temperature and power output) and fluctuations in external power supply parameters. By comparing with these thresholds, the system can determine whether the current cooking process is within the ideal range. For example, if the actual temperature deviation exceeds the set allowable range of 3°C, the system will determine it as an "abnormal" state and trigger a subsequent adjustment mechanism. The threshold settings are usually based on actual cooking experience and historical data analysis to ensure that energy waste in emergency situations is minimized without affecting food quality. Through this judgment mechanism, the system ensures that the monitoring of the cooking process is sufficiently intelligent and sensitive.

[0041] As described in step S704 above, the operating parameters of the target zones are reset based on real-time cooking information. If the comparison result shows that the change exceeds a preset threshold, the operating parameters of each target zone are readjusted based on the real-time cooking information to ensure that its heat demand and cooking objectives are met. Specifically, resetting the parameters may include adjusting the current power output, updating the heating time, or changing the heating strategy (e.g., switching from continuous heating to intermittent heating mode) to adapt to the new cooking state and conditions. This process typically relies on dynamic adjustment algorithms, which combine real-time data with target cooking needs for optimization, thereby ensuring maximum energy utilization efficiency. When performing the reset, the system comprehensively considers multiple factors, such as the current state of food maturity, external power supply conditions, and the heating needs of other cookware, to form a globally optimal heating solution. Finally, the readjusted operating parameters are continuously monitored through the system feedback mechanism to confirm their effectiveness and ensure the stability and efficiency of the cooking process.

[0042] In one embodiment, the heat demand index includes the minimum power required to reach the target temperature range and the power fluctuation range. In step S4, which calculates the heat demand index for each target zone based on the target zone set and each current cooking task profile, the step of calculating the minimum power required to reach the target temperature range and the power fluctuation range includes: S401: Obtain the thermal inertia coefficient, unit power heating coefficient, and heat dissipation coefficient of each current cooking task image to form a set of partition thermal response parameters corresponding to the target partition; S402: Based on the set of thermal response parameters for each zone, calculate the minimum power required for the zone to reach the target temperature range and the allowable power fluctuation range.

[0043] As described in step S401 above, the system acquires the zone thermal response parameters associated with each current cooking task profile. These parameters mainly include the thermal inertia coefficient, the unit power temperature rise coefficient, and the heat dissipation coefficient. The thermal inertia coefficient represents the lag in temperature change of the cookware and food during heating, and it depends on the cookware material, shape, and food composition. When determining the thermal inertia coefficient, the system can obtain typical values ​​for different cookware and foods through experiments or by consulting relevant literature. The unit power temperature rise coefficient refers to the rate of temperature increase caused by a unit power input, and is usually related to the thermal conductivity of the cookware and the specific heat capacity of the food. The heat dissipation coefficient represents the rate at which the cookware and food lose heat to the surrounding environment during cooking, and is usually closely related to factors such as ambient temperature, cookware surface characteristics, and wind speed. By combining these zone thermal response parameters, the system forms a complete parameter set for subsequent calculation of the heat required for each target zone to reach the target temperature under different cooking conditions. The accuracy and real-time performance of the system in acquiring these parameters helps improve the response speed and precision of subsequent temperature adjustments.

[0044] As described in step S402 above, the minimum power required for the target temperature range and the power fluctuation range are estimated. The acquired set of zone thermal response parameters is used to estimate the minimum power required for each target zone to reach the target temperature range and the allowable power fluctuation range. First, the system uses the thermal inertia coefficient and the unit power heating coefficient to calculate the minimum power required within a certain cooking time. This calculation typically relies on the heat conduction equation and can be expressed by the following formula: Minimum power = (m*c*ΔT) / t, where m is the mass of the target food, c is the specific heat capacity of the food, ΔT is the change in the target temperature range, and t is the heating time. Next, the system analyzes the heat dissipation coefficient to estimate the heat loss that may occur during heating, and then calculates the power fluctuation range. The fluctuation range reflects the power variation space required to maintain temperature stability based on the minimum power. This process ensures that the system can effectively cope with temperature fluctuations during dynamic cooking, ensuring that the food is heated within the optimal temperature range. This accurate estimation helps improve cooking efficiency, ensures the taste and quality of food, and maximizes energy efficiency by cooking with the lowest power consumption. Through this series of calculations, the system can generate adaptive thermal demand indicators to guide the setting of subsequent zonal power parameters.

[0045] In one embodiment, step S5, which calculates the partition power parameters of each target partition in a manner that minimizes the cost of thermal demand deviation under the constraints of the current external power supply capability parameters, includes: S501: Input the heat demand index, the current external power supply capacity parameters, and the target partition set into a preset initial model to obtain the target model; wherein, the initial model is a mathematical model; S502: Solve the target model by minimizing the thermal demand deviation cost to obtain the partition power parameters of each target partition.

[0046] As described in step S501 above, the calculated heat demand index, current external power supply capacity parameters, and target zone set are first input into a preset initial model to form a complete target model. This preset initial model is typically a mathematical model designed to describe the relationships between different variables and to optimize them under certain conditions. In this process, the heat demand index provides the standards that each zone needs to achieve under different cooking conditions, including minimum power and allowable power fluctuation range, which can serve as the basic data for optimization objectives. The external power supply capacity parameters introduce global constraints to the model, including the currently available maximum power and energy quota, ensuring that the optimization results are within a practically feasible range. Finally, the target zone set contains the specific areas that need power allocation, allowing the model to adjust the power allocation according to the characteristics of different cookware. By integrating this information into the initial model, the system can establish a dynamic response mathematical framework, providing a good foundation for subsequent solutions and optimizations.

[0047] As described in step S502 above, the target model is solved using the method of minimizing the heat demand deviation cost. The system uses the method of minimizing the heat demand deviation cost to solve the previously formed target model to obtain the partition power parameters of each target partition. An optimization algorithm is used to find a set of power allocation schemes that minimize the heat demand deviation cost. The heat demand deviation cost is a function used to quantify the deviation between the target temperature and the actual temperature. It is usually defined as a cost function that can include the error between the actual power of each partition and its heat demand index. Using numerical optimization methods, such as linear programming, nonlinear programming, or heuristic optimization algorithms (such as genetic algorithms, ant colony algorithms, etc.), the system can perform the minimization calculation of the cost function, comprehensively considering the power constraints and power supply capacity of each link. During this process, the system will also monitor and adjust in real time to ensure that the optimization result meets the heat demand of each target partition without exceeding the available power supply capacity, thereby achieving intelligent and efficient energy allocation. Finally, the partition power parameters obtained will be fed back to the electric stove control system for specific heating control. The successful completion of this process significantly improves the thermal efficiency and cooking quality during the cooking process, while ensuring the safety of the equipment.

[0048] In one embodiment, after step S6 of setting the current operating parameters of each target partition according to the partition power parameters, the method further includes: S711: Detect whether there are any target partitions among the target partitions that have not undergone heating; S712: If there are target partitions in each of the target partitions that have not been heated, remove the target partitions that have not been heated from the target partition set and regenerate the partition power parameters of each target partition.

[0049] As described in step S711 above, the system detects whether any unheated target zones exist. The system monitors the operating status of each heating zone in real time. Through temperature sensors and power detectors embedded in the heating zones, the system can obtain the current operating status information of each target zone. If the temperature of a target zone does not reach the preset start-heating condition, or if the power output is zero, it indicates that the zone has not yet started heating. Furthermore, the system monitors the actual temperature of each target zone against the target temperature in real time. If the actual temperature does not change and the power assessment is lower than the set value, it indicates that these zones are not operating within the normal heating range. During this monitoring process, the system needs to quickly and efficiently collect and analyze information from each heating zone to maintain the real-time dynamic response capability of the overall control system. Once an unheated zone is detected, the system marks these zones as problem areas and performs timely follow-up processing, preparing for subsequent removal and power parameter regeneration operations, thereby ensuring the energy distribution and cooking efficiency of the entire electric stove.

[0050] As described in step S712 above, once a target zone that is not being heated is detected, the system will remove these zones and update the target zone set to ensure that subsequent power allocation calculations are based only on the active zones that are being heated. This removal operation not only improves the efficiency of system calculations but also helps ensure the rational use of heat energy, avoiding unnecessary energy waste due to invalid zones. Furthermore, after removing unheated zones, the system needs to regenerate the power parameters for each target zone. This process involves steps similar to the previous power calculations; the system will call the previously set heat demand indicators and current external power supply capacity parameters to assess the required power output under the new target zone set. Through integrated analysis of the active zones and by applying the optimization algorithm that minimizes the cost of heat demand deviation, the system can ensure that the newly generated power parameters are adapted to actual cooking needs. Ultimately, the process of removing unheated zones and recalculating power parameters forms a dynamic adaptive control loop, continuously improving the energy efficiency and cooking effect of the electric stove while ensuring a smooth and efficient user experience.

[0051] In one embodiment, step S2, which involves obtaining the target partitions of each cookware on the stovetop to obtain a set of target partitions, includes: S201: The coverage boundary of each cookware is detected by infrared detection method; S202: Calculate the coverage area based on the coverage boundary of each cookware; S203: The area with a coverage area greater than the set area is recorded as the target partition.

[0052] As described in step S201 above, infrared detection technology is used to detect the coverage boundaries of each pot on the electric stove surface. Infrared detection is a non-contact measurement method. An infrared sensor emits infrared light of a specific wavelength, which is reflected or transmitted through the pot surface and then received by the sensor, thus accurately obtaining the shape and boundary information of the pot. In practical applications, infrared detection can efficiently identify the shape characteristics of different pots, such as round, square, or specially shaped pots. With accurate pot boundary information, the system can provide the necessary spatial data support for subsequent coverage area calculations.

[0053] As described in step S202 above, the coverage area of ​​each pot is calculated using the acquired pot coverage boundary data. This process typically relies on geometric calculations, involving precise measurement of the area of ​​the boundaries surrounding the pot's shape. Assuming the pot has a regular shape, the system can use standard geometric formulas to calculate the area; for example, for a round pot, the area can be calculated using the radius; for a rectangular pot, the area can be calculated by multiplying the length and width. If the pot shape is more complex, the system needs to rely on more advanced calculation methods, such as polygon area calculation formulas or region segmentation techniques, to divide the complex shape into simpler shapes (such as triangles) and then perform a comprehensive calculation. After calculating the coverage area, the system needs to store this information and prepare for subsequent threshold judgments to determine which target zones can be considered effective heating areas. By accurately calculating the coverage area, the system can ensure accurate heat distribution during cooking and optimize energy efficiency.

[0054] As described in step S203 above, the system compares the coverage area of ​​each cookware to determine whether it exceeds a preset area threshold. This threshold can be preset based on actual application and user needs. The set area threshold can be based on various factors such as the standard size of the cookware, catering needs, or thermal efficiency optimization. Through this step, the system can effectively eliminate all areas with insufficient coverage, as these areas cannot support effective cooking functions or heat transfer. When a cookware's coverage area is detected to be greater than the set area threshold, that area will be marked as a target zone and added to the target zone set, ready to receive subsequent heating control. This process ensures the effective utilization and precise distribution of heat energy, laying the foundation for improving the overall performance of the electric stove and the user experience.

[0055] Reference Figure 3 The present invention also provides a dynamic allocation control device for heating zones, the device comprising: The first acquisition module 902 is used to acquire the current external power supply capability parameters for supplying power to the specified electric stove; wherein, the current external power supply capability parameters include at least one of the available maximum power and available energy quota; The second acquisition module 904 is used to acquire the target partitions of each pot on the stove surface and obtain a set of target partitions. The third acquisition module 906 is used to acquire the current cooking object information of each of the pots and generate a current cooking task profile corresponding to each pot; wherein, the current cooking object information includes at least one of the following: food category, cooking stage, target temperature range, and uniformity requirement level; The first calculation module 908 is used to calculate the heat demand index of each target partition based on the target partition set and each current cooking task profile; wherein the heat demand index includes at least one of heating rate requirement, heat preservation requirement, and temperature fluctuation tolerance. The second calculation module 910 is used to calculate the partition power parameters of each target partition in a way that minimizes the cost of thermal demand deviation, under the constraint of satisfying the current external power supply capability parameters. The setting module 912 is used to set the current operating parameters of each target partition according to the partition power parameters.

[0056] In one embodiment, the third acquisition module 906 includes: The food information acquisition submodule is used to acquire food information for each pot and to acquire food images in each pot through a preset camera. The food feature recognition submodule is used to identify food features based on the food information and food images of each cookware. The food portrait generation submodule is used to generate food portraits corresponding to each cookware based on the food characteristics.

[0057] In one embodiment, the heating zone dynamic allocation control device further includes: A real-time cooking information monitoring module is used to periodically monitor the real-time cooking information of each of the target zones; wherein, the real-time cooking information includes real-time operating parameters, real-time external power supply capability parameters, and real-time cooking object information; The cooking information comparison module is used to compare the real-time cooking information with the current cooking information to obtain a comparison result; wherein, the current cooking information includes current operating parameters, current external power supply capability parameters, and current cooking object information; The comparison result judgment module is used to determine whether any of the comparison results exceed any one of the preset change thresholds in the set of change cases; The reset module is used to reset the working parameters of each target partition according to the real-time cooking information if any of the comparison results exceed any one of the preset change thresholds.

[0058] In one embodiment, the heat demand index includes the minimum power required to reach the target temperature range and the power fluctuation range, and the first calculation module 908 includes: The partition thermal response parameter set acquisition submodule is used to acquire the thermal inertia coefficient, unit power heating coefficient, and heat dissipation coefficient of each current cooking task profile, forming a partition thermal response parameter set corresponding to the target partition; The minimum power calculation submodule is used to calculate the minimum power required for a zone to reach the target temperature range and the allowable power fluctuation range based on the set of zone thermal response parameters.

[0059] In one embodiment, the second computing module 910 includes: The heat demand index input submodule is used to input the heat demand index, the current external power supply capacity parameters, and the target partition set into a preset initial model to obtain the target model; wherein, the initial model is a mathematical model; The partition power parameter solving submodule is used to solve the target model by minimizing the thermal demand deviation cost, and obtain the partition power parameters of each target partition.

[0060] In one embodiment, the heating zone dynamic allocation control device further includes: The target partition detection module is used to detect whether there are any target partitions in each of the target partitions that have not undergone heating. The partition power parameter regeneration module is used to remove the target partitions that have not been heated from the target partition set and regenerate the partition power parameters of each target partition if there are target partitions that have not been heated.

[0061] In one embodiment, the second acquisition module 904 includes: The coverage boundary detection submodule is used to detect the coverage boundary of each cookware using infrared detection methods; The coverage area calculation module is used to calculate the coverage area based on the coverage boundary of each cookware. The target partition marking module is used to mark areas with a coverage area greater than a set area as target partitions.

[0062] Figure 4 An internal structural diagram of an electric stove in one embodiment is shown. This electric stove can specifically be a terminal or a server, and more specifically, a computer device. Figure 4As shown, the electric stove includes a processor, a memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement a dynamic heating zone allocation control method. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to implement the dynamic heating zone allocation control method. Those skilled in the art will understand that… Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electric stove to which the present application is applied. A specific electric stove may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0063] In one embodiment, an electric stove is provided, including a memory and a processor. The memory stores a computer program that, when executed by the processor, causes the processor to perform the following steps: Obtain the current external power supply capability parameters for supplying power to the specified electric stove; wherein, the current external power supply capability parameters include at least one of the available maximum power and available energy quota; Obtain the target partitions of each pot on the stovetop to obtain the target partition set; Obtain the current cooking object information for each of the aforementioned cookwares and generate a current cooking task profile for each cookware; wherein, the current cooking object information includes at least one of the following: food category, cooking stage, target temperature range, and uniformity requirement level; Based on the target partition set and each of the current cooking task profiles, calculate the heat demand index for each target partition; wherein, the heat demand index includes at least one of the following: heating rate requirement, heat preservation requirement, and temperature fluctuation tolerance. Under the constraint of satisfying the current external power supply capacity parameters, the partition power parameters of each target partition are calculated in a way that minimizes the cost of thermal demand deviation. Set the current operating parameters for each target partition according to the partition power parameters.

[0064] By acquiring external power supply parameters in real time as constraints and combining them with the cooking task profile corresponding to the cookware, the system generates heat demand indicators for each heating zone. It then calculates the zone power with the optimization objective of minimizing heat demand deviation, achieving intelligent dynamic energy allocation under power-constrained conditions. This effectively avoids cooking interruptions or overloads caused by insufficient power, ensuring stable system operation. By prioritizing and accurately matching limited electrical energy to the key heat demands of different cooking stages and food types, the system improves the success rate and effectiveness of multi-task concurrent cooking. Simultaneously, the systematic optimization scheduling reduces ineffective energy consumption while ensuring heating uniformity and temperature stability, thus balancing cooking quality and overall energy efficiency in a limited power supply environment.

[0065] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the following steps: Obtain the current external power supply capability parameters for supplying power to the specified electric stove; wherein, the current external power supply capability parameters include at least one of the available maximum power and available energy quota; Obtain the target partitions of each pot on the stovetop to obtain the target partition set; Obtain the current cooking object information for each of the aforementioned cookwares and generate a current cooking task profile for each cookware; wherein, the current cooking object information includes at least one of the following: food category, cooking stage, target temperature range, and uniformity requirement level; Based on the target partition set and each of the current cooking task profiles, calculate the heat demand index for each target partition; wherein, the heat demand index includes at least one of the following: heating rate requirement, heat preservation requirement, and temperature fluctuation tolerance. Under the constraint of satisfying the current external power supply capacity parameters, the partition power parameters of each target partition are calculated in a way that minimizes the cost of thermal demand deviation. Set the current operating parameters for each target partition according to the partition power parameters.

[0066] By acquiring external power supply parameters in real time as constraints and combining them with the cooking task profile corresponding to the cookware, the system generates heat demand indicators for each heating zone. It then calculates the zone power with the optimization objective of minimizing heat demand deviation, achieving intelligent dynamic energy allocation under power-constrained conditions. This effectively avoids cooking interruptions or overloads caused by insufficient power, ensuring stable system operation. By prioritizing and accurately matching limited electrical energy to the key heat demands of different cooking stages and food types, the system improves the success rate and effectiveness of multi-task concurrent cooking. Simultaneously, the systematic optimization scheduling reduces ineffective energy consumption while ensuring heating uniformity and temperature stability, thus balancing cooking quality and overall energy efficiency in a limited power supply environment.

[0067] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0068] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0069] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for dynamic allocation and control of heating zones, characterized in that, The method includes: Obtain the current external power supply capability parameters for supplying power to the specified electric stove; wherein, the current external power supply capability parameters include at least one of the available maximum power and available energy quota; Obtain the target partitions of each pot on the stovetop to obtain the target partition set; Obtain the current cooking object information for each of the aforementioned cookwares and generate a current cooking task profile for each cookware; wherein, the current cooking object information includes at least one of the following: food category, cooking stage, target temperature range, and uniformity requirement level; Based on the target partition set and each of the current cooking task profiles, calculate the heat demand index for each target partition; wherein, the heat demand index includes at least one of the following: heating rate requirement, heat preservation requirement, and temperature fluctuation tolerance. Under the constraint of satisfying the current external power supply capacity parameters, the partition power parameters of each target partition are calculated in a way that minimizes the cost of thermal demand deviation. Set the current operating parameters for each target partition according to the partition power parameters.

2. The dynamic allocation control method for heating zones according to claim 1, characterized in that, The step of obtaining the current cooking object information of each of the pots and generating the current cooking task profile corresponding to each pot includes: It acquires food information for each cookware and obtains food images from each cookware through a pre-set camera; Food features identified based on food information and food images from various cookware; Based on the food characteristics, a cooking food profile corresponding to each cookware is generated.

3. The dynamic allocation control method for heating zones according to claim 1, characterized in that, After the step of setting the current operating parameters of each target partition according to the partition power parameters, the method further includes: The real-time cooking information of each target zone is periodically monitored; wherein, the real-time cooking information includes real-time operating parameters, real-time external power supply capability parameters, and real-time cooking object information; The real-time cooking information is compared with the current cooking information to obtain a comparison result; wherein, the current cooking information includes the current working parameters, the current external power supply capability parameters, and the current cooking object information; Determine whether any of the comparison results exceed any one of the preset change thresholds in the set of change scenarios; If any of the comparison results exceeds any one of the preset change thresholds, then the working parameters of each target partition are reset according to the real-time cooking information.

4. The dynamic allocation control method for heating zones according to claim 1, characterized in that, The heat demand index includes the minimum power required to reach the target temperature range and the power fluctuation range. The step of calculating the heat demand index for each target zone based on the target zone set and each current cooking task profile, specifically the step of calculating the minimum power required to reach the target temperature range and the power fluctuation range, includes: The thermal inertia coefficient, unit power heating coefficient, and heat dissipation coefficient of each current cooking task image are obtained to form a set of partition thermal response parameters corresponding to the target partition. Based on the set of thermal response parameters for each zone, calculate the minimum power required for the zone to reach the target temperature range and the allowable power fluctuation range.

5. The dynamic allocation control method for heating zones according to claim 1, characterized in that, The step of calculating the partition power parameters of each target partition in a manner that minimizes the cost of thermal demand deviation, under the constraints of the current external power supply capacity parameters, includes: The heat demand index, the current external power supply capacity parameters, and the target partition set are input into a preset initial model to obtain the target model; wherein, the initial model is a mathematical model; The target model is solved by minimizing the cost of heat demand deviation, and the partition power parameters of each target partition are obtained.

6. The dynamic allocation control method for heating zones according to claim 1, characterized in that, After the step of setting the current operating parameters of each target partition according to the partition power parameters, the method further includes: Detect whether there are any target partitions among the target partitions that have not undergone heating; If any of the target partitions does not perform heating, the target partition that does not perform heating is removed from the target partition set, and the partition power parameters of each target partition are regenerated.

7. The dynamic allocation control method for heating zones according to claim 1, characterized in that, The step of obtaining the target partitions of each cookware on the stove surface and obtaining the target partition set includes: The coverage boundaries of each cookware are detected using infrared detection methods; The coverage area is calculated based on the coverage boundary of each cookware. The area with a coverage area greater than the set area is recorded as the target partition.

8. A dynamic distribution control device for heating zones, characterized in that, The device includes: The first acquisition module is used to acquire the current external power supply capability parameters for supplying power to the specified electric stove; wherein, the current external power supply capability parameters include at least one of the available maximum power and available energy quota; The second acquisition module is used to acquire the target partitions of each pot on the stove surface and obtain a set of target partitions. The third acquisition module is used to acquire the current cooking object information of each of the pots and generate a current cooking task profile corresponding to each pot; wherein, the current cooking object information includes at least one of the following: food category, cooking stage, target temperature range, and uniformity requirement level; The first calculation module is used to calculate the heat demand index of each target partition based on the target partition set and each current cooking task profile; wherein the heat demand index includes at least one of heating rate requirement, heat preservation requirement, and temperature fluctuation tolerance. The second calculation module is used to calculate the partition power parameters of each target partition in a way that minimizes the cost of thermal demand deviation, under the constraint of satisfying the current external power supply capacity parameters. The setting module is used to set the current operating parameters of each target partition according to the partition power parameters.

9. A computer-readable storage medium, characterized in that, The device contains a computer program that, when executed by a processor, causes the processor to perform the steps of the dynamic allocation control method for heating zones as described in any one of claims 1 to 7.

10. An electric stove, characterized in that, The device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the dynamic allocation control method for heating zones as described in any one of claims 1 to 7.

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