Waste heat distribution method, device and equipment of smart electric appliance

Abstract

This application discloses a method, apparatus, and device for waste heat distribution in smart appliances. The method includes: recovering waste heat generated by the smart appliances during operation using multiple heat storage units; determining a first priority for each heat redistribution module based on its energy efficiency attributes, and constructing a first heat distribution strategy; determining a second priority for each heat redistribution module based on the urgency of its heat demand, and constructing a second heat distribution strategy; selecting a target heat redistribution module from the multiple heat redistribution modules based on at least one of the first and second heat distribution strategies and the heat data corresponding to the waste heat; and controlling the heat acquisition module to transfer waste heat to the target heat redistribution module by adjusting the opening direction of the control valve. This application achieves efficient utilization of waste heat resources.

Description

Technical Field

[0001] This application relates to the field of smart electrical appliance technology, and in particular to a method, apparatus and equipment for waste heat distribution in smart electrical appliances. Background Technology

[0002] Currently, cooking appliances in kitchen systems (such as steam ovens, electric ovens, and air fryers) generally suffer from heat energy waste. Although some technologies have attempted to recover waste heat from the cavity, most of them focus on hot water heating or simple heat preservation, and most use fixed-path heat pipe heat exchange structures, failing to achieve intelligent distribution and dynamic reuse of waste heat.

[0003] Furthermore, existing thermal energy management systems are typically based on fixed logic, such as static sensor data like ambient temperature and cookware location, and cannot respond to user behavior patterns or multi-module collaboration needs. In practical applications, there are numerous "downtime periods" or "multi-device collaboration" scenarios during kitchen cooking, making it difficult for existing technologies to efficiently allocate waste heat resources. Summary of the Invention

[0004] This application provides a method, device, and equipment for waste heat distribution in smart appliances, which realizes efficient utilization of waste heat resources and intelligent and dynamic distribution of waste heat in smart appliances.

[0005] On one hand, this application provides a waste heat distribution method for a smart appliance, wherein the smart appliance is equipped with a heat acquisition module, multiple heat storage units, and multiple heat redistribution modules. The heat acquisition module is connected to the multiple heat storage units, and the heat acquisition module is equipped with a control valve, which is connected to the multiple heat redistribution modules. The method includes: The waste heat generated by the smart appliance during operation is recovered based on the multiple heat storage units; Based on the energy efficiency attributes of each of the multiple heat redistribution modules, the first priority of each heat redistribution module is determined, and a first heat distribution strategy is constructed. Based on the urgency of the heat demand corresponding to each of the multiple heat redistribution modules, a second priority is determined for each heat redistribution module, and a second heat distribution strategy is constructed. Based on at least one of the first heat distribution strategy and the second heat distribution strategy, and the heat data corresponding to the waste heat, a target heat redistribution module is selected from the plurality of heat redistribution modules; By adjusting the opening direction of the control valve, the heat acquisition module is controlled to transfer the waste heat to the target heat redistribution module.

[0006] In one exemplary embodiment, the step of selecting a target heat energy redistribution module from the plurality of heat energy redistribution modules based on at least one of the first heat allocation strategy, the second heat allocation strategy, and the heat data corresponding to the waste heat includes: Based on the usage frequency of the multiple heat redistribution modules in each historical period, determine the high-frequency usage period corresponding to each heat redistribution module; Based on the high-frequency usage period corresponding to each heat redistribution module, the third priority of each heat redistribution module in each preset period is determined, and a third heat distribution strategy is constructed. Based on at least one of the first heat distribution strategy, the second heat distribution strategy, and the third heat distribution strategy, and the heat data corresponding to the waste heat, a target heat redistribution module is selected from the plurality of heat redistribution modules.

[0007] In one exemplary embodiment, the step of selecting a target heat energy redistribution module from the plurality of heat energy redistribution modules based on at least one of the first heat distribution strategy, the second heat distribution strategy, and the third heat distribution strategy, and the heat data corresponding to the waste heat, includes: The first priority of each heat redistribution module is determined according to the first heat distribution strategy; The second priority of each heat redistribution module is determined according to the second heat distribution strategy; The third priority of each heat redistribution module in the current time period is determined according to the third heat distribution strategy. Obtain the first weight corresponding to the first heat allocation strategy, the second weight corresponding to the second heat allocation strategy, and the third weight corresponding to the third heat allocation strategy; The target priority of each heat redistribution module is determined based on its first priority, second priority, third priority, and first weight, second weight, and third weight. Based on the target priority of each heat redistribution module, a target heat redistribution module is selected from the plurality of heat redistribution modules.

[0008] In one exemplary embodiment, the method further includes: The start-stop time, usage frequency, cooking recipe type, and residual heat data of the smart appliance within a preset sample time period are obtained as sample training data; the sample training data is labeled with the real-time start time tag of the smart appliance in the sample future time period. The sample training data is input into the time series prediction model to obtain the sample prediction start time of the smart appliance in the future time period of the sample. Based on the difference between the predicted startup time of the sample and the real-time startup time label of the sample, the time series prediction model is trained to obtain the appliance startup data prediction model. The start-up and shutdown times, usage frequency, cooking recipe types, and residual heat data of the smart appliance during historical preset time periods prior to the current time period are input into the appliance start-up data prediction model, and the predicted start-up time of the smart appliance during the current time period is output.

[0009] In one exemplary embodiment, the sample training data is labeled with the sample cooking type label of the smart appliance in the sample's future time period. The time series prediction model also outputs the sample predicted cooking type. The step of training the time series prediction model based on the difference between the sample predicted start time and the sample real-time start time label to obtain the appliance start data prediction model includes: The target loss data is determined based on the difference between the sample's predicted start time and the sample's real-time start time label, and the difference between the sample's predicted cooking type and the sample's cooking type label. The model parameters of the time series prediction model are adjusted according to the target loss data until the training termination condition is met, and the model at the end of training is determined as the appliance startup data prediction model. Accordingly, the step of inputting the start-up and shutdown times, usage frequency, cooking recipe types, and residual heat data of the smart appliance during historical preset time periods prior to the current time period into the appliance start-up data prediction model, and outputting the predicted start-up time of the smart appliance in the current time period, includes: The start-up and shutdown times, usage frequency, cooking recipe types, and residual heat data of the smart appliance during historical preset time periods prior to the current time period are input into the appliance start-up data prediction model, and the predicted start-up time and predicted cooking type of the smart appliance during the current time period are output.

[0010] In one exemplary embodiment, after outputting the predicted start-up time and predicted cooking type of the smart appliance for the current time period, the method further includes: The preheating path of the smart appliance is determined based on the predicted cooking type, and the preheating time of the smart appliance is determined based on the predicted start time. The smart appliance is controlled to activate the preheating path during the preheating time.

[0011] In one exemplary embodiment, the plurality of heat storage units includes a first heat storage unit disposed in the bottom tray area of ​​the smart appliance, a second heat storage unit disposed in the interlayer of the cooking cavity wall, and a third heat storage unit disposed in the air inlet duct or steam condensation area; the melting point of the phase change material in the first heat storage unit, the second heat storage unit, and the third heat storage unit decreases sequentially, and the recovery of waste heat generated by the smart appliance during operation based on the plurality of heat storage units includes: The waste heat from baking in the smart appliance is recovered based on the first heat storage unit; The second heat storage unit recovers the waste heat from the steaming and baking processes of the smart appliance; The third heat storage unit recovers the damp heat from the smart appliance.

[0012] On the other hand, a waste heat distribution device for a smart appliance is provided. The smart appliance includes a heat acquisition module, multiple heat storage units, and multiple heat redistribution modules. The heat acquisition module is connected to the multiple heat storage units. A control valve is included in the heat acquisition module and is connected to the multiple heat redistribution modules. The device includes: The waste heat recovery module is used to recover the waste heat generated by the smart appliance during operation based on the multiple heat storage units; The first strategy construction module is used to determine the first priority of each heat energy redistribution module according to the energy efficiency attributes of each of the plurality of heat energy redistribution modules, and to construct a first heat distribution strategy. The second strategy construction module is used to determine the second priority of each heat energy redistribution module based on the urgency of the heat demand corresponding to each of the plurality of heat energy redistribution modules, and to construct a second heat allocation strategy. The filtering module is used to filter out a target heat energy redistribution module from the plurality of heat energy redistribution modules based on at least one of the first heat distribution strategy, the second heat distribution strategy, and the heat data corresponding to the waste heat. The waste heat transfer module is used to control the heat acquisition module to transfer the waste heat to the target heat redistribution module by adjusting the opening direction of the control valve.

[0013] In one exemplary embodiment, the filtering module includes: The time period determination unit is used to determine the high-frequency usage period corresponding to each heat energy redistribution module based on the usage frequency of the plurality of heat energy redistribution modules in each historical time period; The third strategy construction unit is used to determine the third priority of each heat redistribution module in each preset time period based on the high-frequency usage period corresponding to each heat redistribution module, and to construct the third heat distribution strategy. The filtering unit is used to filter out a target heat energy redistribution module from the plurality of heat energy redistribution modules based on at least one of the first heat distribution strategy, the second heat distribution strategy, the third heat distribution strategy, and the heat data corresponding to the waste heat.

[0014] In one exemplary embodiment, the filtering unit includes: The first determining subunit is used to determine the first priority of each heat redistribution module according to the first heat distribution strategy. The second determining subunit is used to determine the second priority of each heat redistribution module according to the second heat distribution strategy; The third determining subunit is used to determine the third priority of each heat redistribution module in the current time period according to the third heat distribution strategy. The target priority determination subunit is used to determine the target priority of each heat energy redistribution module based on the first priority, second priority, and third priority corresponding to each heat energy redistribution module. The filtering subunit is used to filter out the target heat energy redistribution module from the plurality of heat energy redistribution modules according to the target priority of each heat energy redistribution module.

[0015] In one exemplary embodiment, the apparatus further includes: The sample data acquisition module is used to acquire the start-stop time, usage frequency, cooking recipe type, and residual heat data of the smart appliance within a preset sample time period, as sample training data; the sample training data is labeled with the sample real-time start time tag of the smart appliance in the sample future time period. The sample time prediction module is used to input the sample training data into the time series prediction model to obtain the sample prediction start time of the smart appliance in the future time period of the sample. The training module is used to train the time series prediction model based on the difference between the predicted start time of the sample and the real-time start time label of the sample, so as to obtain the appliance start data prediction model. The time prediction module is used to input the start-up and stop times, usage frequency, cooking recipe types, and residual heat data of the smart appliance in the historical preset time periods before the current time period into the appliance start-up data prediction model, and output the predicted start-up time of the smart appliance in the current time period.

[0016] In one exemplary embodiment, the sample training data is labeled with the sample cooking type label of the smart appliance in the sample's future time period, and the time series prediction model also outputs the sample predicted cooking type. The training module includes: The target data determination unit is used to determine target loss data based on the difference between the sample predicted start time and the sample real-time start time label, and the difference between the sample predicted cooking type and the sample cooking type label; The model determination unit is used to adjust the model parameters of the time series prediction model according to the target loss data until the training end condition is met, and to determine the model at the end of training as the appliance startup data prediction model. Correspondingly, the time prediction module is also used to input the start-up and stop times, usage frequency, cooking recipe types and residual heat data of the smart appliance in the historical preset time periods before the current time period into the appliance start-up data prediction model, and output the predicted start-up time and predicted cooking type of the smart appliance in the current time period.

[0017] In one exemplary embodiment, the apparatus further includes: The preheating time determination module is used to determine the preheating path of the smart appliance based on the predicted cooking type, and to determine the preheating time of the smart appliance based on the predicted start time. A preheating path activation module is used to control the smart appliance to activate the preheating path during the preheating time.

[0018] In one exemplary embodiment, the plurality of heat storage units include a first heat storage unit disposed in the bottom tray area of ​​the smart appliance, a second heat storage unit disposed in the interlayer of the cooking cavity wall, and a third heat storage unit disposed in the air inlet duct or steam condensation area; the melting point of the phase change material in the first heat storage unit, the second heat storage unit, and the third heat storage unit decreases sequentially, and the waste heat recovery module includes: The first recovery unit is used to recover the waste heat from baking of the smart appliance based on the first heat storage unit; The second recovery unit is used to recover the waste heat from steaming and baking of the smart appliance based on the second heat storage unit; The third recovery unit is used to recover the damp heat of the smart appliance based on the third heat storage unit.

[0019] On the other hand, a smart appliance is provided, which adopts the above-mentioned waste heat distribution method. The smart appliance includes at least one of the following: steam oven, electric oven, air fryer, steam oven, dishwasher, refrigerator, and water purifier.

[0020] On the other hand, an electronic device is provided, the device including a processor and a memory, the memory storing a computer program, the computer program being loaded and executed by the processor to implement the waste heat distribution method of the smart appliance as described above.

[0021] On the other hand, a computer storage medium is provided, which stores a computer program that is loaded and executed by a processor to implement the waste heat distribution method of the smart appliance as described above.

[0022] On the other hand, a computer program product is provided, including a computer program that is loaded and executed by a processor to implement the waste heat distribution method of the smart appliance as described above.

[0023] The waste heat distribution method, device, and equipment for intelligent electrical appliances provided in this application have the following technical advantages: The smart appliance of this application includes a heat acquisition module, multiple heat storage units, and multiple heat redistribution modules. The heat acquisition module is connected to the multiple heat storage units, and a control valve is installed in the heat acquisition module. The control valve is connected to the multiple heat redistribution modules. By constructing multiple heat storage units, waste heat in the smart appliance can be fully recovered, and the multiple heat redistribution modules can achieve selective distribution of waste heat. The method includes: recovering waste heat generated by the smart appliance during operation based on multiple heat storage units; determining the first priority of each heat redistribution module according to its corresponding energy efficiency attributes. First, a first heat distribution strategy is constructed. Then, based on the urgency of heat demand corresponding to each of the multiple heat redistribution modules, a second priority is determined for each heat redistribution module, and a second heat distribution strategy is constructed. Based on at least one of the first and second heat distribution strategies and the heat data corresponding to the waste heat, a target heat redistribution module is selected from the multiple heat redistribution modules. By adjusting the opening direction of the control valve, the heat acquisition module is controlled to transfer waste heat to the target heat redistribution module. This achieves efficient utilization of waste heat resources and realizes intelligent and dynamic distribution of waste heat in smart appliances. Attached Figure Description

[0024] To more clearly illustrate the technical solutions and advantages in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the structure of a smart appliance provided in the embodiments of this specification; Figure 2 This is a schematic flowchart of a waste heat distribution method for a smart appliance provided in the embodiments of this specification; Figure 3 This is a flowchart illustrating a method for predicting the start-up time of a smart appliance during the current time period, as provided in an embodiment of this specification. Figure 4 This is a schematic flowchart of a preheating method for a smart appliance provided in the embodiments of this specification; Figure 5 This is a flowchart illustrating a method for recovering waste heat generated by a smart appliance during operation based on the plurality of heat storage units, as provided in an embodiment of this specification. Figure 6 This is a schematic diagram of the structure of a waste heat distribution device for a smart appliance provided in the embodiments of this specification; Figure 7 This is a schematic diagram of the structure of a server provided in an embodiment of this specification; The reference numerals in the figure are as follows: 1. Shell; 2. Heat redistribution manifold; 3. Insulation layer; 4. Interlayer; 5. Heat storage unit; 6. Control module. Detailed Implementation

[0026] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0028] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0029] The smart appliances in this embodiment may include one or more of the following: steam oven, electric oven, air fryer, steam oven combo, dishwasher, refrigerator, and water purifier; such as... Figure 1 As shown, Figure 1 This is a structural diagram of a smart appliance. The smart appliance includes an outermost shell 1, a heat redistribution manifold 2 at the top connected to various heat redistribution modules, a heat insulation layer 3 in the middle cooking cavity, and a heat-conducting layer and phase change heat pipes in the interlayer 4 as heat collection modules. Distributed heat storage units 5 are provided on the side walls and bottom. It also includes a control module 6 (central control system) located at the rear or bottom of the smart appliance. This control module may include a microcontroller unit (MCU), a sensor acquisition unit, and control circuitry.

[0030] The following describes a waste heat distribution method for intelligent electrical appliances according to this application. Figure 2 This is a flowchart illustrating a waste heat distribution method for a smart appliance provided in an embodiment of this specification. This specification provides the operational steps of the method described in the embodiment or flowchart, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiment is merely one possible execution order among many and does not represent the only possible execution order. In actual system or server products, the method can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment) as shown in the embodiment or the accompanying drawings. The smart appliance includes a heat acquisition module, multiple heat storage units, and multiple heat redistribution modules. The heat acquisition module is connected to the multiple heat storage units, and a control valve is provided in the heat acquisition module, which is connected to the multiple heat redistribution modules. The method of this embodiment can be applied to the control module of the smart appliance, specifically, as shown in the following example. Figure 2 As shown, the method may include: S201: The waste heat generated by the smart appliance during operation is recovered based on the plurality of heat storage units. The smart appliance in this embodiment may include a combination of one or more of the following: steam oven, electric oven, air fryer, steam oven, dishwasher, refrigerator, and water purifier. Multiple heat storage units can be set in different areas inside the smart appliance, and different heat storage units can be filled with high specific heat materials of different phase change temperature zones. For example, a medium-temperature heat storage unit (60-90℃) can be set in the interlayer of the cooking cavity wall of the smart appliance to recover residual heat from steaming and baking; a high-temperature heat storage unit (100-120℃) can be set in the bottom tray area to recover residual heat from high-temperature baking; and a low-temperature heat storage unit (40-60℃) can be set in the air inlet duct or steam condensation area to recover humid heat.

[0031] S203: Based on the energy efficiency attributes of each of the plurality of heat redistribution modules, determine the first priority of each heat redistribution module and construct a first heat distribution strategy.

[0032] In the embodiments of this specification, the plurality of heat energy redistribution modules include at least two of the following: a micro-thawing module, a drying and dehumidifying module, a deodorizing molecule release module, a water tank preheating loop, and a heating module for preventing fogging of glass doors. Specifically, the priority of the heat energy redistribution modules can be set according to their energy efficiency attributes; for example, modules with high energy efficiency requirements can be given higher priorities; for instance, modules such as the micro-thawing module and the dehumidifying module can be given higher priorities, while other modules can be given lower priorities. This determines the first priority of each heat energy redistribution module, and a first heat distribution strategy is constructed based on the first priorities corresponding to each of the plurality of heat energy redistribution modules. One heat energy redistribution module can be assigned one first priority, or two or more heat energy redistribution modules can be assigned the same first priority. That is, the first heat distribution strategy includes the first priorities corresponding to each of the plurality of heat energy redistribution modules.

[0033] S205: Based on the urgency of the heat demand corresponding to each of the plurality of heat redistribution modules, determine the second priority of each heat redistribution module and construct a second heat distribution strategy.

[0034] In the embodiments of this specification, the urgency of heat demand for each heat redistribution module can be determined based on the attribute information of each heat redistribution module during actual application. For example, for smart appliances such as refrigerators and freezers, the deodorizing molecule release module is used to release deodorizing molecules by heating when an odor is detected. In the application process, its corresponding priority can be adjusted according to the detection results of the deodorizing molecule release module. If an odor is detected in the smart appliance, the priority of the deodorizing molecule release module is set to the highest priority, that is, the residual heat is allocated to it first.

[0035] This embodiment can also be adjusted based on the first heat distribution strategy to obtain a second heat distribution strategy. For example, when no odor is detected, a first heat distribution strategy can be constructed according to the first priority of each of the multiple heat redistribution modules. At this time, the priority of the odor removal molecule release module is lower than that of the micro-thawing module and the dehumidification module. When an odor is detected, the priority is adjusted so that the priority of the odor removal molecule release module is higher than that of the micro-thawing module and the dehumidification module, thereby obtaining the second heat distribution strategy.

[0036] S207: Based on at least one of the first heat distribution strategy and the second heat distribution strategy, and the heat data corresponding to the waste heat, select a target heat redistribution module from the plurality of heat redistribution modules.

[0037] In the embodiments of this specification, a target heat energy redistribution module can be selected from the plurality of heat energy redistribution modules based on one of the first heat energy distribution strategy and the second heat energy distribution strategy, or by combining the first heat energy distribution strategy and the second heat energy distribution strategy and the heat data corresponding to the waste heat. The target heat energy redistribution module is the module that prioritizes the distribution of waste heat. This embodiment can combine the first heat energy distribution strategy and the second heat energy distribution strategy to improve the accuracy of the selection of the target heat energy redistribution module.

[0038] S209: By adjusting the opening direction of the control valve, the heat acquisition module is controlled to transfer the waste heat to the target heat redistribution module.

[0039] In the embodiments of this specification, the control valve is used to control the heat distribution of the multiple heat redistribution modules. The heat acquisition module includes a heat-conducting layer and a phase change heat pipe network array disposed inside the smart appliance. Each heat storage unit is filled with a phase change material, and the melting point of the phase change material in each heat storage unit is different. The heat acquisition module includes a multi-layer heat-conducting layer disposed on the top / side wall / bottom of the cooking cavity and a phase change heat pipe network array; the heat pipe adopts a phase change material and a control valve combination structure, which has a heat valve directional flow guiding function.

[0040] The smart appliance in the embodiments of this specification includes a heat acquisition module, multiple heat storage units, and multiple heat redistribution modules. The heat acquisition module is connected to the multiple heat storage units, and a control valve is installed in the heat acquisition module. The control valve is connected to the multiple heat redistribution modules. By constructing multiple heat storage units, waste heat in the smart appliance can be fully recovered, and the multiple heat redistribution modules can achieve selective distribution of waste heat. The method includes: recovering waste heat generated by the smart appliance during operation based on multiple heat storage units; determining the energy efficiency attributes of each heat redistribution module according to their respective energy efficiency attributes. First, a first priority is set, and a first heat distribution strategy is constructed. Then, based on the urgency of heat demand corresponding to each of the multiple heat redistribution modules, a second priority is determined for each heat redistribution module, and a second heat distribution strategy is constructed. Based on at least one of the first and second heat distribution strategies and the heat data corresponding to the waste heat, a target heat redistribution module is selected from the multiple heat redistribution modules. By adjusting the opening direction of the control valve, the heat acquisition module is controlled to transfer waste heat to the target heat redistribution module. This achieves efficient utilization of waste heat resources and realizes intelligent and dynamic distribution of waste heat in smart appliances.

[0041] In this embodiment of the specification, the step of selecting a target heat energy redistribution module from the plurality of heat energy redistribution modules based on at least one of the first heat distribution strategy and the second heat distribution strategy and the heat data corresponding to the waste heat includes: Based on the usage frequency of the multiple heat redistribution modules in each historical period, determine the high-frequency usage period corresponding to each heat redistribution module; Based on the high-frequency usage period corresponding to each heat redistribution module, the third priority of each heat redistribution module in each preset period is determined, and a third heat distribution strategy is constructed. Based on at least one of the first heat distribution strategy, the second heat distribution strategy, and the third heat distribution strategy, and the heat data corresponding to the waste heat, a target heat redistribution module is selected from the plurality of heat redistribution modules.

[0042] In the embodiments of this specification, the high-frequency usage period corresponding to each heat redistribution module can be determined based on the usage frequency of the multiple heat redistribution modules in each historical time period; that is, the user's usage habits of smart appliances can be predicted based on the usage frequency of the multiple heat redistribution modules in each historical time period; for example, the frequency of use of smart appliances is highest during the dinner time period each day. Therefore, the dinner time period can be determined as the high-frequency usage period; and the heat redistribution module corresponding to the cooking mode during this period can be set as the highest priority. For example, if the frequency of use of electric heating appliances is high during this period, the priority of electric heating appliances can be set higher than other priorities. In this case, the heat redistribution module corresponding to the high-frequency usage period can be set as the highest priority module, and the priority of other modules can be reduced, thereby obtaining a third heat distribution strategy.

[0043] In this embodiment, a heat storage unit can also be set around the electric heating appliance corresponding to the high-frequency usage period, and the heat storage unit can be set as the target heat storage unit (for example, the high-temperature heat storage unit can be reserved first). That is, the target heat storage unit can be used first to transfer waste heat to the heat redistribution module.

[0044] This embodiment can construct a heat allocation strategy for each type of smart appliance's heat redistribution module based on the first heat allocation strategy, the second heat allocation strategy, and the third heat allocation strategy corresponding to each of the preset smart appliances, forming a preset database. Then, for the current smart appliance, its corresponding appliance type and application data can be obtained to determine the heat allocation strategy, thereby further filtering out the target heat redistribution module. The application data of the smart appliance may include, but is not limited to, odor detection results, current time period, etc.

[0045] In this embodiment of the specification, the step of selecting a target heat energy redistribution module from the plurality of heat energy redistribution modules based on at least one of the first heat distribution strategy, the second heat distribution strategy, and the third heat distribution strategy, and the heat data corresponding to the waste heat, includes: The first priority of each heat redistribution module is determined according to the first heat distribution strategy; The second priority of each heat redistribution module is determined according to the second heat distribution strategy; The third priority of each heat redistribution module in the current time period is determined according to the third heat distribution strategy. The target priority of each heat redistribution module is determined based on its first priority, second priority, and third priority. Based on the target priority of each heat redistribution module, a target heat redistribution module is selected from the plurality of heat redistribution modules.

[0046] In the embodiments of this specification, a target heat energy redistribution module can be selected from the plurality of heat energy redistribution modules based on the first priority, second priority, and third priority corresponding to each heat energy redistribution module. If the same heat energy redistribution module corresponds to multiple priorities, the selection strategy corresponding to that heat energy redistribution module can be determined based on the real-time odor detection results of the smart appliance, the current working period, etc., thereby quickly and accurately determining the target priority of each heat energy redistribution module.

[0047] In the embodiments described in this specification, such as Figure 3 As shown, the method further includes: S301: Obtain the start-stop time, usage frequency, cooking recipe type, and residual heat data of the smart appliance within a preset sample time period, and use them as sample training data; the sample training data is labeled with the sample real-time start time label of the smart appliance in the sample future time period; S303: Input the sample training data into the time series prediction model to obtain the sample prediction start time of the smart appliance in the future time period of the sample; S305: Based on the difference between the predicted start-up time of the sample and the real-time start-up time label of the sample, train the time series prediction model to obtain the appliance start-up data prediction model; S307: Input the start-stop time, usage frequency, cooking recipe type, and residual heat data of the smart appliance in the historical preset time period before the current time period into the appliance start-up data prediction model, and output the predicted start-up time of the smart appliance in the current time period.

[0048] In the embodiments of this specification, sample training data can be constructed based on the historical operating data of smart appliances. Specifically, the start-stop time, usage frequency, cooking recipe type, and residual heat data of the smart appliance within a preset sample time period can be obtained as sample training data; and the sample training data can be labeled with the real-time start-up time label of the smart appliance in the sample future time period; the obtained sample training data includes the usage frequency of the smart appliance in multiple preset time periods within a certain period, thereby distinguishing the high-frequency time periods of frequent use of the smart appliance by users through cluster analysis (K-means). For example, the high-frequency time periods of the smart appliance during the day can be analyzed, or the high-frequency usage time periods of the smart appliance within a week or a month can be analyzed. Then, a pre-trained time series prediction model is obtained, and the sample training data is input into the time series prediction model to obtain the sample predicted start time of the smart appliance in the sample's future time period. Then, based on the difference between the sample predicted start time and the sample's real-time start time label, the target loss data is determined. Then, the model parameters of the time series prediction model are adjusted according to the target loss data until the training termination condition is met, and the time series prediction model at the end of training is determined as the appliance start data prediction model. Thus, the predicted start time of the smart appliance in the current time period can be predicted quickly and accurately through the appliance start data prediction model.

[0049] In this embodiment of the specification, the sample training data is labeled with the sample cooking type label of the smart appliance in the sample future time period, and the time series prediction model also outputs the sample predicted cooking type. The step of training the time series prediction model based on the difference between the sample predicted start time and the sample real-time start time label to obtain the appliance start data prediction model includes: The target loss data is determined based on the difference between the sample's predicted start time and the sample's real-time start time label, and the difference between the sample's predicted cooking type and the sample's cooking type label. The model parameters of the time series prediction model are adjusted according to the target loss data until the training termination condition is met, and the model at the end of training is determined as the appliance startup data prediction model. Accordingly, the step of inputting the start-up and shutdown times, usage frequency, cooking recipe types, and residual heat data of the smart appliance during historical preset time periods prior to the current time period into the appliance start-up data prediction model, and outputting the predicted start-up time of the smart appliance in the current time period, includes: The start-up and shutdown times, usage frequency, cooking recipe types, and residual heat data of the smart appliance during historical preset time periods prior to the current time period are input into the appliance start-up data prediction model, and the predicted start-up time and predicted cooking type of the smart appliance during the current time period are output.

[0050] In the embodiments of this specification, a first loss data is determined based on the difference between the predicted start time of the sample and the real-time start time label of the sample; and a second loss data is determined based on the difference between the predicted cooking type of the sample and the cooking type label of the sample; then, a target loss data is determined based on the sum or weighted sum of the first and second loss data. Finally, the model parameters of the time series prediction model are adjusted according to the target loss data until the training termination condition is met, wherein the training termination condition may include the target loss data being less than a preset threshold, or the number of training iterations reaching a target number; thereby, the model at the end of training is determined as the appliance start-up data prediction model. Correspondingly, the predicted start-up time and predicted cooking type of the smart appliance in the current time period can be predicted based on the start-up and stop times, usage frequency, cooking recipe types, and residual heat data of the smart appliance in the historical preset time periods before the current time period.

[0051] In this embodiment of the specification, after outputting the predicted start-up time and predicted cooking type of the smart appliance in the current time period, the method further includes: The preheating path of the smart appliance is determined based on the predicted cooking type, and the preheating time of the smart appliance is determined based on the predicted start time. The smart appliance is controlled to activate the preheating path during the preheating time.

[0052] In this embodiment, after predicting the start-up time and cooking type of the smart appliance in the current time period using a model, it can be determined whether preheating is required based on the type of the smart appliance. If preheating is required (e.g., an air fryer), the preheating path can be determined based on the predicted cooking type. Specifically, at least one heat redistribution module can be determined based on the predicted cooking type. Then, the waste heat transfer path between multiple heat storage units and at least one heat redistribution module can be further determined. The preheating time of the smart appliance is determined based on the predicted start-up time. Then, the smart appliance is controlled to activate the preheating path during the preheating time, thereby achieving advance preheating of the smart appliance and improving its working efficiency. This embodiment can reduce the repeated preheating time of the cavity by 30-40% through advance preheating, effectively reducing energy consumption and start-up waiting time.

[0053] For example, such as Figure 4 As shown in the figure, this embodiment discloses a preheating method for smart appliances, including: S401: Obtain the start-stop time, usage frequency, cooking recipe type, and residual heat data of the smart appliance within a preset sample time period, and use them as sample training data; the sample training data is labeled with the sample real-time start time label and sample cooking type label of the smart appliance in the sample future time period. S403: Input the sample training data into the time series prediction model to obtain the sample prediction start time and sample prediction cooking type of the smart appliance in the future time period of the sample. S405: Determine the target loss data based on the difference between the sample predicted start time and the sample real-time start time label, and the difference between the sample predicted cooking type and the sample cooking type label; S407: Adjust the model parameters of the time series prediction model according to the target loss data until the training termination condition is met, and determine the model at the end of training as the appliance startup data prediction model.

[0054] In the embodiments of this specification, by training a multi-task model, one model can predict multiple results. Specifically, the sample training data is input into a time series prediction model to obtain the sample predicted start time and sample predicted cooking type of the smart appliance in the sample's future time period. Then, based on the difference between the sample predicted start time and the sample's real-time start time label, and the difference between the sample predicted cooking type and the sample's cooking type label, target loss data is determined. Thus, an appliance start data prediction model is trained based on the target loss data, thereby improving the model's efficiency.

[0055] In this embodiment of the specification, the plurality of heat storage units include a first heat storage unit disposed in the bottom tray area of ​​the smart appliance, a second heat storage unit disposed in the interlayer of the cooking cavity wall, and a third heat storage unit disposed in the air inlet duct or steam condensation area; the melting point of the phase change material in the first heat storage unit, the second heat storage unit, and the third heat storage unit decreases sequentially, such as... Figure 5 As shown, the recovery of waste heat generated by the smart appliance during operation based on the plurality of heat storage units includes: S501: Recover the residual heat from baking of the smart appliance based on the first heat storage unit; S503: Recover the waste heat from steaming and baking of the smart appliance based on the second heat storage unit; S505: The damp heat of the smart appliance is recovered based on the third heat storage unit.

[0056] In the embodiments of this specification, the recovery temperatures of the first heat storage unit, the second heat storage unit, and the third heat storage unit decrease sequentially. For example, the first heat storage unit can be a high-temperature heat storage unit, the second heat storage unit can be a medium-temperature heat storage unit, and the third heat storage unit can be a low-temperature heat storage unit. A high-temperature heat storage unit (100-120℃) is provided in the bottom tray area of ​​the smart appliance to recover the waste heat from high-temperature baking; a medium-temperature heat storage unit (60-90℃) is provided in the interlayer of the cooking cavity wall to recover the waste heat from steaming and baking; and a low-temperature heat storage unit (40-60℃) is provided in the air inlet duct or steam condensation area of ​​the equipment to recover humid heat.

[0057] Each heat storage unit is pre-filled with a phase change material with a different melting point. For example: the low-temperature unit is filled with paraffin-based PCM with a melting point of 45-55°C; the medium-temperature unit is filled with eutectic salt PCM with a melting point of 70-80°C; and the high-temperature unit is filled with metallic alloy PCM with a melting point of 110-120°C. Multi-temperature zone heat storage is achieved through physical location and material differences. Independent zone heat storage is possible, supporting temperature regulation within low-temperature (40-60°C), medium-temperature (60-90°C), and high-temperature (100-120°C) ranges.

[0058] In this embodiment, the preheating time and temperature can be dynamically adjusted according to the type of smart appliance and user habits. For example, the preheating temperature for the steam-bake mode is 80℃ and the time is 3 minutes; the preheating temperature for the baking mode is 120℃ and the time is 5 minutes. The control system continuously learns and optimizes through historical data, gradually adapting to the actual needs of users.

[0059] In the embodiments described in this specification, the smart appliance is also equipped with a multi-point thermal imaging acquisition system to determine the residual heat in each area. The thermal imaging system is an independent acquisition sub-module, arranged on the top of the device or the outside of the cavity, used to monitor temperature distribution. It adopts multi-area heat pipe collection + distributed heat storage modules, supporting temperature-zone energy management; adaptable to the collaboration of multiple types of kitchen appliances, the system can be linked with steam ovens, dishwashers, refrigerators, and water purification and hot water systems to form a closed-loop energy system in the kitchen. After the steam oven completes a high-temperature baking cycle, the cavity wall temperature is still above 100℃. The residual heat can be introduced into the dishwasher's water inlet loop through heat pipes and heat storage units, directly raising the washing water temperature by 15-20℃. For example, after the dishwasher finishes the drying cycle, the condenser duct still contains heat, which can be recovered and entered into the water tank preheating loop for subsequent washing of vegetables and degreasing. Therefore, the residual heat of one device can be used as preheating / auxiliary energy for another device, achieving a closed-loop energy system.

[0060] In the embodiments of this specification, functional layer coupling and reuse are implemented: non-traditional paths such as deodorization, defrosting, and anti-fogging are integrated with thermal energy management. The layout of multiple heat storage units and multiple thermal energy redistribution modules can be determined based on the characteristics of each smart appliance. The characteristics of various smart appliances are as follows: steam ovens, water heaters, etc.: require preheating of the cavity or water circuit; refrigerators, dishwashers: do not require high-temperature preheating, but can utilize waste heat for functions such as "defrosting," "drying," "dehumidifying," and "deodorizing"; glass door observation windows, sachet modules → utilize waste heat to achieve anti-fogging and fragrance release. The collaborative mechanism of this embodiment is not limited to "preheating," but also includes non-traditional functional paths such as defrosting / drying / deodorizing / anti-fogging. Each functional module (defrosting, drying, deodorizing, anti-fogging) is connected to the main heat pipe network through a thermal energy redistribution module; The intelligent control system calculates the priority weight W_i or the allocation ratio P_i based on sensor data and user behavior prediction results. The system drives and controls valves to direct waste heat to corresponding paths: If an odor is detected → the deodorization module's heat pipe is opened; if excessive humidity is detected → the waste heat path in the drying duct is opened; if fog is detected on the glass door → the anti-fog heating film is activated; if it is predicted that a user will soon retrieve frozen goods → the preheating and defrosting module is activated. This achieves multi-path heat energy reuse and dynamic fusion scheduling.

[0061] Traditional control logic is mostly linearly weighted (e.g., air volume = Σ weight × input), but in multi-path waste heat management, different module demands exhibit threshold effects and nonlinear relationships. This invention employs a nonlinear control logic model to determine the preheating allocation ratio for each heat redistribution module. It obtains the energy efficiency attributes, heat demand urgency (immediate demand intensity), and user behavior prediction factors (including high-frequency usage periods) corresponding to each heat redistribution module. Then, it determines the weights corresponding to each energy efficiency attribute, heat demand urgency, and user behavior prediction factor. Based on the weighted sum of these factors, the waste heat allocation ratio for each heat redistribution module is determined. The waste heat can then be transferred to each module according to its allocation ratio. For example, the calculation formula for the waste heat allocation ratio of each module is as follows: W_i=f(E_i,D_i,U_i)=α*E_i^2+β*log(1+D_i)+γ*√U_i Wherein, E_i: module energy efficiency benefit, using a square form to reflect "increasing benefits"; D_i: immediate demand intensity, using a logarithmic form to suppress extreme values; U_i: user behavior prediction factor, using a square root form for smoothing; α, β, γ: adjustable priority coefficients.

[0062] Finally, calculate W_i for all modules and normalize it using softmax: P_i=exp(W_i) / Σexp(W_j) to obtain the waste heat distribution ratio for each module. For example, this embodiment provides a method for waste heat distribution in smart appliances, including: 1. Input stage: Sensors collect temperature / humidity / odor data, and appliance startup data prediction models provide predicted values; 2. Calculation stage: Substitute the data into the nonlinear control model to obtain the weights of each module; 3. Scheduling phase: Waste heat is allocated via P_i to drive the corresponding control valves; 4. Feedback phase: Real-time monitoring of the effects of each module; if the target is not met, dynamically adjust the coefficients of α, β, and γ. The nonlinear model in this embodiment can more realistically reflect the priority relationships in complex kitchen scenarios, ensuring that the system balances energy saving and functional requirements. This specification also provides an intelligent appliance that uses the above-described waste heat distribution method. The intelligent appliance includes at least one of the following: a steam oven, an electric oven, an air fryer, a steam oven combo, a dishwasher, a refrigerator, and a water purifier.

[0063] This specification also provides a waste heat distribution device for a smart appliance. The smart appliance includes a heat acquisition module, multiple heat storage units, and multiple heat redistribution modules. The heat acquisition module is connected to the multiple heat storage units. The heat acquisition module includes a control valve, which is connected to the multiple heat redistribution modules. Figure 6 As shown, the waste heat distribution device includes: Waste heat recovery module 610 is used to recover waste heat generated by the smart appliance during operation based on the plurality of heat storage units; The first strategy construction module 620 is used to determine the first priority of each heat energy redistribution module according to the energy efficiency attributes corresponding to each of the plurality of heat energy redistribution modules, and to construct a first heat distribution strategy. The second strategy construction module 630 is used to determine the second priority of each heat energy redistribution module based on the urgency of the heat demand corresponding to each of the plurality of heat energy redistribution modules, and to construct a second heat allocation strategy. The filtering module 640 is used to filter out a target heat energy redistribution module from the plurality of heat energy redistribution modules based on at least one of the first heat distribution strategy, the second heat distribution strategy, and the heat data corresponding to the waste heat. The waste heat transfer module 650 is used to control the heat acquisition module to transfer the waste heat to the target heat redistribution module by adjusting the opening direction of the control valve.

[0064] In one exemplary embodiment, the filtering module includes: The time period determination unit is used to determine the high-frequency usage period corresponding to each heat energy redistribution module based on the usage frequency of the plurality of heat energy redistribution modules in each historical time period; The third strategy construction unit is used to determine the third priority of each heat redistribution module in each preset time period based on the high-frequency usage period corresponding to each heat redistribution module, and to construct the third heat distribution strategy. The filtering unit is used to filter out a target heat energy redistribution module from the plurality of heat energy redistribution modules based on at least one of the first heat distribution strategy, the second heat distribution strategy, the third heat distribution strategy, and the heat data corresponding to the waste heat.

[0065] In one exemplary embodiment, the filtering unit includes: The first determining subunit is used to determine the first priority of each heat redistribution module according to the first heat distribution strategy. The second determining subunit is used to determine the second priority of each heat redistribution module according to the second heat distribution strategy; The third determining subunit is used to determine the third priority of each heat redistribution module in the current time period according to the third heat distribution strategy. The target priority determination subunit is used to determine the target priority of each heat energy redistribution module based on the first priority, second priority, and third priority corresponding to each heat energy redistribution module. The filtering subunit is used to filter out the target heat energy redistribution module from the plurality of heat energy redistribution modules according to the target priority of each heat energy redistribution module.

[0066] In one exemplary embodiment, the apparatus further includes: The sample data acquisition module is used to acquire the start-stop time, usage frequency, cooking recipe type, and residual heat data of the smart appliance within a preset sample time period, as sample training data; the sample training data is labeled with the sample real-time start time tag of the smart appliance in the sample future time period. The sample time prediction module is used to input the sample training data into the time series prediction model to obtain the sample prediction start time of the smart appliance in the future time period of the sample. The training module is used to train the time series prediction model based on the difference between the predicted start time of the sample and the real-time start time label of the sample, so as to obtain the appliance start data prediction model. The time prediction module is used to input the start-up and stop times, usage frequency, cooking recipe types, and residual heat data of the smart appliance in the historical preset time periods before the current time period into the appliance start-up data prediction model, and output the predicted start-up time of the smart appliance in the current time period.

[0067] In one exemplary embodiment, the sample training data is labeled with the sample cooking type label of the smart appliance in the sample's future time period, and the time series prediction model also outputs the sample predicted cooking type. The training module includes: The target data determination unit is used to determine target loss data based on the difference between the sample predicted start time and the sample real-time start time label, and the difference between the sample predicted cooking type and the sample cooking type label; The model determination unit is used to adjust the model parameters of the time series prediction model according to the target loss data until the training end condition is met, and to determine the model at the end of training as the appliance startup data prediction model. Correspondingly, the time prediction module is also used to input the start-up and stop times, usage frequency, cooking recipe types and residual heat data of the smart appliance in the historical preset time periods before the current time period into the appliance start-up data prediction model, and output the predicted start-up time and predicted cooking type of the smart appliance in the current time period.

[0068] In one exemplary embodiment, the apparatus further includes: The preheating time determination module is used to determine the preheating path of the smart appliance based on the predicted cooking type, and to determine the preheating time of the smart appliance based on the predicted start time. A preheating path activation module is used to control the smart appliance to activate the preheating path during the preheating time.

[0069] In one exemplary embodiment, the plurality of heat storage units include a first heat storage unit disposed in the bottom tray area of ​​the smart appliance, a second heat storage unit disposed in the interlayer of the cooking cavity wall, and a third heat storage unit disposed in the air inlet duct or steam condensation area; the melting point of the phase change material in the first heat storage unit, the second heat storage unit, and the third heat storage unit decreases sequentially, and the waste heat recovery module includes: The first recovery unit is used to recover the waste heat from baking of the smart appliance based on the first heat storage unit; The second recovery unit is used to recover the waste heat from steaming and baking of the smart appliance based on the second heat storage unit; The third recovery unit is used to recover the damp heat of the smart appliance based on the third heat storage unit.

[0070] The apparatus and method embodiments described herein are based on the same inventive concept.

[0071] This specification provides an electronic device, which includes a processor and a memory. The memory stores a computer program, which is loaded and executed by the processor to implement the waste heat distribution method of the smart appliance as described above.

[0072] Embodiments of this application also provide a computer storage medium storing a computer program, which is loaded and executed by a processor to implement the waste heat distribution method for intelligent appliances as described above.

[0073] Embodiments of this application also provide a computer program product, including a computer program loaded and executed by a processor to implement the waste heat distribution method for smart appliances as described above.

[0074] Optionally, in the embodiments of this specification, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0075] The memory described in the embodiments of this specification can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for the functions, etc.; the data storage area may store data created according to the use of the device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide the processor with access to the memory.

[0076] The waste heat distribution method for intelligent appliances provided in the embodiments of this specification can be executed on a mobile terminal, computer terminal, server, or similar computing device. Taking running on a server as an example, Figure 7 This is a hardware structure block diagram of a server for a waste heat distribution method for intelligent electrical appliances provided in the embodiments of this specification. For example... Figure 7As shown, the server 700 can vary significantly due to different configurations or performance. It may include one or more Central Processing Units (CPUs) 710 (CPUs 710 may include, but are not limited to, microprocessors (MCUs) or programmable logic devices (FPGAs), a memory 730 for storing data, and one or more storage media 720 (e.g., one or more mass storage devices) for storing application programs 723 or data 722. The memory 730 and storage media 720 may be temporary or persistent storage. The program stored in the storage media 720 may include one or more modules, each module may include a series of instruction operations on the server. Furthermore, the CPU 710 may be configured to communicate with the storage media 720 and execute the series of instruction operations stored in the storage media 720 on the server 700. Server 700 may also include one or more power supplies 760, one or more wired or wireless network interfaces 750, one or more input / output interfaces 740, and / or one or more operating systems 721, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0077] The input / output interface 740 can be used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of server 700. In one example, the input / output interface 740 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the input / output interface 740 may be a radio frequency (RF) module used for wireless communication with the Internet.

[0078] Those skilled in the art will understand that Figure 7 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, server 700 may also include... Figure 7 The more or fewer components shown, or having the same Figure 7 The different configurations shown.

[0079] As can be seen from the embodiments of the waste heat distribution method, apparatus, device, or storage medium of the smart appliance provided in this application, the smart appliance of this application is provided with a heat acquisition module, multiple heat storage units, and multiple heat redistribution modules. The heat acquisition module is connected to multiple heat storage units, and a control valve is provided in the heat acquisition module. The control valve is connected to multiple heat redistribution modules. By constructing multiple heat storage units, the waste heat in the smart appliance can be fully recovered, and the selective distribution of waste heat can be achieved through multiple heat redistribution modules. The method includes: recovering the waste heat generated by the smart appliance during operation based on multiple heat storage units; and distributing the waste heat according to the energy of each of the multiple heat redistribution modules. The system determines the first priority of each heat redistribution module based on its efficiency attributes and constructs a first heat allocation strategy. Based on the urgency of heat demand for each module, a second priority is determined, and a second heat allocation strategy is constructed. Based on at least one of the first and second heat allocation strategies and the corresponding heat data for waste heat, a target heat redistribution module is selected from the multiple modules. By adjusting the opening direction of the control valve, the heat acquisition module is controlled to transfer waste heat to the target heat redistribution module. This achieves efficient utilization of waste heat resources and realizes intelligent and dynamic allocation of waste heat in smart appliances.

[0080] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0082] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer storage medium, such as a read-only memory, a disk, or an optical disk.

[0083] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for distributing waste heat in intelligent electrical appliances, characterized in that, The intelligent appliance includes a heat acquisition module, multiple heat storage units, and multiple heat redistribution modules. The heat acquisition module is connected to the multiple heat storage units. The heat acquisition module includes a control valve, which is connected to the multiple heat redistribution modules. The method includes: The waste heat generated by the smart appliance during operation is recovered based on the multiple heat storage units; Based on the energy efficiency attributes of each of the multiple heat redistribution modules, the first priority of each heat redistribution module is determined, and a first heat distribution strategy is constructed. Based on the urgency of the heat demand corresponding to each of the multiple heat redistribution modules, a second priority is determined for each heat redistribution module, and a second heat distribution strategy is constructed. Based on at least one of the first heat distribution strategy and the second heat distribution strategy, and the heat data corresponding to the waste heat, a target heat redistribution module is selected from the plurality of heat redistribution modules; By adjusting the opening direction of the control valve, the heat acquisition module is controlled to transfer the waste heat to the target heat redistribution module.

2. The method according to claim 1, characterized in that, The step of selecting a target heat energy redistribution module from the plurality of heat energy redistribution modules based on at least one of the first heat distribution strategy and the second heat distribution strategy, and the heat data corresponding to the waste heat, includes: Based on the usage frequency of the multiple heat redistribution modules in each historical period, determine the high-frequency usage period corresponding to each heat redistribution module; Based on the high-frequency usage period corresponding to each heat redistribution module, the third priority of each heat redistribution module in each preset period is determined, and a third heat distribution strategy is constructed. Based on at least one of the first heat distribution strategy, the second heat distribution strategy, and the third heat distribution strategy, and the heat data corresponding to the waste heat, a target heat redistribution module is selected from the plurality of heat redistribution modules.

3. The method according to claim 2, characterized in that, The step of selecting a target heat energy redistribution module from the plurality of heat energy redistribution modules based on at least one of the first heat distribution strategy, the second heat distribution strategy, and the third heat distribution strategy, and the heat data corresponding to the waste heat, includes: The first priority of each heat redistribution module is determined according to the first heat distribution strategy; The second priority of each heat redistribution module is determined according to the second heat distribution strategy; The third priority of each heat redistribution module in the current time period is determined according to the third heat distribution strategy. The target priority of each heat redistribution module is determined based on its first priority, second priority, and third priority. Based on the target priority of each heat redistribution module, a target heat redistribution module is selected from the plurality of heat redistribution modules.

4. The method according to claim 2, characterized in that, The method further includes: The start-stop time, usage frequency, cooking recipe type, and residual heat data of the smart appliance within a preset sample time period are obtained as sample training data; the sample training data is labeled with the real-time start time tag of the smart appliance in the sample future time period. The sample training data is input into the time series prediction model to obtain the sample prediction start time of the smart appliance in the future time period of the sample. Based on the difference between the predicted startup time of the sample and the real-time startup time label of the sample, the time series prediction model is trained to obtain the appliance startup data prediction model. The start-up and shutdown times, usage frequency, cooking recipe types, and residual heat data of the smart appliance during historical preset time periods prior to the current time period are input into the appliance start-up data prediction model, and the predicted start-up time of the smart appliance during the current time period is output.

5. The method according to claim 4, characterized in that, The sample training data is labeled with the sample cooking type label of the smart appliance in the future time period of the sample. The time series prediction model also outputs the sample predicted cooking type. The step of training the time series prediction model based on the difference between the sample predicted start time and the sample real-time start time label to obtain the appliance start data prediction model includes: The target loss data is determined based on the difference between the sample's predicted start time and the sample's real-time start time label, and the difference between the sample's predicted cooking type and the sample's cooking type label. The model parameters of the time series prediction model are adjusted according to the target loss data until the training termination condition is met, and the model at the end of training is determined as the appliance startup data prediction model. Accordingly, the step of inputting the start-up and shutdown times, usage frequency, cooking recipe types, and residual heat data of the smart appliance during historical preset time periods prior to the current time period into the appliance start-up data prediction model, and outputting the predicted start-up time of the smart appliance in the current time period, includes: The start-up and shutdown times, usage frequency, cooking recipe types, and residual heat data of the smart appliance during historical preset time periods prior to the current time period are input into the appliance start-up data prediction model, and the predicted start-up time and predicted cooking type of the smart appliance during the current time period are output.

6. The method according to claim 5, characterized in that, After outputting the predicted start-up time and predicted cooking type of the smart appliance for the current time period, the method further includes: The preheating path of the smart appliance is determined based on the predicted cooking type, and the preheating time of the smart appliance is determined based on the predicted start time. The smart appliance is controlled to activate the preheating path during the preheating time.

7. The method according to claim 1, characterized in that, The plurality of heat storage units include a first heat storage unit disposed in the bottom tray area of ​​the smart appliance, a second heat storage unit disposed in the interlayer of the cooking cavity wall, and a third heat storage unit disposed in the air inlet duct or steam condensation area; the melting point of the phase change material in the first heat storage unit, the second heat storage unit, and the third heat storage unit decreases sequentially, and the recovery of waste heat generated by the smart appliance during operation based on the plurality of heat storage units includes: The waste heat from baking in the smart appliance is recovered based on the first heat storage unit; The second heat storage unit recovers the waste heat from the steaming and baking processes of the smart appliance; The third heat storage unit recovers the damp heat from the smart appliance.

8. A waste heat distribution device for intelligent electrical appliances, characterized in that, The intelligent appliance includes a heat acquisition module, multiple heat storage units, and multiple heat redistribution modules. The heat acquisition module is connected to the multiple heat storage units. The heat acquisition module includes a control valve, which is connected to the multiple heat redistribution modules. The device comprises: The waste heat recovery module is used to recover the waste heat generated by the smart appliance during operation based on the multiple heat storage units; The first strategy construction module is used to determine the first priority of each heat energy redistribution module according to the energy efficiency attributes of each of the plurality of heat energy redistribution modules, and to construct a first heat distribution strategy. The second strategy construction module is used to determine the second priority of each heat energy redistribution module based on the urgency of the heat demand corresponding to each of the plurality of heat energy redistribution modules, and to construct a second heat allocation strategy. The filtering module is used to filter out a target heat energy redistribution module from the plurality of heat energy redistribution modules based on at least one of the first heat distribution strategy, the second heat distribution strategy, and the heat data corresponding to the waste heat. The waste heat transfer module is used to control the heat acquisition module to transfer the waste heat to the target heat redistribution module by adjusting the opening direction of the control valve.

9. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing a computer program that is loaded and executed by the processor to implement the waste heat distribution method of the smart appliance as described in any one of claims 1-7.

10. A smart appliance, characterized in that, The smart appliance employs the waste heat distribution method described in any one of claims 1-7, and the smart appliance includes at least one of a steam oven, an electric oven, an air fryer, a steam oven, a dishwasher, a refrigerator, and a water purifier.

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