Control method of intelligent microwave oven, intelligent microwave oven, equipment and storage medium

Abstract

The invention relates to a control method of an intelligent microwave oven, the intelligent microwave oven, equipment and a storage medium, and the control method of the intelligent microwave oven comprises the following steps: obtaining multi-mode information of to-be-heated food in the intelligent microwave oven in real time; determining a microwave energy distribution strategy matched with the to-be-heated food based on the multi-modal information of the to-be-heated food; and dynamically controlling the working state of each waveguide channel in the intelligent microwave oven based on a microwave energy distribution strategy. Through the application, the problem that the microwave energy in the cavity cannot be dynamically regulated and controlled in real time and is difficult to adapt to diversified cooking scenes and food characteristics is solved, and the microwave energy in the cavity is dynamically regulated and controlled in real time so as to adapt to the diversified cooking scenes and food characteristics.

Description

Technical Field

[0001] This application relates to the field of smart home appliances, and in particular to control methods, smart microwave ovens, devices, and storage media for smart microwave ovens. Background Technology

[0002] Microwave ovens, as frequently used smart kitchen appliances in modern households, have become an important part of the smart kitchen ecosystem. Their core heating principle involves generating microwave energy through a magnetron and then channeling that energy into the heating cavity via a specific channel to rapidly heat food. However, microwave transmission typically uses a single waveguide path. To improve the uneven distribution of microwaves within the cavity, mechanical stirrers are commonly used to enhance heating uniformity. But limited by the stirrer's mechanical rotation speed, the microwave field strength refresh rate is low, making it difficult to eliminate temperature differences between hot and cold spots and to achieve real-time dynamic control of microwave energy within the cavity. This makes it difficult to adapt to diverse cooking scenarios and food characteristics.

[0003] There is currently no effective solution to the problem that related technologies cannot achieve real-time dynamic control of microwave energy within the cavity, making it difficult to adapt to diverse cooking scenarios and food characteristics. Summary of the Invention

[0004] This embodiment provides a control method, a smart microwave oven, a device, and a storage medium for a smart microwave oven, in order to solve the problem in related technologies that it is impossible to achieve real-time dynamic control of microwave energy within the cavity, thus making it difficult to adapt to diverse cooking scenarios and food characteristics.

[0005] Firstly, this embodiment provides a control method for an intelligent microwave oven, including:

[0006] Real-time acquisition of multimodal information of the food to be heated inside the smart microwave oven;

[0007] Based on the multimodal information of the food to be heated, a microwave energy distribution strategy matching the food to be heated is determined;

[0008] Based on the microwave energy distribution strategy, the working state of each waveguide channel in the intelligent microwave oven is dynamically controlled.

[0009] In some embodiments, the real-time acquisition of multimodal information of the food to be heated inside the smart microwave oven includes:

[0010] Based on a multimodal detection network, the food to be heated inside the smart microwave oven is sensed in real time to obtain the multimodal information of the food to be heated; the multimodal detection network consists of multiple sensing modules.

[0011] In some embodiments, the multimodal information includes shape features of the food to be heated; determining a microwave energy distribution strategy matching the food to be heated based on the multimodal information of the food to be heated includes:

[0012] Determine the shape coefficient corresponding to the shape characteristics of the food to be heated;

[0013] The shape features and shape coefficients of the food to be heated are analyzed and calculated using a three-dimensional volume reconstruction algorithm to obtain the volume of the food to be heated.

[0014] Based on the volume of the food to be heated and the multimodal information, a microwave energy distribution strategy matching the food to be heated is generated.

[0015] In some embodiments, determining a microwave energy distribution strategy matching the food to be heated based on the multimodal information of the food to be heated includes:

[0016] Determine a preset cooking mode that matches the multimodal information of the food to be heated;

[0017] Based on the target microwave energy characteristics indicated by the preset cooking mode, a microwave energy distribution strategy matching the food to be heated is generated.

[0018] In some embodiments, dynamically controlling the operating state of each waveguide channel in the smart microwave oven based on the microwave energy distribution strategy includes:

[0019] Based on the microwave energy distribution strategy, corresponding control commands are generated to dynamically control the working state of each waveguide channel in the intelligent microwave oven; the working state includes the on / off state of each waveguide channel and the microwave power distribution ratio.

[0020] In some embodiments, prior to acquiring the multimodal information of the food to be heated inside the smart microwave oven in real time, the method further includes:

[0021] The load on the heating area inside the smart microwave oven is detected to obtain multiple target parameters, including weight parameters, capacitance change, and temperature gradient.

[0022] When each of the target parameters meets the preset threshold conditions, the smart microwave oven is determined to be in an unloaded state.

[0023] The redundant channels in each waveguide channel are shut down under the no-load state.

[0024] Secondly, this embodiment provides an intelligent microwave oven, which includes a multi-modal detection module, a controller, and an RF switch matrix; the RF switch matrix is ​​provided with multiple waveguide channels;

[0025] The multimodal detection module is used to acquire multimodal information of the food to be heated inside the smart microwave oven in real time;

[0026] The controller is used to determine a microwave energy distribution strategy that matches the food to be heated based on the multimodal information of the food to be heated.

[0027] The controller is also used to dynamically control the working state of each waveguide channel in the smart microwave oven based on the microwave energy distribution strategy.

[0028] In some embodiments, the radio frequency switch matrix further includes a switching unit for controlling the on / off state of each of the waveguide channels.

[0029] Thirdly, this embodiment provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the control method for the intelligent microwave oven described in the first aspect.

[0030] Fourthly, this embodiment provides a storage medium storing a computer program that, when executed by a processor, implements the intelligent microwave oven control method described in the first aspect above.

[0031] Compared with related technologies, the control method, intelligent microwave oven, device, and storage medium provided in this embodiment acquire multimodal information of the food to be heated inside the intelligent microwave oven in real time; determine a microwave energy distribution strategy matching the food to be heated based on the multimodal information of the food to be heated; and dynamically control the working state of each waveguide channel in the intelligent microwave oven based on the microwave energy distribution strategy. This solves the problem that it is impossible to achieve real-time dynamic regulation of microwave energy inside the cavity, thus making it difficult to adapt to diverse cooking scenarios and food characteristics. It realizes real-time dynamic regulation of microwave energy inside the cavity to adapt to diverse cooking scenarios and food characteristics.

[0032] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0033] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0034] Figure 1 This is a flowchart of a control method for an intelligent microwave oven provided in an embodiment of this application;

[0035] Figure 2 This is a flowchart of a microwave energy allocation strategy generation method provided in an embodiment of this application;

[0036] Figure 3 This is a flowchart of a microwave energy allocation strategy generation method provided in another embodiment of this application;

[0037] Figure 4 This is a flowchart of an embodiment of the no-load detection method provided in this application;

[0038] Figure 5 This is a flowchart of a preferred embodiment of the control method for an intelligent microwave oven provided in this application;

[0039] Figure 6 This is a structural block diagram of a control device for an intelligent microwave oven provided in an embodiment of this application;

[0040] Figure 7 This is a structural block diagram of an intelligent microwave oven provided in one embodiment of this application.

[0041] In the diagram: 10, Acquisition module; 20, Analysis module; 30, Control module; 40, Multimodal detection module; 50, Controller; 60, RF switch matrix. Detailed Implementation

[0042] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0043] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these” used in this application do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to these processes, methods, products, or devices. Words such as “connected,” “linked,” and “coupled” used in this application are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. Normally, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," "third," etc., used in this application are merely to distinguish similar objects and do not represent a specific order of objects.

[0044] This embodiment provides a control method for an intelligent microwave oven. Figure 1 This is a flowchart of the control method for the intelligent microwave oven in this embodiment, as shown below. Figure 1 As shown, the process includes the following steps:

[0045] Step S110: Acquire multimodal information of the food to be heated inside the smart microwave oven in real time;

[0046] Specifically, a multimodal detection network is used to acquire real-time multimodal information about the food to be heated inside the smart microwave oven. This multimodal detection network integrates multiple high-precision sensing modules, including infrared thermal imaging sensors (e.g., with an accuracy of ±1℃), capacitive proximity sensors (e.g., with a resolution of 0.5mm), and weighing sensors (e.g., with a measurement accuracy of ±3g). Through multi-source information fusion processing, the multimodal information of the food to be heated is accurately acquired, including spatial coordinates based on the cavity coordinate system, contour shape features, mass data, and temperature distribution information, providing a complete data foundation for subsequent adaptive energy distribution control.

[0047] Step S120: Based on the multimodal information of the food to be heated, determine a microwave energy distribution strategy that matches the food to be heated;

[0048] Specifically, based on multimodal information such as the location, shape features, and mass data of the food to be heated, an adaptive algorithm generates a microwave energy distribution strategy in real time that matches the food. This microwave energy distribution strategy is an energy distribution scheme based on the optimal microwave transmission path, used to indicate the waveguide channels to be activated and their corresponding microwave power distribution ratios to achieve precise and uniform heating effects. In other embodiments, based on a pre-established food thermodynamic model, the spatial distribution of microwave energy under specific shapes and arrangements can be calculated using numerical simulation methods, and a corresponding energy delivery strategy can be generated to effectively improve heating uniformity and energy efficiency.

[0049] For example, the microwave energy distribution strategy is shown in Table 1. When the food to be heated is located in the front left region of the cavity, the CH1 and CH4 waveguide channels are activated, and microwave energy is distributed in a power ratio of 70:30, ultimately achieving a field strength uniformity of 85%. When the food to be heated is located in the center region of the cavity, the CH2 and CH3 channels are activated, and a balanced power distribution of 50:50 is adopted, achieving a field strength uniformity of 92%. If the food to be heated is located in the rear right region, the CH3 and CH4 channels are used for coordinated heating in a power ratio of 60:40, so that the field strength uniformity in this region reaches 88%.

[0050] Table 1

[0051]

[0052] Step S130: Based on the microwave energy distribution strategy, dynamically control the working status of each waveguide channel in the intelligent microwave oven.

[0053] Specifically, based on a real-time generated microwave energy distribution strategy, the system dynamically controls multiple waveguide channels in the smart microwave oven. It should be noted that the control of multiple waveguide channels is typically achieved using an RF switch matrix, enabling dynamic reconfiguration of the microwave path through a programmable waveguide matrix. For example, a 4×4 RF MEMS switch matrix can be used, employing gold-electrode silicon nitride dielectric MEMS switches (3×3×0.5mm in size). This matrix supports electrostatic drive (30V / 300μs), has an insertion loss of less than 0.3dB, can flexibly configure 16 waveguide path combinations, and achieves a field intensity modulation range of 0-100%.

[0054] During actual heating, the system can also adjust the working status of each channel in real time based on changes in the food's state (such as temperature rise curves) to achieve beamforming or energy focusing, thereby effectively avoiding local overheating or underheating and significantly improving heating uniformity and energy utilization efficiency.

[0055] Microwave ovens, as frequently used smart kitchen appliances in modern households, have become an important part of the smart kitchen ecosystem. However, their microwave transmission typically uses a single waveguide path. To improve the uneven distribution of microwaves within the cavity, mechanical stirrers are commonly used to enhance heating uniformity. But limited by the mechanical speed of the stirrer, the microwave field strength refresh rate is low, making it difficult to eliminate temperature differences between hot and cold spots and to achieve real-time dynamic control of microwave energy within the cavity. Consequently, they are ill-suited to diverse cooking scenarios and food characteristics.

[0056] Compared to existing technologies, this application acquires multimodal information of the food to be heated inside a smart microwave oven in real time; determines a microwave energy distribution strategy matching the food based on this multimodal information; and dynamically controls the operating state of each waveguide channel in the smart microwave oven based on the microwave energy distribution strategy. Based on this, it utilizes multimodal sensors to fuse and accurately perceive the real-time state information of the food, and dynamically reconstructs the microwave transmission path based on this information to intelligently distribute microwave energy. This solves the problem of the inability to achieve real-time dynamic control of microwave energy within the cavity, thus making it difficult to adapt to diverse cooking scenarios and food characteristics. It achieves real-time dynamic control of microwave energy within the cavity to adapt to diverse cooking scenarios and food characteristics.

[0057] In some embodiments, the real-time acquisition of multimodal information of the food to be heated inside the smart microwave oven in step S110 includes the following steps:

[0058] Based on a multimodal detection network, the food to be heated inside the smart microwave oven is sensed in real time to obtain multimodal information about the food; the multimodal detection network consists of multiple sensing modules.

[0059] In this embodiment, the multimodal detection network integrates multiple sensing modules, including infrared thermal imaging sensors, capacitive proximity sensors, and weighing sensors, to achieve real-time, non-contact sensing of the food to be heated inside the oven cavity. Through multi-source sensor fusion technology, the network simultaneously collects the food's location information, shape features, and quality data, providing a high-precision and multi-dimensional data foundation for subsequent decision-making.

[0060] Specifically, infrared thermal imaging sensors can acquire the surface temperature distribution and macroscopic shape of food; capacitive proximity sensors are used to detect the vertical height and edge contour of food within the cavity; and weighing sensors accurately measure its mass. These pieces of information together constitute multimodal sensing data, including position, shape, and mass, to comprehensively characterize the current state of the food.

[0061] In this embodiment, based on a multimodal detection network, the food to be heated inside the smart microwave oven is sensed in real time to obtain multimodal information about the food. The multimodal detection network consists of multiple sensing modules. By fusing multimodal sensors, it can accurately sense multi-dimensional parameters such as the position, geometry, and dielectric properties of the food, providing data for subsequent microwave energy distribution and thus helping to improve the accuracy of microwave energy distribution.

[0062] In some embodiments, the multimodal information includes shape features of the food to be heated; such as Figure 2 As shown, step S120, which determines a microwave energy distribution strategy matching the food to be heated based on the multimodal information of the food to be heated, includes the following steps:

[0063] Step S121: Determine the shape coefficient corresponding to the shape characteristics of the food to be heated;

[0064] Step S122: Using a three-dimensional volume reconstruction algorithm, the shape features and shape coefficients of the food to be heated are analyzed and calculated to obtain the volume of the food to be heated;

[0065] Step S123: Based on the volume and multimodal information of the food to be heated, generate a microwave energy distribution strategy that matches the food to be heated.

[0066] Specifically, the shape coefficients (such as sphericity, aspect ratio, or similarity coefficient with standard geometric objects) are determined based on the shape characteristics of the food to be heated. Then, a three-dimensional volume reconstruction algorithm is used to combine the shape characteristics and shape coefficients to reconstruct a three-dimensional model of the food to be heated and accurately calculate its volume.

[0067] Subsequently, by integrating the food's volume information and other multimodal information (such as mass and location), an adaptive optimization algorithm is used to generate a matching microwave energy distribution strategy. This strategy can specify the on / off states of different waveguide channels and the microwave power ratio, thereby achieving on-demand distribution of microwave energy within the cavity.

[0068] In this embodiment, the shape coefficient corresponding to the shape characteristics of the food to be heated is determined. The shape characteristics and shape coefficient of the food to be heated are analyzed and calculated using a three-dimensional volume reconstruction algorithm to obtain the volume of the food to be heated. Then, based on the volume and multimodal information of the food to be heated, a microwave energy distribution strategy matching the food to be heated is generated. By introducing food volume information, the heating adaptability and control accuracy of complex-shaped foods are significantly improved.

[0069] In some of these embodiments, such as Figure 3 As shown, step S120, which determines a microwave energy distribution strategy matching the food to be heated based on the multimodal information of the food to be heated, includes the following steps:

[0070] Step S124: Determine a preset cooking mode that matches the multimodal information of the food to be heated;

[0071] Step S125: Generate a microwave energy distribution strategy that matches the food to be heated, based on the target microwave energy characteristics indicated by the preset cooking mode.

[0072] Specifically, based on the multimodal information acquired by the multimodal detection network, it is matched with a pre-established cooking pattern database to determine a preset cooking pattern that matches the food to be heated. This pattern includes key parameters such as the ideal operating frequency, field distribution type, uniformity, and power density of the food to be heated. The cooking pattern database can be continuously updated and adjusted to adapt to diverse cooking scenarios and user needs.

[0073] For example, the cooking mode database is shown in Table 2. In defrost mode, a 2.45GHz operating frequency and a circular field distribution are used, achieving a gentle heating with 85% uniformity at a power density of 0.5W / cm², effectively preventing localized food denaturation. Grill mode uses a 5.8GHz high frequency and a focused field distribution, combined with a high power density of 3W / cm², forming a concentrated energy zone with 70% uniformity, suitable for cooking with a crispy surface. Steaming mode also operates in the 2.45GHz band, employing a uniform field pattern, achieving up to 90% field uniformity at a medium power density of 1.2W / cm², ensuring the food is heated evenly and has a tender texture. The parameters of each mode can be fine-tuned according to the actual food condition to adapt to diverse cooking needs.

[0074] Table 2

[0075]

[0076] Subsequently, based on the target microwave energy characteristics (such as operating frequency, field distribution type, uniformity, and power density) indicated by the matching cooking mode, and combined with the multimodal information of the current food, a strategy generation algorithm is used to analyze and obtain a microwave energy allocation strategy that precisely matches it. This strategy specifically specifies the on / off state and power ratio of each waveguide channel to ensure that the energy allocation is highly consistent with the requirements of the preset cooking mode.

[0077] This embodiment determines a preset cooking mode that matches the multimodal information of the food to be heated. Based on the target microwave energy characteristics indicated by the preset cooking mode, a microwave energy distribution strategy matching the food to be heated is generated. This matches the optimal microwave transmission path for different cooking modes, significantly improving the convenience and controllability of intelligent microwave heating and achieving highly adaptable control for different cooking needs.

[0078] In some embodiments, step S130, which dynamically controls the operating state of each waveguide channel in the smart microwave oven based on a microwave energy distribution strategy, includes the following steps:

[0079] Based on the microwave energy distribution strategy, corresponding control commands are generated to dynamically control the working state of each waveguide channel in the smart microwave oven; the working state includes the on / off state of each waveguide channel and the microwave power distribution ratio.

[0080] Specifically, based on a microwave energy distribution strategy matched to the food to be heated, this strategy is translated into corresponding control commands to precisely drive the coordinated operation of each waveguide channel in the smart microwave oven. These control commands specify the on / off state of each channel and its microwave power distribution ratio. Through programmable radio frequency control circuits (such as an RF switch matrix), the path selection and energy weighting of the microwave signal are achieved, thereby synthesizing the target energy field shape within the cavity that meets the strategy requirements.

[0081] It should be noted that during the heating process, the control commands can be dynamically adjusted based on multimodal sensor feedback. For example, the channel power ratio can be adaptively fine-tuned or the active channel combination can be switched according to the temperature rise curve. This control mechanism can effectively cope with changes in food state, significantly improve heating uniformity and energy utilization efficiency, and ultimately achieve intelligent and adaptive precise heating effects.

[0082] In this embodiment, based on the microwave energy distribution strategy, corresponding control commands are generated to dynamically control the working state of each waveguide channel in the smart microwave oven. The working state includes the on / off state of each waveguide channel and the microwave power distribution ratio, thereby breaking through the limitation of fixed field strength distribution and realizing dynamic reconstruction of microwave transmission path.

[0083] In some of these embodiments, such as Figure 4 As shown, before acquiring the multimodal information of the food to be heated inside the smart microwave oven in real time, the control method of the smart microwave oven mentioned above also includes the following steps:

[0084] Step S101: Perform load detection on the heating placement area inside the smart microwave oven to obtain multiple target parameters; the target parameters include weight parameters, capacitance change and temperature gradient.

[0085] Step S102: When each target parameter meets the preset threshold condition, it is determined that the smart microwave oven is in an unloaded state.

[0086] Step S103: Control the shutdown of redundant channels in each waveguide channel under no-load conditions.

[0087] Specifically, the built-in multimodal sensors perform real-time load detection on the heated placement area within the cavity to obtain multiple target parameters such as weight, capacitance change, and temperature gradient. A high-precision weighing sensor monitors the load mass, a capacitive proximity sensor detects changes in the dielectric constant within the cavity to identify the presence of objects, and an infrared thermal imaging sensor captures differences in temperature distribution.

[0088] Furthermore, when each target parameter meets the preset threshold conditions, the smart microwave oven is determined to be in an unloaded state. For example, the target parameters and their corresponding preset threshold conditions are shown in Table 3. Specifically, the weight parameter of the heating zone load is continuously monitored; if the detected value is consistently below 5g for more than 3 seconds, it is considered to meet the weight-based empty condition. Simultaneously, changes in the dielectric properties within the cavity are detected by a capacitance sensor; if the capacitance change ΔC / C is consistently below 0.1% and maintained for 5 consecutive sampling cycles, the capacitance dimension is determined to meet the empty-load characteristics. Additionally, the cavity is scanned by an infrared thermal imaging sensor; if a temperature gradient below 0.5℃ / cm² is detected, it is confirmed that there is no significant thermal load. In practical applications, one or more target parameters can be selected and configured to participate in the empty-load determination according to specific detection needs, balancing system flexibility and state recognition accuracy.

[0089] Table 3

[0090]

[0091] Once the system is determined to be in an unloaded state, the control unit will immediately execute the corresponding control strategy, shut down the redundant channels in each waveguide channel, and maintain only the minimum necessary number of channels in standby mode, so as to effectively reduce standby power consumption and significantly improve the reliability and security of the system.

[0092] In this embodiment, the load of the heating area inside the smart microwave oven is detected to obtain multiple target parameters, including weight parameters, capacitance change and temperature gradient. When each target parameter meets the preset threshold condition, the smart microwave oven is determined to be in an unloaded state. Then, the redundant channels in each waveguide channel are controlled to be turned off in the unloaded state, reducing energy loss under unload or light load and achieving effective unload protection.

[0093] The present embodiment will now be described and illustrated through preferred embodiments.

[0094] Figure 5 This is a flowchart of the control method for the intelligent microwave oven according to a preferred embodiment, as shown below. Figure 5 As shown, the control method of this smart microwave oven includes the following steps:

[0095] Step S510: Based on the multimodal detection network, the food to be heated inside the smart microwave oven is sensed in real time to obtain multimodal information of the food to be heated; the multimodal detection network consists of multiple sensing modules;

[0096] Step S520: Determine a preset cooking mode that matches the multimodal information of the food to be heated;

[0097] Step S530: Generate a microwave energy distribution strategy that matches the food to be heated based on the target microwave energy characteristics indicated by the preset cooking mode.

[0098] Step S540: Based on the microwave energy distribution strategy, generate corresponding control commands to dynamically control the working state of each waveguide channel in the smart microwave oven; the working state includes the on / off state of each waveguide channel and the microwave power distribution ratio.

[0099] This embodiment utilizes a multimodal detection network to perceive the food to be heated inside a smart microwave oven in real time, acquiring its multimodal information. The multimodal detection network consists of multiple sensing modules. A preset cooking mode matching the multimodal information of the food is determined. Based on the target microwave energy characteristics indicated by the preset cooking mode, a microwave energy allocation strategy matching the food is generated. Then, based on the microwave energy allocation strategy, corresponding control commands are generated to dynamically control the operating state of each waveguide channel in the smart microwave oven. The operating state includes the on / off state of each waveguide channel and the microwave power allocation ratio. This solves the problem of not being able to achieve real-time dynamic control of microwave energy within the cavity, thus making it difficult to adapt to diverse cooking scenarios and food characteristics. It achieves real-time dynamic control of microwave energy within the cavity to adapt to diverse cooking scenarios and food characteristics.

[0100] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0101] This embodiment also provides a control device for an intelligent microwave oven, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. The terms "module," "unit," "subunit," etc., used below refer to combinations of software and / or hardware that implement a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0102] Figure 6 This is a structural block diagram of the control device for the intelligent microwave oven in this embodiment, as shown below. Figure 6 As shown, the device includes:

[0103] The acquisition module 10 is used to acquire multimodal information of the food to be heated inside the smart microwave oven in real time;

[0104] Analysis module 20 is used to determine a microwave energy distribution strategy that matches the food to be heated based on the multimodal information of the food to be heated;

[0105] The control module 30 is used to dynamically control the working status of each waveguide channel in the intelligent microwave oven based on the microwave energy distribution strategy.

[0106] The device provided in this embodiment acquires multimodal information of the food to be heated in the smart microwave oven in real time; based on the multimodal information of the food to be heated, a microwave energy distribution strategy matching the food to be heated is determined; based on the microwave energy distribution strategy, the working state of each waveguide channel in the smart microwave oven is dynamically controlled, solving the problem that it is impossible to achieve real-time dynamic control of microwave energy in the cavity, thus making it difficult to adapt to diverse cooking scenarios and food characteristics, and realizing real-time dynamic control of microwave energy in the cavity to adapt to diverse cooking scenarios and food characteristics.

[0107] In some embodiments, the acquisition module 10 is used to perceive the food to be heated in the smart microwave oven in real time based on a multimodal detection network, so as to obtain multimodal information of the food to be heated; the multimodal detection network consists of multiple sensing modules.

[0108] In some embodiments, the analysis module 20 is used to determine the shape coefficient corresponding to the shape features of the food to be heated; to analyze and calculate the shape features and shape coefficient of the food to be heated using a three-dimensional volume reconstruction algorithm to obtain the volume of the food to be heated; and to generate a microwave energy distribution strategy that matches the food to be heated based on the volume and multimodal information of the food to be heated.

[0109] In some embodiments, the analysis module 20 is used to determine a preset cooking mode that matches the multimodal information of the food to be heated; and to generate a microwave energy distribution strategy that matches the food to be heated based on the target microwave energy characteristics indicated by the preset cooking mode.

[0110] In some embodiments, the control module 30 is used to generate corresponding control commands based on the microwave energy distribution strategy to dynamically control the working state of each waveguide channel in the smart microwave oven; the working state includes the on / off state of each waveguide channel and the microwave power distribution ratio.

[0111] In some of these embodiments, Figure 6Based on this, the device also includes an unload determination module, which is used to detect the load of the heating placement area inside the smart microwave oven and obtain multiple target parameters. The target parameters include weight parameters, capacitance change and temperature gradient. When each target parameter meets the preset threshold condition, the smart microwave oven is determined to be in an unloaded state. The redundant channels in each waveguide channel are controlled to be turned off in the unloaded state.

[0112] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0113] This embodiment also provides a smart microwave oven, which is used to implement the above embodiments and preferred embodiments. Details that have already been described will not be repeated. Figure 7 This is a structural block diagram of the control device for the intelligent microwave oven in this embodiment, as shown below. Figure 7 As shown, the smart microwave oven includes a multimodal detection module 40, a controller 50, and an RF switch matrix 60; the RF switch matrix 60 is provided with multiple waveguide channels;

[0114] The multimodal detection module 40 is used to acquire multimodal information of the food to be heated inside the smart microwave oven in real time;

[0115] The controller 50 is used to determine a microwave energy distribution strategy that matches the food to be heated based on the multimodal information of the food to be heated.

[0116] The controller 50 is also used to dynamically control the working status of each waveguide channel in the smart microwave oven based on a microwave energy distribution strategy.

[0117] Specifically, the multimodal detection module 40 integrates multiple sensors, including infrared thermal imaging, capacitive sensing, and weighing, to collect multimodal information such as the position, shape, and mass of the food to be heated inside the cavity in real time. Based on the information acquired by the multimodal detection module 40, the controller 50 generates a microwave energy distribution strategy that matches the food to be heated in real time using a built-in adaptive algorithm or a pre-set strategy library. The controller 50 can employ a central controller based on an ARM architecture, updating the control strategy every 50ms; no specific limitations are specified here.

[0118] Furthermore, the controller 50 also converts this strategy into specific control commands, dynamically adjusting the on / off state and power distribution ratio of each waveguide channel in the RF switch matrix 60, thereby achieving high-precision and reconfigurable control of microwave energy and significantly improving heating uniformity and energy efficiency.

[0119] The intelligent microwave oven provided in this embodiment acquires multimodal information of the food to be heated inside the microwave oven in real time; based on the multimodal information of the food to be heated, a microwave energy distribution strategy matching the food to be heated is determined; based on the microwave energy distribution strategy, the working state of each waveguide channel in the intelligent microwave oven is dynamically controlled, solving the problem that it is impossible to achieve real-time dynamic control of microwave energy in the cavity, thus making it difficult to adapt to diverse cooking scenarios and food characteristics, and realizing real-time dynamic control of microwave energy in the cavity to adapt to diverse cooking scenarios and food characteristics.

[0120] In some embodiments, the RF switch matrix 60 also includes a switching unit for controlling the on / off state of each waveguide channel.

[0121] Specifically, the control of multiple waveguide channels is typically achieved by an RF switch matrix 60, enabling dynamic reconfiguration of microwave paths through a programmable waveguide matrix. For example, a 4×4 RF MEMS switch matrix can be used, employing gold-electrode silicon nitride dielectric MEMS switches (3×3×0.5mm in size). This matrix supports 30V electrostatic drive, has a switching time of approximately 300μs, an insertion loss of less than 0.3dB, and can flexibly configure 16 waveguide path combinations, achieving a field intensity modulation range of 0-100%.

[0122] This embodiment also provides a computer device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0123] Optionally, the computer device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0124] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0125] S1, real-time acquisition of multimodal information of food to be heated inside the smart microwave oven;

[0126] S2, Based on the multimodal information of the food to be heated, determine the microwave energy distribution strategy that matches the food to be heated;

[0127] S3, based on a microwave energy distribution strategy, dynamically controls the working status of each waveguide channel in the smart microwave oven.

[0128] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.

[0129] Furthermore, in conjunction with the intelligent microwave oven control method provided in the above embodiments, this embodiment can also provide a storage medium for implementation. This storage medium stores a computer program; when executed by a processor, the computer program implements any of the intelligent microwave oven control methods described in the above embodiments.

[0130] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0131] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0132] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0133] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. 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 scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A control method for an intelligent microwave oven, characterized in that, include: Real-time acquisition of multimodal information of the food to be heated inside the smart microwave oven; Based on the multimodal information of the food to be heated, a microwave energy distribution strategy matching the food to be heated is determined; Based on the microwave energy distribution strategy, the working state of each waveguide channel in the intelligent microwave oven is dynamically controlled.

2. The control method for an intelligent microwave oven according to claim 1, characterized in that, The real-time acquisition of multimodal information of the food to be heated inside the smart microwave oven includes: Based on a multimodal detection network, the food to be heated inside the smart microwave oven is sensed in real time to obtain the multimodal information of the food to be heated; the multimodal detection network consists of multiple sensing modules.

3. The control method for an intelligent microwave oven according to claim 1, characterized in that, The multimodal information includes the shape features of the food to be heated; determining a microwave energy distribution strategy matching the food to be heated based on the multimodal information of the food to be heated includes: Determine the shape coefficient corresponding to the shape characteristics of the food to be heated; The shape features and shape coefficients of the food to be heated are analyzed and calculated using a three-dimensional volume reconstruction algorithm to obtain the volume of the food to be heated. Based on the volume of the food to be heated and the multimodal information, a microwave energy distribution strategy matching the food to be heated is generated.

4. The control method for an intelligent microwave oven according to claim 1, characterized in that, The step of determining a microwave energy distribution strategy matching the food to be heated based on the multimodal information of the food to be heated includes: Determine a preset cooking mode that matches the multimodal information of the food to be heated; Based on the target microwave energy characteristics indicated by the preset cooking mode, a microwave energy distribution strategy matching the food to be heated is generated.

5. The control method for an intelligent microwave oven according to claim 1, characterized in that, The dynamic control of the operating state of each waveguide channel in the intelligent microwave oven based on the microwave energy distribution strategy includes: Based on the microwave energy distribution strategy, corresponding control commands are generated to dynamically control the working state of each waveguide channel in the intelligent microwave oven; the working state includes the on / off state of each waveguide channel and the microwave power distribution ratio.

6. The control method for an intelligent microwave oven according to claim 1, characterized in that, Before acquiring the multimodal information of the food to be heated inside the smart microwave oven in real time, the method further includes: The load on the heating area inside the smart microwave oven is detected to obtain multiple target parameters, including weight parameters, capacitance change, and temperature gradient. When each of the target parameters meets the preset threshold conditions, the smart microwave oven is determined to be in an unloaded state. The redundant channels in each waveguide channel are shut down under the no-load state.

7. A smart microwave oven, characterized in that, The intelligent microwave oven includes a multimodal detection module, a controller, and an RF switch matrix; the RF switch matrix has multiple waveguide channels; The multimodal detection module is used to acquire multimodal information of the food to be heated inside the smart microwave oven in real time; The controller is used to determine a microwave energy distribution strategy that matches the food to be heated based on the multimodal information of the food to be heated. The controller is also used to dynamically control the working state of each waveguide channel in the smart microwave oven based on the microwave energy distribution strategy.

8. The intelligent microwave oven according to claim 7, characterized in that, The radio frequency switch matrix also includes a switch unit for controlling the on / off state of each waveguide channel.

9. A computer device, comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the steps of the control method for the intelligent microwave oven according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the control method for the intelligent microwave oven as described in any one of claims 1 to 6.

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