Control method, system and equipment of range hood

Abstract

The invention relates to a control method, system and equipment of a range hood. The control method comprises the steps that a thermal imaging video of a stove area is collected; performing data processing on the thermal imaging video to obtain temperature data; based on the temperature data, generating a temperature distribution diagram of the kitchen range area, and performing hot spot area division on the temperature distribution diagram; and determining the operation gear of the range hood according to the temperature data of each hot spot area. According to the range hood, all-around temperature monitoring of the kitchen range area is achieved, the accuracy and precision of temperature data are improved, the operation gear of the range hood is determined according to the high-precision temperature distribution diagram, the operation gear of the range hood is adjusted in real time, and the operation efficiency and the energy-saving effect of the range hood are improved.

Description

Technical Field

[0001] This application relates to the technical field of range hoods, and particularly to a control method, system and device for a range hood. Background Art

[0002] As an important kitchen appliance, the main function of a range hood is to effectively remove the oil fumes and steam generated during cooking to keep the kitchen air fresh. Existing range hoods generally use a single temperature sensor to monitor the temperature in the cooking area and adjust the operating gear of the range hood accordingly. Although automatic control is achieved to a certain extent, there are still problems such as limited temperature detection range and inability to adapt to environmental changes. Summary of the Invention

[0003] In order to solve the problems in the prior art that the temperature detection range of the range hood is limited, and the control strategy of the range hood is not flexible enough due to the inability to adapt to environmental changes, making it difficult to adapt to different cooking environments and requirements.

[0004] This application provides a control method for a range hood, and the control method includes:

[0005] Collect the thermal imaging video of the cooking area;

[0006] Perform data processing on the thermal imaging video to obtain temperature data;

[0007] Based on the temperature data, generate a temperature distribution map of the cooking area, and divide the hot spot areas of the temperature distribution map;

[0008] Determine the operating gear of the range hood according to the temperature data of each hot spot area.

[0009] Further, the thermal imaging video includes multiple consecutive thermal imaging images.

[0010] Further, the performing data processing on the thermal imaging video to obtain temperature data includes:

[0011] Perform denoising and temperature calibration processing on the thermal imaging image to obtain the temperature data.

[0012] Further, the control method further includes:

[0013] Adjust the operating gear of the range hood according to the difference between the temperature data of each hot spot area and a preset temperature threshold.

[0014] Further, the control method further includes:

[0015] Collect the environmental data in the kitchen, and the environmental data at least includes the humidity data, air flow velocity in the kitchen and the real-time temperature data of the cooking area;

[0016] Perform data processing on the environmental data and extract characteristic parameters;

[0017] Classify the environmental state based on the characteristic parameters;

[0018] Adjust the operating gear of the range hood according to the classification result of the environmental state and the real-time temperature data.

[0019] Furthermore, the performing data processing on the environmental data includes:

[0020] Perform noise filtering, smoothing processing and normalization processing on the environmental data.

[0021] Furthermore, the classifying the environmental state based on the characteristic parameters includes:

[0022] Construct an environmental perception model using the characteristic parameters to identify the current environmental state;

[0023] Classify the environmental state using a support vector machine or a K-nearest neighbor algorithm.

[0024] This application also provides a control system for a range hood, including:

[0025] A dynamic thermal imaging module for collecting a thermal imaging video of the cooking appliance area;

[0026] An image processing module for performing data processing on the thermal imaging video to obtain temperature data;

[0027] A first data analysis module for generating a temperature distribution map of the cooking appliance area based on the temperature data and dividing a hot spot area on the temperature distribution map;

[0028] An operation strategy determination module for determining the operating gear of the range hood according to the temperature data of each hot spot area.

[0029] Furthermore, it further includes an environmental detection module for collecting environmental data in the kitchen, where the environmental data at least includes humidity data, air flow velocity in the kitchen and real-time temperature data of the cooking appliance area;

[0030] An environmental data processing module for performing data processing on the environmental data and extracting characteristic parameters;

[0031] A second data analysis module for classifying the environmental state based on the characteristic parameters;

[0032] A second control module for adjusting the operating gear of the range hood according to the classification result of the environmental state and the real-time temperature data.

[0033] The present application also provides a range hood device, which includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the control method of the range hood as described in any one of claims 1 to 7.

[0034] Implementing the embodiments of the present application has the following beneficial effects:

[0035] The control method of the range hood in the present application realizes the omnidirectional temperature monitoring of the cooking area by collecting the thermal imaging video of the cooking area and combining image processing and data analysis technologies, improves the accuracy and precision of temperature data, determines the operation gear of the range hood according to the high-precision temperature distribution map, and adjusts the operation gear of the range hood in real time, thereby improving the operation efficiency and energy-saving effect of the range hood.

[0036] By further integrating an environment detection module, an environment perception algorithm and an adaptive control algorithm, the comprehensive monitoring and intelligent response of the kitchen environment are realized, and the performance and user experience of the range hood are significantly improved. The environment detection module collects multi-dimensional environment data in the kitchen in real time and accurately. Based on these data, the environment perception algorithm intelligently identifies the environment state of the kitchen, so as to ensure that the range hood can make dynamic adjustments for different cooking scenarios, greatly enhancing its adaptability to complex environments. Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0038] Figure 1 is a flowchart of a control method for a range hood according to an embodiment of the present application;

[0039] Figure 2 is a flowchart of adjusting the range hood gear according to temperature data in an embodiment of the present application;

[0040] Figure 3 is a flowchart of adjusting the range hood gear according to the kitchen environment in an embodiment of the present application;

[0041] Figure 4 is a flowchart of classifying the environment state in an embodiment of the present application;

[0042] Figure 5It is a hardware structure block diagram of a server for a control method of a range hood according to an embodiment of the present application. Detailed implementation manners

[0043] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without making creative efforts belong to the scope of protection of the present application.

[0044] In the description of the present application, it should be understood that the orientation or positional relationship indicated by terms such as "upper, lower, inner, outer, top, bottom" and the like is based on the orientation or positional relationship shown in the drawings, or is in the vertical, perpendicular or gravitational direction of the component itself. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation to the present application. The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features.

[0045] Unless otherwise defined, the technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field of the present application. The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present application. Terms such as "part" and "component" as used herein may represent a single part or a combination of multiple parts. Terms such as "installation", "setting", and "connection" as used herein should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may mean that a component is directly attached to another component, or it may mean that a component is attached to another component through an intermediate member, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances. The features described in one embodiment herein can be applied alone or in combination with other features in another embodiment, unless the feature is not applicable or otherwise stated in the other embodiment.

[0046] The following in combination with Figures 1 - 5The present invention introduces a control method for a range hood provided in an embodiment of the present application. This specification provides method operation steps such as the embodiments or flow charts, but more or fewer operation steps may be included based on routine or non-creative work. The order of steps listed in the embodiments is only one way of executing the steps among many, and does not represent the only execution order. When the preparation method is actually executed, it can be executed in the order shown in the embodiments or the drawings or in parallel. The control method may include the following steps:

[0047] S100 collects thermal imaging video of the stove area.

[0048] Specifically, the thermal imaging video includes multiple frames of continuous thermal imaging images. This embodiment uses a dynamic thermal imaging module to capture thermal imaging videos of the stove area. The dynamic thermal imaging module can be a thermal imaging sensor typically located above the stove, on the side, and at a certain angle to ensure that it can capture all-round temperature changes in the stove area and effectively identify the distribution of heat.

[0049] Assuming that the resolution of the thermal imaging sensor is m×n, the temperature data of each frame of thermal imaging image is represented as a matrix T_{image}(i,j), where i and j are the rows and columns of the image respectively.

[0050] S200: Process the thermal imaging video to obtain temperature data, including performing denoising and temperature calibration on the thermal imaging image to obtain the temperature data.

[0051] Specifically, the Gaussian filtering algorithm is used to remove noise from the thermal imaging image, and the filtered temperature data matrix is represented as T_{filtered}(i,j).

[0052]

[0053] Where σ is the standard deviation of the Gaussian filter, and K and L are the sizes of the filter window.

[0054] Temperature calibration involves calibrating the filtered temperature data to eliminate the effects of ambient temperature and sensor deviation to obtain accurate temperature data. Assuming the calibration parameters are a and b, the calibrated temperature data is T calibrated (i,j).

[0055] T calibrated (i, j) = a·T filtered (i,j)+b

[0056] In some possible embodiments, preprocessing the data of the thermal imaging video further includes processing for enhancing the contrast of the thermal imaging image. Contrast is the brightness difference between different regions in an image. In a thermal imaging image, enhancing the contrast can make the boundaries between regions of different temperatures clearer, which helps to more accurately identify and analyze the temperature distribution.

[0057] S300. Based on the temperature data, generate a temperature distribution map of the stove area and divide the hot spot areas in the temperature distribution map.

[0058] Specifically, according to the calibrated temperature data matrix T calibrated (i, j), generate a temperature distribution map of the stove area. The temperature distribution map is represented as a two-dimensional temperature matrix T map (i, j).

[0059] Then calculate the temperature gradient, calculate the temperature gradient in the temperature distribution map, and identify the regions with large temperature changes. The temperature gradient is represented as Obtained by calculating the partial derivatives of the temperature matrix.

[0060]

[0061] where Δx and Δy are the pixel spacings of the image in the x and y directions respectively.

[0062] Perform hot spot area detection, detect hot spot areas according to the temperature gradient and temperature threshold, and identify the regions with higher temperatures. These regions can be used as important monitoring grids. Assume the temperature threshold is T th , and the hot spot area satisfies T map (i, j)>T th .

[0063] Divide the hot spot areas in the temperature distribution map. Divide the temperature distribution map into multiple regions. Assume the temperature distribution map is divided into a p×q grid, and the temperature of each grid is T grid (k, l). Determine the operating gear of the range hood according to the temperature data of each hot spot area.

[0064] The basis for dividing the temperature distribution map into multiple hot spot areas in the embodiments of the present application includes the temperature change amplitude, heat concentration degree and actual usage in the hot spot area, such as the heat demand at different positions during cooking. Calculating the temperature gradient in the temperature distribution map and identifying the regions with large temperature changes aims to discover potential hot spots and overheated areas. The identification basis includes the temperature change rate, temperature difference and temperature outliers in the region. By identifying these hot spots, cooling or adjustment strategies can be taken in advance to optimize thermal management, ensure the safety of the equipment, and improve the overall performance, avoiding equipment damage or safety hazards caused by overheating.

[0065] S400. Determine the operating mode of the range hood according to the temperature data of each hot spot area.

[0066] Specifically, it can be divided according to the average temperature, the highest temperature and the temperature fluctuation of each hot spot area to identify the heat-concentrated area and the relatively cooled area, so as to formulate an appropriate operating strategy for the range hood and determine the operating mode of the range hood. In some possible implementation manners, when the average temperature of all hot spot areas is lower than the preset average temperature value and the highest temperature of no hot spot area exceeds the highest temperature threshold, the range hood operates at a low mode, maintaining the basic oil fume exhaust capacity while saving energy; when the average temperature of a certain or some hot spot areas is higher than the preset average temperature value, but the highest temperature of all hot spot areas does not exceed the highest temperature threshold, the range hood operates at a medium mode to enhance the oil fume exhaust capacity to adapt to moderate cooking activities; when the highest temperature of a certain hot spot area exceeds the highest temperature threshold, the range hood immediately switches to the high mode to quickly exhaust the oil fume and heat with the maximum exhaust air volume, ensuring the freshness of the kitchen air and protecting the health of the cook at the same time.

[0067] In some possible implementation manners, the range hood can dynamically adjust its operating mode according to the real-time changes of each hot spot area in the temperature distribution map. When the average temperature of a certain hot spot area suddenly rises, the range hood can quickly increase the suction force to cope with the increased oil fume generation.

[0068] Furthermore, the control method further includes:

[0069] S500. Adjust the operating mode of the range hood according to the difference between the temperature data of each hot spot area and the preset temperature threshold.

[0070] Specifically, set the preset temperature threshold T set , and adjust the operating mode of the range hood according to the deviation between the temperature data of each hot spot area and the preset temperature threshold, that is, adjust the operating mode of the range hood according to the real-time changes of the temperature data of each hot spot area. The control module of the range hood in this embodiment receives the temperature data of each hot spot area in real time and adjusts the operating mode of the range hood according to the real-time changes of the temperature data of each hot spot area to ensure that its operating state matches the actual cooking environment.

[0071] The control signal U(t) is determined by the maximum value T max =max(T grid (k, l)).

[0072] U(t)=f(T max , T set )

[0073] Among them, the function f reflects the relationship between the operating gear of the range hood and the temperature deviation.

[0074] The control method of the range hood in the embodiment of the present application realizes the all-round temperature monitoring of the cooking area by collecting the thermal imaging video of the cooking area and combining image processing and data analysis technologies, improves the accuracy and precision of temperature data, determines the operating gear of the range hood according to the high-precision temperature distribution map, and adjusts the operating gear of the range hood in real time, thereby improving the operating efficiency and energy-saving effect of the range hood.

[0075] Further, the control method further includes:

[0076] S600, collecting environmental data in the kitchen, where the environmental data at least includes humidity data, air flow velocity in the kitchen, and real-time temperature data of the cooking area;

[0077] S610, performing data processing on the environmental data and extracting characteristic parameters;

[0078] S620, classifying the environmental state based on the characteristic parameters;

[0079] S630, adjusting the operating gear of the range hood according to the classification result of the environmental state and the real-time temperature data.

[0080] Specifically, the environmental data in the kitchen is collected by an environmental detection module to monitor the environmental conditions around the cooker in real time, and accordingly, the operating state of the range hood is flexibly adjusted. In some possible implementation manners, the environmental detection module includes: a temperature sensor for collecting real-time temperature data of the cooking area; a humidity sensor for collecting humidity data of the kitchen environment; and an air flow sensor for measuring the air flow velocity in the kitchen.

[0081] The steps of collecting environmental data in the embodiment of the present application include:

[0082] Temperature data collection: Collecting real-time temperature data T(t) of the cooking area through a temperature sensor, with the unit of degree Celsius (°C).

[0083] Humidity data collection: Collecting humidity data H(t) of the kitchen environment through a humidity sensor, with the unit of percentage (%).

[0084] Air flow data collection: Measuring the air flow velocity V(t) in the kitchen through an air flow sensor, with the unit of meter per second (m / s).

[0085] S610, performing data processing on the environmental data and extracting characteristic parameters includes:

[0086] Performing noise filtering, smoothing processing, and normalization processing on the environmental data.

[0087] Data normalization formula:

[0088]

[0089] Wherein, T min , T max , H min , H max , V min , V max are the minimum and maximum values of temperature, humidity and air flow velocity respectively.

[0090] Extract feature parameters from the processed environmental data to form a feature vector X(t):

[0091] X(t) = [T s (t), H(t), V(t), S(t)]

[0092] S620. Based on the above feature parameters, the environmental state classification includes:

[0093] S621. Use the feature parameters to construct an environmental perception model to identify the current environmental state.

[0094] Specifically, the environmental state vector E(t) = [T norm (t), H norm (t), V norm (t)].

[0095] S622. Use the support vector machine or K-nearest neighbor algorithm to classify the environmental state to identify different cooking scenarios.

[0096] Specifically, the steps of using the K-nearest neighbor algorithm to classify the environmental state include:

[0097] 1) Define the K-nearest neighbor algorithm:

[0098] For each test sample, find its K nearest neighbors in the training set according to a certain distance metric (such as Euclidean distance).

[0099] Determine the class of the test sample by majority voting according to the classes of these K neighbors.

[0100] 2) Training data set and test data set:

[0101] Let the training data set be where X i is the feature vector and y i is the corresponding label (environmental state class).

[0102] 3) Euclidean distance calculation:

[0103] For the test sample X(t), calculate the Euclidean distance between it and the training sample X i :

[0104]

[0105] where M is the dimension of the feature vector.

[0106] 4) Select the K nearest neighbors:

[0107] According to the distances d(X(t), X i ), sort them from smallest to largest and select the K nearest neighbors.

[0108] 5) Classification decision:

[0109] Vote on these K neighbors and select the class with the highest frequency as the class of the test sample.

[0110] The steps for classifying the environmental state using a support vector machine include:

[0111] 1) Define the support vector machine:

[0112] A support vector machine is a supervised learning model mainly used for binary classification tasks. It constructs an optimal hyperplane in a high-dimensional space to maximize the classification margin.

[0113] 2) Training dataset and test dataset:

[0114] Let the training dataset be where X i is the feature vector and y i is the corresponding label (environmental state class).

[0115] 3) Construct the optimal hyperplane:

[0116] The goal of the support vector machine is to find a hyperplane that can maximize the margin between the two types of sample points and the hyperplane.

[0117] The equation of the hyperplane is: w·X + b = 0

[0118] where w is the weight vector and b is the bias.

[0119] 4) Optimization goal:

[0120]

[0121] subject to y i (w·X i + b) ≥ 1, i = 1, …, N

[0122] 5) Classification decision:

[0123] For the test sample X(t), it is classified through the decision function f(X(t)) = w·X(t) + b. If f(X(t)) > 0, the sample belongs to the positive class; otherwise, it belongs to the negative class.

[0124] S630. Adjusting the operating gear of the range hood according to the classification result of the environmental state and the real-time temperature data includes:

[0125] Determine the current environmental state according to the classification result C(t) of the support vector machine or the K-nearest neighbor algorithm.

[0126] Adjust the operating gear U(t) of the range hood according to the environmental state C(t).

[0127]

[0128] The environmental classification model Model takes the environmental state vector E(t) as input and outputs the environmental category C(t).

[0129] C(t) = Model(E(t))

[0130] Adjust the operating gear U(t) of the range hood according to the classification result of the environmental state and the real-time temperature data collected by the temperature sensor in the cooking area to ensure efficient smoke exhaust and energy conservation.

[0131] Specifically, according to the environmental category C(t) and the real-time temperature data T(t), use the adaptive control algorithm to calculate the optimal operating gear.

[0132] Furthermore, the control method of the range hood in the embodiment of the present application can also combine the adaptive control algorithm, quickly respond and adjust the operating gear of the range hood according to the real-time environmental state and temperature data, so as to ensure efficient smoke exhaust and energy conservation effects. The steps of the adaptive control algorithm include:

[0133] Fuzzy controller design: Design a fuzzy controller to calculate the control output according to the temperature deviation e(t) and the temperature change rate Δe(t).

[0134] e(t) = T set -T(t)

[0135]

[0136] Define fuzzy rules to adjust the range hood gear in combination with the temperature deviation and change rate.

[0137] Calculate the output control signal \(U(t)\) through the fuzzy inference system (FIS).

[0138] U(t) = FIS(e(t), Δe(t))

[0139] PID controller adjustment:

[0140] Based on fuzzy control, a PID controller is introduced for fine adjustment.

[0141] PID controller formula:

[0142]

[0143] The final control signal is a weighted combination of the fuzzy controller and the PID controller.

[0144] U(t) = αU FIS (t) + βU PID (t)

[0145] where α and β are weight coefficients, satisfying α + β = 1.

[0146] The fuzzy controller uses fuzzy rules and an inference system to effectively handle uncertainties and non-linear factors in the kitchen environment; while the PID controller achieves fine adjustment of the operating state of the range hood through an accurate mathematical model. This combination enables the control system to respond quickly and accurately to various changes in the kitchen, thus achieving significant energy-saving effects while ensuring efficient smoke exhaust.

[0147] The range hood control method proposed in the embodiments of this application, on the basis of achieving all-round temperature monitoring of the cooking area by collecting thermal imaging videos of the cooking area and combining image processing and data analysis techniques, and determining the operating gear of the range hood according to the temperature data of each hot spot area, further integrates an environment detection module, an environment perception algorithm, and an adaptive control algorithm to achieve comprehensive monitoring and intelligent response to the kitchen environment, significantly improving the performance and user experience of the range hood. Through the environment detection module, multi-dimensional environmental data in the kitchen is collected in real time and accurately. Based on these data, the environment perception algorithm intelligently identifies the environmental state of the kitchen, thus ensuring that the range hood can make dynamic adjustments for different cooking scenarios, greatly enhancing its adaptability to complex environments. Through the adaptive control algorithm that combines fuzzy control and PID control, according to real-time environmental data and environmental states, the operating gear of the range hood is accurately calculated and adjusted to quickly respond to environmental changes and ensure the best operating state.

[0148] A control system for a range hood according to an embodiment of this application includes:

[0149] A dynamic thermal imaging module for collecting thermal imaging videos of the cooking area.

[0150] In this embodiment, a dynamic thermal imaging module is used to capture the thermal imaging video of the cooking range area. The dynamic thermal imaging module can be a thermal imaging sensor, which is usually set on the upper side and at a certain oblique angle of the cooking range to ensure that the omnidirectional temperature changes in the cooking range area can be captured, and the heat distribution can be effectively identified. The main difference between the thermal imaging sensor and the temperature sensor is that the thermal imaging sensor generates a temperature distribution map by detecting infrared radiation and provides temperature change information for the entire area, while the temperature sensor directly measures specific points and can provide accurate real-time data.

[0151] An image processing module, which is used to process the data of the thermal imaging video to obtain temperature data. It includes processing the thermal imaging image for denoising and temperature calibration to obtain the temperature data.

[0152] A first data analysis module, which is used to generate the temperature distribution map of the cooking range area based on the temperature data and divide the hot spot areas of the temperature distribution map.

[0153] An operation strategy determination module, which is used to determine the operation gear of the range hood according to the temperature data of the hot spot area.

[0154] Furthermore, it further includes an operation gear adjustment module, which is used to adjust the operation gear of the range hood according to the difference between the temperature data of each hot spot area and the preset temperature threshold.

[0155] Furthermore, it further includes an environment detection module, which is used to collect the environment data in the kitchen. The environment data at least includes the humidity data, air flow velocity in the kitchen and the real-time temperature data of the cooking range area;

[0156] An environment data processing module, which is used to process the environment data and extract characteristic parameters;

[0157] A second data analysis module, which is used to classify the environment state based on the characteristic parameters;

[0158] A second control module, which is used to adjust the operation gear of the range hood according to the classification result of the environment state and the real-time temperature data.

[0159] In the embodiment of the present application, the dynamic thermal imaging module and the environment detection module are used simultaneously. The dynamic thermal imaging module provides visual data of the overall temperature distribution to quickly identify the heat distribution and concentration; while the temperature sensor of the environment detection module provides high-precision real-time temperature measurement. The combination of the two can realize the comprehensive monitoring of the kitchen environment, thereby more effectively improving the intelligent control and response ability of the range hood.

[0160] An embodiment of the present application further provides an oil fume suction device, which includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the control method of the oil fume suction device.

[0161] The memory can be used to store software programs and modules. By running the software programs and modules stored in the memory, the processor can execute various functional applications and data processing. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for functions, etc.; the data storage area can store data created according to the use of the device, etc. In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices. Correspondingly, the memory can also include a memory controller to provide the processor with access to the memory.

[0162] The method embodiment provided by the embodiment of the present application can be executed on a mobile terminal, a computer terminal, a server, or a similar computing device. Taking running on a server as an example, Figure 5 is a hardware structure block diagram of a server for a control method of an oil fume suction device provided by an embodiment of the present application. As Figure 5 shown, the server 100 may vary greatly due to configuration or performance differences, and may include one or more central processing units (CPUs) 110 (the processor 110 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 130 for storing data, and one or more storage media 120 (such as one or more mass storage devices) for storing application programs 123 or data 122. Among them, the memory 130 and the storage media 120 can be transient storage or persistent storage. The program stored in the storage media 120 may include one or more modules, and each module may include a series of instruction operations on the server. Further, the central processor 110 can be set to communicate with the storage media 120 and execute a series of instruction operations in the storage media 120 on the server 100. The server 100 may also include one or more power supplies 160, one or more wired or wireless network interfaces 150, one or more input / output interfaces 140, and / or one or more operating systems 121, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and so on.

[0163] The input / output interface 140 can be used to receive or transmit data via a network. Specific examples of the above-mentioned network can include a wireless network provided by a communication provider of the server 100. In one example, the input / output interface 140 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one example, the input / output interface 140 can be a Radio Frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0164] Those of ordinary skill in the art can understand that Figure 5 The structure shown is only schematic and does not limit the structure of the above-mentioned electronic device. For example, the server 100 may further include more or fewer components than those shown in Figure 5 or have a different configuration from that shown in Figure 5 shown.

[0165] An embodiment of the present application further provides a storage medium, in which at least one instruction or at least one program segment is stored, and the at least one instruction or the at least one program segment is loaded and executed by a processor to implement the control method of the range hood.

[0166] Optionally, in this embodiment, the above storage medium can be located in at least one of multiple network servers in a computer network. Optionally, in this embodiment, the above storage medium can include, but is not limited to: various media that can store program codes such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0167] According to one aspect of the present application, there is provided a computer program product or a computer program, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above various optional implementation manners.

[0168] The control method, system and device of the range hood in this application realize all-round temperature monitoring of the cooking area by collecting thermal imaging videos of the cooking area and combining image processing and data analysis technologies. On the basis of determining the operation gear of the range hood according to the temperature data of each hot spot area, the environmental detection module, environmental perception algorithm and adaptive control algorithm are further integrated to realize comprehensive monitoring and intelligent response to the kitchen environment, significantly improving the performance and user experience of the range hood. The environmental detection module collects multi-dimensional environmental data in the kitchen in real time and accurately. Based on these data, the environmental perception algorithm intelligently identifies the environmental state of the kitchen, so as to ensure that the range hood can make dynamic adjustments for different cooking scenarios, greatly enhancing its adaptability to complex environments.

[0169] Obviously, the above-described embodiments are only a part of the embodiments of this specification, rather than all of them. Based on the embodiments in this specification, those of ordinary skill in the art can make other different forms of changes or modifications without creative efforts, and all of them should belong to the scope protected by this specification.

[0170] After considering the specification and practicing the disclosed embodiments herein, those skilled in the art will readily conceive of other implementations of the embodiments of this specification. This specification is intended to cover any variations, uses, or adaptations of the embodiments of this specification, which follow the general principles of the embodiments of this specification and include well-known common general knowledge or conventional technical means in the technical field not disclosed in this specification. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the embodiments of this specification are pointed out by the following claims.

[0171] It should be understood that the embodiments of this specification are not limited to the precise structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the embodiments of this specification is only limited by the appended claims.

Claims

1. A control method for a range hood, characterized in that, The control method includes: Collecting a thermal imaging video of the cooking range area; Performing data processing on the thermal imaging video to obtain temperature data; Generating a temperature distribution map of the cooking range area based on the temperature data, and dividing hot spot areas on the temperature distribution map; Determining the operating gear of the range hood according to the temperature data of each hot spot area.

2. The control method of a range hood according to claim 1, characterized in that The thermal imaging video includes multiple consecutive thermal imaging images.

3. The control method of a range hood according to claim 2, characterized in that, The performing data processing on the thermal imaging video to obtain temperature data includes: Performing denoising and temperature calibration processing on the thermal imaging image to obtain the temperature data.

4. The control method of a range hood according to claim 1, characterized in that, The control method further includes: Adjusting the operating gear of the range hood according to the difference between the temperature data of each hot spot area and a preset temperature threshold.

5. The control method of a range hood according to claim 1, characterized in that, The control method further includes: Collecting environmental data in the kitchen, where the environmental data at least includes humidity data, air flow velocity in the kitchen, and real-time temperature data of the cooking range area; Performing data processing on the environmental data and extracting characteristic parameters; Classifying the environmental state based on the characteristic parameters; Adjusting the operating gear of the range hood according to the classification result of the environmental state and the real-time temperature data.

6. The control method of a range hood according to claim 5, wherein, The performing data processing on the environmental data includes: Performing noise filtering, smoothing processing, and normalization processing on the environmental data.

7. The control method of a range hood according to claim 5, characterized in that, The classifying the environmental state based on the characteristic parameters includes: Constructing an environmental perception model using the characteristic parameters to identify the current environmental state; Classifying the environmental state using a support vector machine or a K-nearest neighbor algorithm.

8. A control system for a range hood, characterized in that, Includes: A dynamic thermal imaging module for collecting a thermal imaging video of the cooking range area; An image processing module for performing data processing on the thermal imaging video to obtain temperature data; A first data analysis module for generating a temperature distribution map of the cooking range area based on the temperature data, and dividing hot spot areas on the temperature distribution map; An operating strategy determination module for determining the operating gear of the range hood according to the temperature data of each hot spot area.

9. The control system of a range hood according to claim 8, characterized in that, It further includes an environmental detection module for collecting environmental data in the kitchen, where the environmental data at least includes humidity data, air flow velocity in the kitchen, and real-time temperature data of the cooking range area; An environmental data processing module for performing data processing on the environmental data and extracting characteristic parameters; A second data analysis module for classifying the environmental state based on the characteristic parameters; A second control module for adjusting the operating gear of the range hood according to the classification result of the environmental state and the real-time temperature data.

10. An oil fume suction machine device, characterized in that, The range hood is provided with a processor and a memory, and at least one instruction, at least one program, a code set, or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the control method of the range hood according to any one of claims 1 to 7.

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