CN210267369U - Intelligent microwave oven capable of being visually operated - Google Patents
Intelligent microwave oven capable of being visually operated Download PDFInfo
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- CN210267369U CN210267369U CN201920186184.3U CN201920186184U CN210267369U CN 210267369 U CN210267369 U CN 210267369U CN 201920186184 U CN201920186184 U CN 201920186184U CN 210267369 U CN210267369 U CN 210267369U
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Abstract
The utility model relates to a micro-heating wave field is a visual operation's intelligent microwave oven, has solved among the prior art microwave heating process single, can't be to the problem of multiple heated object pertinence heating. The utility model comprises a microwave source, an antenna device for adjusting the microwave radiation direction, an infrared temperature thermal image feedback device for feeding back the temperature in the heating cavity, an image module for imaging and displaying the heated object and an intelligent control module for controlling the microwave radiation direction and intensity; the intelligent control module is connected with the infrared temperature thermal image feedback device and the microwave source; the antenna device is connected with a microwave source through a coaxial cable. The utility model discloses a but set up real-time feedback heated cavity temperature distribution, through the phase place and the power output of controlling a plurality of microwave sources, change phased array beam radiation direction and radiant intensity, it is high to reach purpose, heating efficiency and the homogeneity of each subregion pertinence.
Description
Technical Field
The utility model relates to a microwave heating ripples field especially indicates a visual operation's intelligent microwave oven.
Background
With the rapid development of modern science and technology, microwave energy is widely applied to various fields such as industrial production, daily life and the like as a novel high-efficiency clean energy.
People also reflect the needs of convenient life on microwave heating, and microwave heating generally focuses on the aspects of heating efficiency, heating uniformity, portability and the like in the prior art, and different substances placed into a microwave heating cavity at the same time cannot be subjected to targeted heating during heating.
There is a need for a new type of intelligent microwave oven that can be targeted for heating that can solve the above problems.
SUMMERY OF THE UTILITY MODEL
The utility model provides an intelligence microwave oven of visual operation has solved among the prior art microwave heating process single, can't be to the problem of multiple specific heating of heated thing.
The technical scheme of the utility model is realized like this: an intelligent microwave oven with visual operation comprises a microwave source, an antenna device for adjusting the microwave radiation direction, an infrared temperature thermal image feedback device for feeding back the temperature in a heating cavity, an image module for imaging and displaying a heated object and an intelligent control module for controlling the microwave radiation direction and intensity; the intelligent control module is connected with the infrared temperature thermal image feedback device, the image module and the microwave source; the antenna device is connected with a microwave source through a coaxial cable.
The image module comprises an imaging unit and an image display device, the image display device comprises an interface operating system, and the interface operating system is connected with the intelligent control module.
The microwave source is a solid state source with more than six paths of output and controllable phase and power.
The intelligent control module comprises: the storage unit is used for storing the trained deep neural network data, the corresponding operation interface and the heating unit; the heating unit controls heating time, microwave source power and phased array phase output; and the temperature detection unit is used for collecting infrared temperature thermal image feedback data in real time.
The antenna device is a 4 x 4 patch antenna array capable of forming a phased array beam; the patch antenna array is in a frequency range of 2.41 GHz-2.49 GHz, and S11< -10 dB.
A heating method of an intelligent microwave oven visually operated comprises the following steps:
a, feeding a heating cavity by using a 16-unit antenna array as a feed source, dividing a heating space into four areas, performing simulation acquisition on data about phases of the 16 antenna units and heating temperature effects corresponding to the four areas, establishing a corresponding relation between directivity and a multi-source phase value by using an FEM algorithm and a phased array theory, corresponding to an interface operating system, and storing a result in a storage unit of an intelligent control module;
b, respectively heating the four areas by giving a short time and power, and acquiring real-time temperature data; by analyzing the acquired data and the interface operating system setting data, the heating unit of the intelligent control module respectively controls the heating time, the microwave source power and the phase of the four areas and monitors the temperature in real time;
c, monitoring the corresponding area in real time through the heating unit according to the temperature data acquired in real time to enable the temperature of each area to be consistent with the temperature set by the interface operating system; namely, the temperature of each area is fed back in real time, and the heating time, the microwave source power and the phase of each area are adjusted.
The step E is also included between the steps B and C: the four areas are heated respectively by changing the heating time and power, the temperature data is fed back in real time, and the data is learned through a convolutional neural network.
The antenna array of the unit in the step A is 32, and corresponds to eight areas for heating.
The utility model discloses an intelligent microwave oven of visual operation can feed back in real time through setting up and is heated cavity temperature distribution, through the phase place and the power output who controls a plurality of microwave sources, changes phased array beam radiation direction and radiant intensity, and it is high to reach purpose, heating efficiency and the homogeneity of each subregion pertinence.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1: a schematic diagram of a module of the utility model;
FIG. 2: a block diagram of a prediction structure of a convolutional neural network algorithm;
FIG. 3: heating temperature curve diagrams of each region;
FIG. 4: slice temperature map at second 15;
FIG. 5: slice temperature map at 92 sec;
FIG. 6: the structure of the antenna unit;
FIG. 7: an antenna unit S parameter diagram;
wherein: 1. an infrared temperature thermal image feedback device; 2. a patch antenna; 3. a coaxial cable; 4. a microwave source; 5. a control bus; 6. an intelligent control module; 7. the chamber is heated.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by a person skilled in the art without creative efforts belong to the protection scope of the present invention.
The utility model discloses an intelligent microwave oven with visual operation, which comprises a microwave source 4, an antenna device for adjusting the microwave radiation direction, an infrared temperature thermal image feedback device 1 for feeding back the temperature in a heating cavity 7, and an intelligent control module 6 for controlling the microwave radiation direction, an image module for imaging and displaying a heated object and the intensity; the intelligent control module 6 is connected with the infrared temperature thermal image feedback device 1 and the microwave source 4; the antenna device is connected to a microwave source 4 by a coaxial cable 3. The image module comprises an imaging unit and an image display device, the image display device comprises an interface operating system, and the interface operating system is connected with the intelligent control module 6. Further, the microwave source 4 is a solid-state source with more than six output channels and controllable phase and power. Further, the intelligent control module 6 includes: the storage unit is used for storing the trained deep neural network data, the corresponding operation interface and the heating unit; a heating unit for controlling the heating time, the power and the phase output of the microwave source 4; and the temperature detection unit is used for collecting infrared temperature thermal image feedback data in real time. The intelligent control module 6 can be DPS/FPGA/single chip microcomputer. As shown in the structure diagram of the antenna unit in fig. 6 and the S parameter diagram of the antenna unit in fig. 7, the antenna device is a 4 × 4 patch antenna 2 array, which can form a phased array beam, and the single antenna unit has S11< -10dB in the frequency range of 2.41GHz to 2.49 GHz.
A heating method of an intelligent microwave oven visually operated comprises the following steps: feeding the heating cavity 7 by using a 16-unit antenna array as a feed source, dividing a heating space into four areas, performing simulation acquisition on data about 16 antenna unit phases and heating temperature effects corresponding to the four areas, establishing a corresponding relation between directivity and a multi-source phase value by using an FEM algorithm and a phased array theory, corresponding to an interface operating system, and storing results in a storage unit of the intelligent control module 6; respectively heating the four areas by giving a short time and power, and acquiring real-time temperature data; by analyzing the acquired data and the interface operating system setting data, the heating unit of the intelligent control module 6 respectively controls the heating time, the power and the phase of the microwave source 4 of the four areas and monitors the temperature in real time; and according to the temperature data acquired in real time, the corresponding areas are monitored in real time through the heating units, so that the temperature of each area is consistent with the set temperature of the interface operating system. Namely, the temperature of each area is fed back in real time, and the heating time, the power and the phase of the microwave source 4 of each area are adjusted. Further, a step E is further included between the steps B and C: the four areas are heated respectively by changing the heating time and power, the temperature data is fed back in real time, and the data is learned through a convolutional neural network. The antenna array of the unit in the step A is 32, and corresponds to eight areas for heating.
The heating cavity 7 is fed by taking the 16-unit antenna array as a feed source, the heating space is divided into four regions, the direction of a microwave beam is controlled by changing the phase of 16 antenna feed sources, the four heating spaces are respectively heated, then the temperature is monitored and fed back in real time by the infrared temperature thermal image feedback device 1, the temperature distribution condition corresponding to the four regions is obtained, the process can be realized by combining with an FEM algorithm, a large amount of data about the phases of the 16 antenna units and the heating temperature effects corresponding to the four regions are obtained by simulation collection, and the data are used as a training sample set to train a Deep Neural Network (DNN) in deep learning. The input vector of the network is four areas, namely areas I, II, III and IV needing to be heated, and the corresponding output vector is the phase value of 16 antenna units. For example: when the acquired data of the training set indicate that the temperature of the I area is higher and the temperatures of other areas are lower, the directional heating of one area is indicated. The phase value of the corresponding 16-element antenna array is used as the output vector of the neural network, and the corresponding heating area is used as the input vector, i.e. the input vector can be represented as [1,0,0,0 ]. Therefore, the number of input neurons of the network is 4, and the number of output neurons is 16. The neural network is trained by combining a training sample set with a label obtained by an FEM algorithm to obtain a network with good predictability, and in practical application, phase values of all antenna units of the 16-unit antenna array to be set can be output by inputting an area to be heated.
The directional heating can be realized, namely, the required areas can be heated, and the final heating temperature of the four areas can be consistent with the set temperature of the interface operating system by controlling the heating time and the input power of each area;
on the basis of realizing directional heating, a short time and power are given to the I area for heating, the temperature distribution conditions of the four areas are obtained through temperature acquisition, namely 192 groups of data can be acquired through infrared temperature thermal image feedback with the scanning scale of 12 x 16. And heating the II area under given short time and power by directional heating, collecting temperature distribution data of each area, and so on to obtain 12 x 16 x 4 groups of temperature data. And then the heating time and power are respectively changed, so that a large amount of data about the heating temperature effect and the heating time and power of each area can be obtained. Because the acquired data volume is large, an algorithm which is more important in deep learning is adopted: the Convolutional Neural Network (CNN) as shown in the block diagram of the predictive structure of the convolutional neural network algorithm of fig. 2.
4 decision algorithms are required to achieve control of the heating time, power and phase of the four zones respectively. Taking the decision algorithm of the I region as an example: the large amount of collected data is used as a training data set to train the deep learning CNN network, and when the I area is heated, the input vector is the temperature distribution data T of the current area and other three areasI，TII，TIII，TIV. The output vector is the phase phi of 16 feed antennas for realizing directional heating1，φ2，φ3...φ16And heating time t, and power supplied P for 18 vectors. The schematic diagram is shown in fig. 2. When heating the I area, the input temperature vector T of the I areaISet temperature value T for realizing uniform heating0Input T of the other three regionsII，TIII，TIVThe temperature can be set in a ratio by random functionThe random allocation is performed in the lower half range. By inputting the temperature values of the areas to be heated, it is possible to obtain how much power P and heating time t are required for the areas to reach the required temperature, and finally, when the four areas are heated respectively, the sum of the heating temperatures of a specific area is equal after the four areas are heated.
Wherein i represents a temperature value of a current zone when the i-th zone is subjected to the directional heating. Therefore, after the four areas are heated, the temperature values obtained by the four areas are equal, in the process of collecting the temperature in real time, the collected temperature of the four areas at the current moment needs to be fed back and adjusted in real time to serve as an input vector, and the time, the phase and the power value which still need to be heated are output. And finally, after the four areas are heated, the final temperature of each area is consistent with the set temperature of the interface operating system, so that the directional heating is realized.
The method comprises the following steps of carrying out analog heating simulation by using a 16-unit phased array antenna array, firstly dividing the space into four areas 1-4 by using a single-path 100W power, respectively placing food made of different materials in the four areas, and respectively setting the dielectric constants as follows:
water 80-12 xj, potatoes 65-20 xj, raw beef 52-20 xj and rice 50-10 xj, the microwave absorbing capacity is different due to different materials, the temperature rising effect is different, and a microwave beam with directivity is formed by changing the phase of an antenna based on a phased array theory;
in the using process, the heated object placed in the heating cavity is shot through the camera, a real-time picture or an infrared picture is displayed on the image display device, namely a touch-controlled liquid crystal display screen, the heated object is selected through the interface operating system, and the temperature is randomly set respectively; then, respectively heating the four areas for a short time, then respectively continuously heating, and obtaining real-time temperature feedback by using an infrared temperature thermal image feedback device 1;
the intelligent control module 6 sets the time, phase and power value of each area to be heated, so that the final temperature of the area where the heated object is located is consistent with the set temperature of the interface operation system, and the purpose of targeted and directional heating of each selected area where the heated object is located is achieved.
Temperature and time change table of heating zone (unit: degC)
| Time(s) | | Zone | 2 temperature | Raw beef temperature in |
Rice temperature in |
| 0 | 20.000 | 20.000 | 20.000 | 20.000 | |
| 5 | 21.230 | 20.651 | 20.196 | 20.530 | |
| 10 | 21.988 | 22.300 | 20.600 | 20.875 | |
| 15 | 22.478 | 22.925 | 22.321 | 21.936 | |
| 20 | 23.066 | 23.500 | 23.468 | 24.249 | |
| 35 | 23.462 | 24.702 | 30.050 | 26.548 | |
| 50 | 25.611 | 27.373 | 32.373 | 27.975 | |
| 65 | 26.889 | 29.647 | 40.938 | 33.098 | |
| 80 | 30.706 | 39.559 | 45.420 | 35.834 | |
| 90 | 34.109 | 42.495 | 47.664 | 37.714 |
As shown in the comparison graph of the heating temperature of each zone and the set temperature in fig. 3, the 15 th section temperature map in fig. 4, the 65 th section temperature map in fig. 5, and the 90 th section temperature map in fig. 6, the heating temperature curve of each zone approximately coincides with the preset temperature curve.
The utility model discloses a visual operation's intelligent microwave oven and equipment can feed back in real time through setting up and be heated 7 temperature distribution of cavity, through the phase place and the power output of controlling a plurality of microwave sources 4, changes phased array beam radiation direction and radiant intensity, and it is high to reach purpose, heating efficiency and the homogeneity of each subregion pertinence.
Of course, without departing from the spirit and essence of the present invention, those skilled in the art should be able to make various corresponding changes and modifications according to the present invention, and these corresponding changes and modifications should fall within the scope of the appended claims.
Claims (5)
1. An intelligent microwave oven of visual operation, includes the microwave source, its characterized in that:
the microwave heating device also comprises an antenna device for adjusting the microwave radiation direction, an infrared temperature thermal image feedback device for feeding back the temperature in the heating cavity, an image module for imaging and displaying a heated object and an intelligent control module for controlling the microwave radiation direction and intensity; the intelligent control module is connected with the infrared temperature thermal image feedback device, the image module and the microwave source;
the antenna device is connected with a microwave source through a coaxial cable.
2. The intelligent visually operated microwave oven as claimed in claim 1, wherein: the image module comprises an imaging unit and an image display device, the image display device comprises an interface operating system, and the interface operating system is connected with the intelligent control module.
3. The intelligent visually operated microwave oven as claimed in claim 1, wherein: the microwave source is a solid state source with more than six paths of output and controllable phase and power.
4. The intelligent visually operated microwave oven as claimed in claim 1, wherein: the intelligent control module comprises:
the storage unit is used for storing the trained deep neural network data, the corresponding operation interface and the heating unit;
the heating unit controls heating time, microwave source power and phased array phase output;
and the temperature detection unit is used for collecting infrared temperature thermal image feedback data in real time.
5. The intelligent visually operated microwave oven as claimed in claim 1, wherein: the antenna device is a 4 x 4 patch antenna array capable of forming a phased array beam; the patch antenna array is in a frequency range of 2.41 GHz-2.49 GHz, and S11< -10 dB.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110056913A (en) * | 2019-02-02 | 2019-07-26 | 四川大学 | A kind of intelligent microwave oven and its heating means of visualized operation |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110056913A (en) * | 2019-02-02 | 2019-07-26 | 四川大学 | A kind of intelligent microwave oven and its heating means of visualized operation |
| CN110056913B (en) * | 2019-02-02 | 2024-03-19 | 四川大学 | Intelligent microwave oven with visual operation and heating method thereof |
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