CN113221598A - Food material judging method, refrigerator and storage medium - Google Patents
Food material judging method, refrigerator and storage medium Download PDFInfo
- Publication number
- CN113221598A CN113221598A CN202010069794.2A CN202010069794A CN113221598A CN 113221598 A CN113221598 A CN 113221598A CN 202010069794 A CN202010069794 A CN 202010069794A CN 113221598 A CN113221598 A CN 113221598A
- Authority
- CN
- China
- Prior art keywords
- food material
- recognition result
- odor
- accuracy
- identification
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 235000013305 food Nutrition 0.000 title claims abstract description 92
- 239000000463 material Substances 0.000 title claims abstract description 78
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000001228 spectrum Methods 0.000 claims description 23
- 230000003595 spectral effect Effects 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 11
- 238000010183 spectrum analysis Methods 0.000 claims description 7
- 238000007789 sealing Methods 0.000 claims description 2
- 239000007789 gas Substances 0.000 description 12
- 238000001514 detection method Methods 0.000 description 5
- 238000002474 experimental method Methods 0.000 description 5
- 230000035943 smell Effects 0.000 description 4
- 240000000560 Citrus x paradisi Species 0.000 description 3
- VGGSQFUCUMXWEO-UHFFFAOYSA-N Ethene Chemical compound C=C VGGSQFUCUMXWEO-UHFFFAOYSA-N 0.000 description 3
- 239000005977 Ethylene Substances 0.000 description 3
- 206010063385 Intellectualisation Diseases 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 235000013399 edible fruits Nutrition 0.000 description 2
- 229910052739 hydrogen Inorganic materials 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 240000000716 Durio zibethinus Species 0.000 description 1
- 235000006025 Durio zibethinus Nutrition 0.000 description 1
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 235000007688 Lycopersicon esculentum Nutrition 0.000 description 1
- 240000003768 Solanum lycopersicum Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 238000002329 infrared spectrum Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 235000013372 meat Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005057 refrigeration Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0001—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00 by organoleptic means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Theoretical Computer Science (AREA)
- Pathology (AREA)
- Immunology (AREA)
- General Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Computational Biology (AREA)
- Probability & Statistics with Applications (AREA)
- Multimedia (AREA)
- Evolutionary Computation (AREA)
- Food Science & Technology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Medicinal Chemistry (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Cold Air Circulating Systems And Constructional Details In Refrigerators (AREA)
Abstract
The invention discloses a food material judging method, a refrigerator and a storage medium, which can accurately judge food material information and avoid the problem of inaccurate identification caused by factors such as food material stacking, complex smell and the like.
Description
Technical Field
The invention relates to the field of refrigeration equipment, in particular to a food material judging method, a refrigerator and a storage medium.
Background
With the development of smart homes, people put forward higher and higher requirements on the intellectualization of home equipment, for example, the home equipment is expected to understand the preference of the home equipment and provide more intelligent services, a refrigerator is used as equipment used at high frequency in life, the intellectualization premise comprises the identification and detection of stored articles, and more extended services can be provided on the basis.
In order to identify food materials conveniently, at present, patents of modes of photographing identification exist, but the modes of placing and light limitation exist, such as food stacking, dark corners and the like, image identification has limitations, identification results are inaccurate, other identification methods also have similar problems, for example, when odor is detected, odor components inside some refrigerators are complex, it is difficult to judge which food materials are specific, the identification methods cannot ensure the accuracy of the identification results, and the food materials cannot be judged, so that the intelligent development of the refrigerators is restricted, and the requirements of intelligent families are not met.
Disclosure of Invention
In order to solve the problems in the prior art, the present invention provides a food material determining method, a refrigerator and a storage medium.
To achieve one of the above objects, an embodiment of the present invention provides a method for determining food materials, including the following steps:
photographing the set area and carrying out image recognition to obtain an image recognition result;
acquiring the odor of the set area, and performing odor identification to obtain an odor identification result;
scanning the spectral data of the set area and carrying out spectral analysis to obtain a spectral identification result;
the image recognition result, the odor recognition result and the spectrum recognition result respectively comprise a plurality of kinds of information and the accuracy corresponding to each kind of information;
matching the image recognition result, the odor recognition result and the spectrum recognition result with corresponding weight proportions;
multiplying each accuracy of the image recognition result, the odor recognition result and the spectrum recognition result by a corresponding weight ratio to obtain a weight value, adding the weight values corresponding to the same type information, and judging the type information with the highest weight value as the type of the food material.
As a further improvement of an embodiment of the present invention, the method further comprises the steps of: the weight proportion is determined according to the type information and the accuracy in the corresponding identification device and identification result, and the higher the accuracy is, the higher the weight proportion is.
As a further improvement of an embodiment of the present invention, the method further comprises the steps of: and when all the category information of any identification result does not accord with the actual category, calibrating the equipment obtaining the identification result.
As a further improvement of an embodiment of the present invention, the method further comprises the steps of: and when all the accuracy rates of any identification result are lower than the lowest preset value, setting the weight proportion corresponding to the identification result as 0.
As a further improvement of an embodiment of the present invention, the method further comprises the steps of: and when the accuracy of any identification result is greater than the highest preset value, the type information corresponding to the accuracy is the type of the food material.
As a further improvement of an embodiment of the present invention, the actual type of the food material is determined, and a weight ratio of the type information corresponding to the accuracy rate to the actual type is increased.
As a further improvement of an embodiment of the present invention, the method further comprises the steps of:
and when any kind of information of the image recognition result and the spectrum recognition result does not accord with all kinds of information of the odor recognition result, exhausting the gas in the set area.
As a further improvement of an embodiment of the present invention, the method further comprises the steps of: and sealing the set area after the gas in the set area is discharged, and acquiring and identifying the odor information of the set area after a set time.
In order to achieve one of the above objects, an embodiment of the present invention provides a refrigerator, including a setting area, a memory and a processor, where the memory stores a computer program operable on the processor, the refrigerator further includes an image recognition unit, an odor recognition unit and a spectrum analysis unit, and the processor, when executing the computer program, can implement the steps in the food material determination method.
To achieve one of the above objects, an embodiment of the present invention provides a storage medium storing a computer program, which when executed by a processor can implement the steps of the food material determining method.
Compared with the prior art, the invention has the following beneficial effects: the food material information is accurately judged, the problem of inaccurate identification caused by factors such as food material stacking and complex smell is solved, each identification device grasps certain information about food materials, although the correct information and noise in the information grasped by the devices cannot be directly determined, the information grasped by various devices is integrated, the weight proportion of the devices with higher identification accuracy is increased, the characteristics of food are analyzed from multiple dimensions like a blind person and then summarized, the information of the food can be accurately judged, accurate information is provided for further analysis of the food materials, the intelligent degree of a refrigerator is higher, and the requirements of intelligent families are met.
Drawings
Fig. 1 is a flowchart illustrating a food material determining method according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an embodiment of a refrigerator according to the present invention;
wherein, 1, an image identification unit; 2. a scent recognition unit; 3. a spectral analysis unit; 4. setting an area; 5. a processor; 6. a memory.
Detailed Description
The present invention will be described in detail below with reference to specific embodiments shown in the drawings. These embodiments are not intended to limit the present invention, and structural, methodological, or functional changes made by those skilled in the art according to these embodiments are included in the scope of the present invention.
An embodiment of the invention provides a food material judging method, a refrigerator and a storage medium, wherein accurate information of food materials is obtained through analysis and comparison of multiple dimensions, so that the intelligent degree of the refrigerator is higher, and the requirements of intelligent families are met.
The food material judging method of the embodiment includes the following steps:
photographing the set area and carrying out image recognition to obtain an image recognition result;
acquiring the odor of the set area, and performing odor identification to obtain an odor identification result;
scanning the spectral data of the set area and carrying out spectral analysis to obtain a spectral identification result;
the image recognition result, the odor recognition result and the spectrum recognition result respectively comprise a plurality of kinds of information and the accuracy corresponding to each kind of information;
matching the image recognition result, the odor recognition result and the spectrum recognition result with corresponding weight proportions;
multiplying each accuracy of the image recognition result, the odor recognition result and the spectrum recognition result by a corresponding weight ratio to obtain a weight value, adding the weight values corresponding to the same type information, and judging the type information with the highest weight value as the type of the food material.
The image recognition can be realized through CCD recognition, and the food materials are photographed and analyzed;
the range of the odor generated by food is limited, and the odor generated by different types of food materials is very different, for example, fruits generate ethylene but not meat, so that an odor sensor capable of distinguishing several specific gases of common food materials can be arranged, when a certain odor gas is detected, for example, ethylene is detected, some food materials capable of generating ethylene gas in fruits are screened, and the range can be further narrowed according to other components and the concentration of the gas.
The spectrum scanning can adopt near infrared spectrum or high spectrum, the analysis technology of the spectra is mature, information of hydrogen-containing groups such as C-O, O-H, N-H, S-H, P-H and the like is recorded, the detection on different groups is very accurate, the method is very suitable for detecting organic matters, and qualitative and quantitative identification can be carried out on food. And analyzing the spectral data according to the spectral mathematical model to obtain the type information of the food materials, and establishing the spectral mathematical modeling, wherein the establishing of the spectral mathematical modeling comprises a plurality of stages of scanning, identifying and collecting data, identifying background information of the food, measuring chemical values of various substances and components in the food, removing abnormal values, selecting a proper spectral region, selecting a proper algorithm and parameters to perform modeling, checking and calibrating a model and the like, so that the spectral data can accurately correspond to the type information of the food materials through the spectral information.
The image recognition result, the odor recognition result and the spectrum recognition result can recognize various results, the accuracy of each recognition result is judged, the accuracy of each judgment is analyzed through multiple experiments, and the accuracy of the recognition equipment on the type information judged by the information can be judged according to the algorithm of classical probability.
Because each identification device can grasp part of information about food materials, on the basis that each identification device cannot be guaranteed to be completely accurate, and the situation that identification devices with different dimensions wrongly judge the same food material is rarely caused, the identification results with different dimensions are converted into weight values through weight proportion and then added, and the type of the food material can be accurately judged.
For example, assuming that all three devices detect possible foods including oranges and oranges, the image recognition result determines that the accuracy of the oranges is P1, the odor recognition result determines that the accuracy of the oranges is S1, the spectrum recognition result determines that the accuracy of the oranges is L1, and the weights of the oranges corresponding to the image recognition result, the odor recognition result, and the spectrum recognition result are P1, S1, and L1, respectively, the total weight value Q of the oranges is determinedOrangeP1 × P1+ S1 × S1+ L1 × L1; assuming that the image recognition result judges that the accuracy of the oranges is P2, the odor recognition result judges that the accuracy of the oranges is S2, the spectral recognition result judges that the accuracy of the oranges is L2, and the image recognition result, the odor recognition result and the spectral recognition resultThe weight of the orange corresponding to the result is p2, s2 and l2 respectively, and the weight is judged to be the total weight Q of the orangeOrangeP2 × P2+ S2 × S2+ L2 × L2; when the grapefruit is detected by the spectrum identification, the tomato is detected by the image identification, and Q is detected because other identification devices do not detect the grapefruit and the tomatoShaddockAnd QTomatoWill be low, comparing Q with a larger valueOrangeAnd QOrangeThe type of the food material can be judged according to the size of the food material.
If the accuracy of the orange is judged to be high by the odor identification result, S2> S1, certainly, the fact that the detection result is influenced by the large S2 when the actual result is the orange after S2> S1 does not need to be worried about, because the accuracy of the odor identification result after the orange is identified is high by the S2, the identification of the identification device pair is particularly accurate, and conversely, when the food material is not the orange, the S2 is low, namely the weight ratio of the food material is higher with higher detection accuracy, and if the object to be detected is other food material, the probability that the food material with high accuracy is still detected is smaller than the probability that the food material is detected.
Further, the weight proportion is determined according to the corresponding identification device, the category information in the identification result and the accuracy, and the higher the accuracy is, the higher the weight proportion is.
When the weight proportion corresponding to each recognition result is determined, the weight proportion can be obtained through multiple times of simulation through experiments. When the actual type of the food material is consistent with the judgment result of a certain identification device, or the identification device performs experiments, and when a certain food is put in, the identification result is always accurate, the identification result of the identification device on the type of the food is judged to be more reliable, the weight proportion of the identification device on the food material judgment is improved, and the accurate identification result can be better reflected in the total weight value.
Further, the method also comprises the following steps: and when all the category information of any identification result does not accord with the actual category, calibrating the equipment obtaining the identification result. The detection result of the identification device has no correctness, the identification device cannot reflect food materials in one dimension, and the identification device needs to be reset when a fault is judged to be identified.
Further, the method also comprises the following steps: and when all the accuracy rates of any identification result are lower than the lowest preset value, setting the weight proportion corresponding to the identification result as 0. When the accuracy of one identification device cannot be determined for all identification results and is lower than the minimum preset value, the minimum preset value can be tried for many times through experiments, and when the accuracy is lower than the minimum preset value, the identification results are mostly noise, so the result with low identification accuracy can be directly ignored by setting the weight ratio to be 0.
Further, the method also comprises the following steps: and when the accuracy of any identification result is greater than the highest preset value, the type information corresponding to the accuracy is the type of the food material.
The identification accuracy of the identification equipment for certain type of food materials is high, the identification equipment has higher certainty on the accuracy of the identification result, when the identification equipment is larger than the highest preset value to a certain degree, the identification result can be regarded as correct information of the food materials, correspondingly, if the accuracy of the food materials judged by the identification equipment is not high, the accuracy of the identification equipment for any identification result is not determined, the reliability of 50% of two identification accuracies can be added, the accuracy cannot be higher than that of one identification accuracy which is 100%, the highest preset value can be obtained by multiple trials of experiments, and when the accuracy is larger than the value, the result is affirmatively correct. Of course, the more accurate the result of the identification device determining a food material means that the more accurate the result of the identification device determining that the food material is not the result of the identification device is, that is, the food material is determined, and when the probability of the food material is determined to be low, the weight ratio can be set to 0 according to the above condition that the probability is high and the probability is low.
Further, the actual type of the food material is judged, the weight proportion of the type information corresponding to the accuracy rate and the actual type is improved, and the setting of the weight proportion is obtained through multiple times of experimental judgment.
Further, the method also comprises the following steps:
and when any kind of information of the image recognition result and the spectrum recognition result does not accord with all kinds of information of the odor recognition result, exhausting the gas in the set area.
Because image recognition and spectrum recognition are more accurate relative to odor recognition judgment, when the odor of the previous food is large and remains or the odor of the food in other spaces is transmitted, the odor recognition may be inaccurate, and when any kind of information of the image recognition result and the spectrum recognition result does not accord with all kinds of information of the odor recognition result, for example, the image recognition result is orange and orange, the spectrum recognition result is orange and grapefruit, and the kind of information of the odor recognition result is durian, and does not accord with any kind of information of the image recognition result and the spectrum recognition result, it is judged that the odor recognition result has a problem, the gas in the set area is discharged, and the air inside the set area can be blown out by a fan.
Further, the method also comprises the following steps: and stopping exhausting after exhausting the gas in the set area, and after a set time, acquiring and identifying the odor information of the set area again.
After the gas in the set area is exhausted, the exhaust is stopped, the set area is sealed, the other smells are prevented from entering, generally, the gas emitted by the food materials is released to the space and forms a certain concentration to be detected, a certain time is needed, a set time is set, when the set time is over, the gas in the food materials can be judged to reflect the stored food materials, and then the smells are judged.
Further, an embodiment of the present application further provides a refrigerator, which includes a setting area 4, a memory 6 and a processor 5, where the memory 6 stores a computer program that can be run on the processor 5, the refrigerator further includes an image recognition unit 1, an odor recognition unit 2 and a spectrum analysis unit 3, and when the processor 5 executes the computer program, any one of the steps of the food material determination method described above can be implemented, that is, the steps in any one of the technical solutions of the food material determination method described above can be implemented.
Further, an embodiment of the present application further provides a storage medium, which stores a computer program, and the computer program, when executed by the processor 5, can implement any one of the steps in the food material determining method, that is, implement the steps in any one of the technical solutions in the food material determining method.
Compared with the prior art, the embodiment has the following beneficial effects:
the food material information is accurately judged, the problem of inaccurate identification caused by factors such as food material stacking and complex smell is solved, each identification device grasps certain information about food materials, although the correct information and noise in the information grasped by the devices cannot be directly determined, the information grasped by various devices is integrated, the weight proportion of the devices with higher identification accuracy is increased, the characteristics of food are analyzed from multiple dimensions like a blind person and then summarized, the information of the food can be accurately judged, accurate information is provided for further analysis of the food materials, the intelligent degree of a refrigerator is higher, and the requirements of intelligent families are met.
The detailed description set forth above is merely a specific description of possible embodiments of the present invention and is not intended to limit the scope of the invention, which is intended to include within the scope of the invention equivalent embodiments or modifications that do not depart from the technical spirit of the present invention.
Claims (10)
1. A food material judging method is characterized by comprising the following steps:
photographing the set area and carrying out image recognition to obtain an image recognition result;
acquiring the odor of the set area, and performing odor identification to obtain an odor identification result;
scanning the spectral data of the set area and carrying out spectral analysis to obtain a spectral identification result;
the image recognition result, the odor recognition result and the spectrum recognition result respectively comprise a plurality of kinds of information and the accuracy corresponding to each kind of information;
matching the image recognition result, the odor recognition result and the spectrum recognition result with corresponding weight proportions;
multiplying each accuracy of the image recognition result, the odor recognition result and the spectrum recognition result by a corresponding weight ratio to obtain a weight value, adding the weight values corresponding to the same type information, and judging the type information with the highest weight value as the type of the food material.
2. The food material judgment method of claim 1, further comprising the steps of: the weight proportion is determined according to the type information and the accuracy in the corresponding identification device and identification result, and the higher the accuracy is, the higher the weight proportion is.
3. The food material judgment method of claim 1, further comprising the steps of: and when all the kinds of information of any identification result do not accord with the actual kinds of the food materials, calibrating the equipment obtaining the identification result.
4. The food material judgment method of claim 1, further comprising the steps of: and when all the accuracy rates of any identification result are lower than the lowest preset value, setting the weight proportion corresponding to the identification result as 0.
5. The food material judgment method of claim 1, further comprising the steps of: and when the accuracy of any identification result is greater than the highest preset value, the type information corresponding to the accuracy is the type of the food material.
6. The food material determination method according to claim 1, wherein an actual type of the food material is determined, and a weight ratio of the type information corresponding to an accuracy rate to the actual type is increased.
7. The food material judgment method of claim 1, further comprising the steps of:
and when any kind of information of the image recognition result and the spectrum recognition result does not accord with all kinds of information of the odor recognition result, exhausting the gas in the set area.
8. The food material judgment method of claim 7, further comprising the steps of: and sealing the set area after the gas in the set area is discharged, and acquiring and identifying the odor information of the set area after a set time.
9. A refrigerator comprising a setting area, a memory and a processor, wherein the memory stores a computer program operable on the processor, the refrigerator further comprising an image recognition unit, an odor recognition unit and a spectral analysis unit, and the processor is capable of implementing the steps of the food material determination method according to any one of claims 1 to 8 when executing the computer program.
10. A storage medium storing a computer program, wherein the computer program, when executed by a processor, is capable of implementing the steps of the food material determination method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010069794.2A CN113221598B (en) | 2020-01-21 | 2020-01-21 | Food material judging method, refrigerator and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010069794.2A CN113221598B (en) | 2020-01-21 | 2020-01-21 | Food material judging method, refrigerator and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113221598A true CN113221598A (en) | 2021-08-06 |
| CN113221598B CN113221598B (en) | 2023-04-18 |
Family
ID=77085223
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010069794.2A Active CN113221598B (en) | 2020-01-21 | 2020-01-21 | Food material judging method, refrigerator and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113221598B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106766662A (en) * | 2017-01-19 | 2017-05-31 | Tcl家用电器（合肥）有限公司 | Food materials monitoring management method and system in refrigerator |
| CN206222781U (en) * | 2016-11-03 | 2017-06-06 | Tcl集团股份有限公司 | A kind of intelligent refrigerator |
| CN106871570A (en) * | 2017-01-22 | 2017-06-20 | 浙江大学 | A kind of device based on various dietary regimens in single multispectral imaging unit detection refrigerating chamber |
| CN107835938A (en) * | 2015-07-08 | 2018-03-23 | 皮道练 | Food state measuring device, food state measuring block, include its intelligent apparatus |
-
2020
- 2020-01-21 CN CN202010069794.2A patent/CN113221598B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107835938A (en) * | 2015-07-08 | 2018-03-23 | 皮道练 | Food state measuring device, food state measuring block, include its intelligent apparatus |
| CN206222781U (en) * | 2016-11-03 | 2017-06-06 | Tcl集团股份有限公司 | A kind of intelligent refrigerator |
| CN106766662A (en) * | 2017-01-19 | 2017-05-31 | Tcl家用电器（合肥）有限公司 | Food materials monitoring management method and system in refrigerator |
| CN106871570A (en) * | 2017-01-22 | 2017-06-20 | 浙江大学 | A kind of device based on various dietary regimens in single multispectral imaging unit detection refrigerating chamber |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113221598B (en) | 2023-04-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110895082B (en) | Refrigerator control method and device, computer readable storage medium and refrigerator | |
| CN108460397A (en) | Analysis method, device, storage medium and the electronic equipment of equipment fault type | |
| CN113124644B (en) | Method for identifying peculiar smell food in refrigerator, refrigerator and readable storage medium | |
| CN118152226B (en) | Double-open type moisture-proof cabinet humidity monitoring method based on intelligent sensor | |
| CN105629948B (en) | Test case detection method and device | |
| JP2022160658A (en) | Measuring terminal, measuring system, measuring method, and program | |
| CN104346565B (en) | A kind of vulnerability scanning method and system | |
| CN113221598B (en) | Food material judging method, refrigerator and storage medium | |
| Bian et al. | Medical waste classification system based on opencv and ssd-mobilenet for 5g | |
| CN113218879A (en) | Food material storage method of refrigerator, refrigerator and storage medium | |
| CN117437740B (en) | Industrial fire risk early warning system and analysis method based on big data technology | |
| CN117853827A (en) | Sampling pump working state operation monitoring system and method for atmospheric chamber gas monitoring | |
| CN117541988A (en) | Object detection method of renewable resource recycling system | |
| CN113125358A (en) | Hyperspectrum-based food pesticide residue detection method, device and medium | |
| CN111127814B (en) | Fire alarm identification method and related device | |
| JP2004093447A (en) | Smell measuring device | |
| CN116072244A (en) | Odor detection method for refrigeration appliance and refrigeration appliance | |
| CN111709913B (en) | Method, device and system for detecting deteriorated food in refrigerator | |
| CN114118309A (en) | Sample classification and identification method based on convolutional neural network | |
| CN110672796B (en) | Method and system for monitoring peculiar smell of refrigerator | |
| CN114298137A (en) | Tiny target detection system based on countermeasure generation network | |
| CN114527198A (en) | Polymer product odor detection method and device and electronic nose system | |
| CN108064340A (en) | Determination methods, device, storage medium and the electronic equipment that focus deviates | |
| CN117804550B (en) | Detection method and device for integrated light source encapsulator | |
| CN117129160B (en) | Early warning and alarming method, system, terminal and storage medium of component airtight detection device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |