CN109916855A - The data processing method of oil smoke sensor - Google Patents
The data processing method of oil smoke sensor Download PDFInfo
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- CN109916855A CN109916855A CN201711329383.7A CN201711329383A CN109916855A CN 109916855 A CN109916855 A CN 109916855A CN 201711329383 A CN201711329383 A CN 201711329383A CN 109916855 A CN109916855 A CN 109916855A
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Abstract
A kind of data processing method of oil smoke sensor, which comprises the following steps: one, starting kitchen ventilator, oil smoke sensor acquisition m group data are simultaneously successively cached according to chronological order；Two, acquisition data initialization processing；Three, data before removing cache newest m group acquisition data, carry out data mode analysis to current newest m group acquisition data；Four, the data mode of acquisition is judged, and corresponding adjustment is made to m group reference data according to different situations；Five, it calculates and exports the corresponding oil smoke data of newest acquisition data, then return step three.The present invention is according to the feature of human body interference signal, it is first analyzed by the acquisition data to sensor, then reference data is adjusted, the data handling procedure of final output oil smoke data, the interference of human action bring can be effectively filtered out, and then reduce the error in oil smoke data handling procedure, improve the output precision of oil smoke data.
Description
Technical field
The present invention relates to a kind of data processing method, especially a kind of data processing method of oil smoke sensor.
Background technique
Existing range hood is typically provided with multiple gears for adjusting, typically according to kitchen fume environment need
Manual intervention is wanted to realize gear switch, not only intelligence degree is low, but also lag, the shelves of range hood are often compared in manual operation
Position switching is difficult to timely be responded, and causes kitchen fume highly seasoned, user environment is poor.
Currently, infrared signal processing technique is mature, cost is relatively low, the oil smoke sensor based on infrared external reflection principle can be kept away
Exempt from directly to contact with greasy dirt, installs on glass panel of the kitchen ventilator close to hearth and passed by the oil smoke that infrared external reflection principle is realized
Sensor, can not only quickly find the variation of oil smoke, and can prevent greasy dirt, guarantee the service life of sensor, be a kind of
More feasible oil smoke concentration detection scheme.
But since the having differences property of kitchen environment of different user, such as the mounting height of kitchen ventilator are different, hearth
The factors such as of different size cause the sensor based on infrared signal to be readily possible to detect the interference other than oil smoke in actual use,
Dynamic disturbance when especially human body various operational motions during the cooking process and human body are close to or away from kitchen ventilator, this
There are biggish deviations for the actual signal that the dynamic disturbance of sample detects oil smoke sensor, and directly exporting these signals can lead
Testing result inaccuracy is caused, and then influences the using effect of kitchen ventilator.
Summary of the invention
The technical problem to be solved by the present invention is to for above-mentioned state of the art and people can be effectively filtered out by providing one kind
The oil smoke sensing data processing method of body dynamic environment interference.
The technical scheme of the invention to solve the technical problem is: a kind of data processing side of oil smoke sensor
Method, which is characterized in that the data processing method the following steps are included:
Step 1: starting kitchen ventilator, oil smoke sensor acquisition m group data are simultaneously successively cached according to chronological order；
Step 2: acquisition data initialization processing, m group acquisition data correspond m group reference data respectively, and initialize
In the process, the minimum value in initial m group acquisition data is set as the initial value of all m group reference datas；
Step 3: the acquisition data before removing, cache newest m group acquisition data, number is acquired to current newest m group
According to progress data mode analysis；The data mode be by analysis of data collected obtain comprising " steady ", " slowly increasing
The state of one of length ", " slowly decline ", " increasing rapidly ", " decline rapidly " or a variety of data-signals；
Step 4: the data mode to acquisition judges, and phase is made to m group reference data according to following several situations
The adjustment answered:
If a, latest data state is comprising in " slowly increasing ", " slowly decline ", " increasing rapidly " and " rapid decline "
One of signal or multiple signals, then the oil smoke data trend exported before estimates newest m group and acquires data institute
Corresponding oil smoke data subtract the oil smoke data accordingly estimated according to every group of acquisition data and extrapolate corresponding to every group of acquisition data
Reference data；
If b, latest data state includes " steady " signal, also, currently acquires data and the previous of output is completed
Absolute difference corresponding to group acquisition data between reference data is greater than given threshold, then is used as this using current acquisition data
Acquire reference data corresponding to data；
If c, latest data state includes " steady " signal, also, currently acquires data and the previous of output is completed
Absolute difference between reference data corresponding to group acquisition data is less than or equal to given threshold, then acquires data institute with previous group
Corresponding reference data value is as reference data adjusted；
Step 5: calculate and export the corresponding oil smoke data of newest acquisition data, then return step three.
Preferably, the m=t/T_sample, wherein the t is sensor sample cache-time, the T_
Sample is that sensor acquires the data period；The t is according to sensor tolerable output data lag time T_delay and packet
The minimum time T_min of the interference data characteristics containing human body is determined, also, the T_min < t < T_delay.
As further preferred, the T_sample is not more than 500ms, and the T_delay is not more than 5s, the T_min
Not less than 500ms.
Preferably, " steady " data mode is realized by following analysis method: the change between continuous m1 group data
Change amount within preset range (Stable_L, Stable_H), meanwhile, the cumulative variable quantity of the m1 group data is in preset range
Within (AddStable_L, AddStable_H).
Preferably, the m1 is natural number greater than 1, also, m≤m1 < T_delay/T_sample.
The lower limit value of Stable_L and the upper limit value of Stable_H in no oil smoke and have oil smoke feelings according to oil smoke sensor
The fluctuation situation setting of data is acquired under condition.As further preferred, the preset value Stable_L and Stable_H can divide
The smaller value close to 0 is not taken, wherein Stable_L<0, Stable_H>0；The preset value AddStable_L and
AddStable_H is respectively the smaller value close to 0, wherein AddStable_L<0, AddStable_H>0, also,
AddStable_L >=Stable_L and AddStable_H≤Stable_H.
Preferably, the data mode of described " slowly increasing " is realized by following analysis method: between newest m2 group data
Variable quantity within preset range (SlowRise_L, SlowRise_H), also, the cumulative variable quantity between the m2 group data exists
Within preset range (AddSlowRise_L, AddSlowRise_H)；Meanwhile there is change between n2 group data in the m2 group data
Change amount is greater than 0.
Preferably, the m2 is natural number greater than 1, also, m2≤m；The n2 is the nature more than or equal to 1
Number, and m2/2 < n2 < m2.
As further preferred, preset value SlowRise_L<0, preset value SlowRise_H>0, the preset value
AddSlowRise_L is more than or equal to preset value AddStable_H；Meanwhile the preset value SlowRise_H and preset value
The sum of SlowRise_L is greater than 0.
Preferably, the data mode of " the slowly decline " is realized by following analysis method: newest m3 group data
Between variable quantity within preset range (SlowDrop_L, SlowDrop_H), also, the cumulative variable quantity between the m3 group data
Within set interval preset range (AddSlowDrop_L, AddSlowDrop_H)；Meanwhile there is n3 group in the m3 group data
Variable quantity between data is less than 0.
Preferably, the m3 is natural number greater than 1, also, m3≤m.
Preferably, the n3 is natural number more than or equal to 1, also, m3/2 < n3 < m3.
As further preferred, setting value SlowDrop_L<0, setting value SlowDrop_H>0；Also, institute
The sum of setting value SlowDrop_H and setting value SlowDrop_L are stated less than 0；The setting value AddSlowDrop_H is less than or equal to
Setting value AddStable_L.
Preferably, the data mode of described " increasing rapidly " is realized by following analysis method: between continuous m4 group data
Variable quantity be greater than preset value BigRise_L.
Preferably, the m4 is natural number greater than 1, also, m4≤m.
As further preferred, the preset value BigRise_L is greater than preset value SlowRise_H.
Preferably, the data mode of " decline rapidly " is realized by following analysis method: between continuous m5 group data
Variable quantity be less than preset value BigDrop_H.
Preferably, the m5 is natural number greater than 1, also, m5≤m.
As further preferred, the preset value BigDrop_H is less than preset value SlowDrop_L.
Compared with the prior art, the advantages of the present invention are as follows: it is easy for the oil smoke sensor based on infrared external reflection technology
The problem of by hearth environmental disturbances, provides a kind of sensing data processing method, general according to the acquisition data of sensor
Comprising oil smoke data and reference data, using the feature of human body interference signal, number is first analyzed by the acquisition data to sensor
According to state, reference data, then the data handling procedure of final output oil smoke data are then adjusted and corrected, human body can be effectively filtered out
Close to hearth, hearth, manpower are left in the various movement brings interference such as sensor front slide, and then raising oil smoke data processing
Accuracy, the final accuracy and intelligence degree for improving oil smoke judgement.
Detailed description of the invention
Fig. 1 is the oil smoke sensing data processing method general flow chart of the embodiment of the present invention.
Fig. 2 is that the acquisition signal of the embodiment of the present invention is the data analysis flowcharts of " steady " state.
Fig. 3 is the data analysis flowcharts that the acquisition signal of the embodiment of the present invention is " slowly increasing " state.
Fig. 4 is the data analysis flowcharts that the acquisition signal of the embodiment of the present invention is " slowly decline " state.
Fig. 5 is that the acquisition signal of the embodiment of the present invention is the data analysis flowcharts of " increasing rapidly " state.
Fig. 6 is the data analysis flowcharts that the acquisition signal of the embodiment of the present invention is " decline rapidly " state.
Specific embodiment
The present invention will be described in further detail below with reference to the embodiments of the drawings.
The working principle of oil smoke sensor based on infrared external reflection technology is the infrared signal quilt of infrared launcher transmitting
After smoke particle or the reflection of other objects, received by infrared receiving device, and digital signal is converted by signal conversion unit,
Acquisition data are handled finally by data processing unit, filtering interference signals simultaneously export oil smoke signal.
Generally, the acquisition data packet data containing oil smoke and reference data of sensor, oil smoke data, that is, smoke particle generate
Infrared reflectance signal intensity conversion data, the infrared reflectance signal that reference data, that is, fume eliminating outer sensor ambient enviroment generates
Intensity conversion data.In sensor ambient stable, reference data is basically unchanged, can be in data stabilization acquisition data
As reference data, subtracting reference data by acquisition data when detecting oil smoke can be obtained oil smoke data.But it is dry for human body
The variation for causing sensor ambient enviroment is disturbed, reference data can change therewith, directly subtract base value by acquiring data
It is larger according to the oil smoke data error of acquisition.
The present embodiment proposes a kind of oil smoke data processing side that can effectively filter out human body interference aiming at the above problem
Method.The oil smoke sensing data processing method of the present embodiment includes the following steps, referring to Fig. 1 flow chart:
Step 1: starting kitchen ventilator, oil smoke sensor acquisition m group data are simultaneously successively cached according to chronological order, this
It collects new data every time afterwards and removes earliest acquisition data, so that the acquisition data for guaranteeing caching are that newest m group is adopted
Collect data, wherein m=t/T_sample (m is the natural number greater than 1), t is sensor sample cache-time, and T_sample is
Sensor acquires the data period；T interferes number according to sensor tolerable output data lag time T_delay and comprising human body
It is determined according to the minimum time T_min of feature, also, T_min < t < T_delay；Generally, T_sample is not more than 500ms, T_
Delay is not more than 5s, and T_min is not less than 500ms.
Step 2: acquisition data initialization processing, m group acquisition data correspond m group reference data respectively, and initialize
In the process, the minimum value in initial m group acquisition data is set as the initial value of all m group reference datas.
Step 3: the acquisition data before removing, cache newest m group acquisition data, number is acquired to current newest m group
According to progress data mode analysis.
Step 4: the data mode to acquisition judges, and phase is made to m group reference data according to following several situations
The adjustment answered:
If a, latest data state is comprising in " slowly increasing ", " slowly decline ", " increasing rapidly " and " rapid decline "
One of signal or multiple signals, then the oil smoke data trend exported before estimates newest m group and acquires data institute
It is right to extrapolate each group of acquisition data institute according to the oil smoke data of current acquisition data and each estimation for corresponding oil smoke data
Each reference data is adjusted to calculate obtained corresponding reference data value by the reference data answered；
If b, latest data state includes " steady " signal, also, currently acquires data and the previous of output is completed
Absolute difference corresponding to group acquisition data between reference data is greater than given threshold, then is used as this using current acquisition data
Acquire reference data corresponding to data；
If c, latest data state includes " steady " signal, also, currently acquires data and the previous of output is completed
Absolute difference between reference data corresponding to group acquisition data is less than or equal to given threshold, then acquires data institute with previous group
Corresponding reference data value is as reference data adjusted；
Step 5: calculate and export the corresponding oil smoke data of newest acquisition data, then return step three.
Data mode in the present embodiment be by analysis of data collected obtain comprising " steady ", " slowly growth ",
The state of one of " slowly decline ", " increasing rapidly ", " decline rapidly " or a variety of data-signals；Specific every kind of data mode
Acquisition realized by following Optimization Analysis.
As shown in Fig. 2, " steady " data mode is realized by following analysis method: having between continuous m1 group data
Variable quantity within preset range (Stable_L, Stable_H), meanwhile, the cumulative variable quantity of the m1 group data is in preset range
Within (AddStable_L, AddStable_H).M1 is natural number greater than 1, also, m≤m1 < T_delay/T_sample.
In order to enable the output of " steady " data mode is more stringent and accurate, avoid such as exporting " steady " data in small oil smoke
The erroneous judgement of state continuously detects acquisition data, and guarantees that the quantity of detection data cannot be too small, and m1 minimum value is with m1
=m is preferred；In order to guarantee the reaction speed of detection, m1 again cannot be too big, and m1 maximum value is with m1=T_delay/T_sample
It is advisable, wherein T_delay/T_sample is the data group number acquired in sensor tolerable output data lag time.
The preset value Stable_L and Stable_H is respectively the smaller value close to 0, wherein Stable_L < 0,
Stable_H>0；The preset value AddStable_L and AddStable_H is respectively the smaller value close to 0, wherein
AddStable_L<0, AddStable_H>0, also, AddStable_L>=Stable_L and AddStable_H≤Stable_
H." steady " signal indicates oil smoke may be not present or oil smoke is extremely small.
As shown in figure 3, the data mode of described " slowly increasing " is realized by following analysis method: between newest m2 group data
Variable quantity within preset range (SlowRise_L, SlowRise_H), also, the cumulative variable quantity between the m2 group data exists
Within preset range (AddSlowRise_L, AddSlowRise_H)；Meanwhile there is change between n2 group data in the m2 group data
Change amount is greater than 0.The m2 is natural number greater than 1, also, m2≤m；The n2 is the natural number more than or equal to 1, and
m2/2<n2<m2.Preset value SlowRise_L<0, preset value SlowRise_H>0, the preset value AddSlowRise_L
More than or equal to preset value AddStable_H；Meanwhile the sum of the preset value SlowRise_H and preset value SlowRise_L is greater than
0." slowly increasing " signal indicates that there may be the close equal violate-actions of human body.
As shown in figure 4, the data mode of " the slowly decline " is realized by following analysis method: newest m3 group data
Between variable quantity within preset range (SlowDrop_L, SlowDrop_H), also, the cumulative variable quantity between the m3 group data
Within preset range (AddSlowDrop_L, AddSlowDrop_H)；Meanwhile having between n3 group data in the m3 group data
Variable quantity is less than 0.The m3 is natural number greater than 1, also, m3≤m, preferably m3=m2.The n3 is more than or equal to 1
Natural number, also, m3/2 < n3 < m3.Preset value SlowDrop_L<0, preset value SlowDrop_H>0；Also, institute
The sum of preset value SlowDrop_H and preset value SlowDrop_L are stated less than 0；The preset value AddSlowDrop_H is less than or equal to
Preset value AddStable_L." slowly decline " signal indicates that there may be the separate equal violate-actions of human body.
As shown in figure 5, the data mode of described " increasing rapidly " is realized by following analysis method: there is continuous m4 group data
Between variable quantity be greater than preset value BigRise_L.The m4 is natural number greater than 1, also, m4≤m.In order to distinguish big concentration
Oil smoke signal, BigRise_L can suitably select greater value, preferably, the present embodiment setting preset value BigRise_L is big
In preset value SlowRise_H." increasing rapidly " signal indicates that there may be wave to enter the violate-actions such as detection zone.
As shown in fig. 6, the data mode of " decline rapidly " is realized by following analysis method: there is continuous m5 group data
Between variable quantity be less than preset value BigDrop_H.The m5 is natural number greater than 1, also, m5≤m, preferably m5=m4.For
The big concentration oil smoke signal of differentiation, BigDrop_H can suitably select smaller value, preferably, the present embodiment setting is described default
Value BigDrop_H is less than preset value SlowDrop_L." decline rapidly " signal indicates that there may be wave to leave detection zone etc.
Violate-action.
In above-mentioned each data mode analysis method of the present embodiment, the variable quantity between data is relatively new between adjacent acquisition data
Acquisition data subtract earlier acquisition data obtain difference.The present embodiment is according to the feature of human body interference signal, by right
The acquisition data of sensor first analyze data mode, then adjust and correct reference data, then the number of final output oil smoke data
According to treatment process, can effectively filter out human body close to hearth, leave hearth, manpower in the various movement brings such as sensor front slide
Interference, and then reduce the error in oil smoke data handling procedure, improve the output precision of oil smoke data.
Claims (19)
1. a kind of data processing method of oil smoke sensor, which is characterized in that the data processing method the following steps are included:
Step 1: starting kitchen ventilator, oil smoke sensor acquisition m group data are simultaneously successively cached according to chronological order；
Step 2: acquisition data initialization processing, m group acquisition data correspond m group reference data, and initialization procedure respectively
In, the minimum value in initial m group acquisition data is set as the initial value of all m group reference datas；
Step 3: the acquisition data before removing, cache newest m group acquisition data, to current newest m group acquisition data into
Row data state analysis；The data mode be by analysis of data collected obtain comprising " steady ", " slowly growth ",
The state of one of " slowly decline ", " increasing rapidly ", " decline rapidly " or a variety of data-signals；
Step 4: the data mode to acquisition judges, and m group reference data is made accordingly according to following several situations
Adjustment:
If a, latest data state includes its in " slowly increase ", " slowly decline ", " increasing rapidly " and " declining rapidly "
A kind of middle signal or multiple signals, then the oil smoke data trend exported according to before estimate corresponding to newest m group acquisition data
Oil smoke data, subtract the oil smoke data accordingly estimated according to every group of acquisition data and extrapolate base corresponding to every group of acquisition data
Quasi- data；
If b, latest data state includes " steady " signal, also, current acquisition data are adopted with the previous group that output is completed
The absolute difference collected between reference data corresponding to data is greater than given threshold, then using current acquisition data as the acquisition
Reference data corresponding to data；
If c, latest data state includes " steady " signal, also, current acquisition data are adopted with the previous group that output is completed
The absolute difference collected between reference data corresponding to data is less than or equal to given threshold, then corresponding to previous group acquisition data
Reference data value as reference data adjusted；
Step 5: calculate and export the corresponding oil smoke data of newest acquisition data, then return step three.
2. data processing method according to claim 1, it is characterised in that: the m=t/T_sample, wherein the t
For sensor sample cache-time, the T_sample is that sensor acquires the data period；The t is tolerable according to sensor
Output data lag time T_delay and the minimum time T_min determination that data characteristics is interfered comprising human body, also, the T_
min<t<T_delay。
3. data processing method according to claim 2, it is characterised in that: the T_sample is not more than 500ms, described
T_delay is not more than 5s, and the T_min is not less than 500ms.
4. data processing method according to claim 2, it is characterised in that: " steady " data mode passes through following
Analysis method is realized: the variable quantity between continuous m1 group data within preset range (Stable_L, Stable_H), meanwhile, this
The cumulative variable quantity of m1 group data is within preset range (AddStable_L, AddStable_H).
5. data processing method according to claim 4, it is characterised in that: the m1 is natural number greater than 1, also, m
≤m1<T_delay/T_sample。
6. data processing method according to claim 4, it is characterised in that: the preset value Stable_L and Stable_H
Respectively close to 0 smaller value, wherein Stable_L<0, Stable_H>0；The preset value AddStable_L and
AddStable_H is respectively the smaller value close to 0, wherein AddStable_L<0, AddStable_H>0, also,
AddStable_L >=Stable_L and AddStable_H≤Stable_H.
7. data processing method according to claim 2, it is characterised in that: the data mode of described " slowly increasing " passes through
Following analysis method is realized: variable quantity between newest m2 group data preset range (SlowRise_L, SlowRise_H) it
It is interior, also, the cumulative variable quantity between the m2 group data is within preset range (AddSlowRise_L, AddSlowRise_H)；
Meanwhile there is the variable quantity between n2 group data to be greater than 0 in the m2 group data.
8. data processing method according to claim 7, it is characterised in that: the m2 is the natural number greater than 1, also,
m2≤m；The n2 is natural number more than or equal to 1, and m2/2 < n2 < m2.
9. data processing method according to claim 7, it is characterised in that: preset value SlowRise_L < 0 is preset
Value SlowRise_H > 0, the preset value AddSlowRise_L are more than or equal to preset value AddStable_H；Meanwhile it is described default
The sum of value SlowRise_H and preset value SlowRise_L are greater than 0.
10. data processing method according to claim 2, it is characterised in that: the data mode of " the slowly decline " is logical
Cross the realization of following analysis method: the variable quantity between newest m3 group data is at preset range (SlowDrop_L, SlowDrop_H)
Within, also, the cumulative variable quantity between the m3 group data preset range (AddSlowDrop_L, AddSlowDrop_H) it
It is interior；Meanwhile there is the variable quantity between n3 group data less than 0 in the m3 group acquisition data.
11. data processing method according to claim 10, it is characterised in that: the m3 is the natural number greater than 1, and
And m3≤m.
12. data processing method according to claim 11, it is characterised in that: the n3 is the nature more than or equal to 1
Number, also, m3/2 < n3 < m3.
13. data processing method according to claim 10, it is characterised in that: preset value SlowDrop_L < 0, institute
State preset value SlowDrop_H > 0；Also, the sum of the preset value SlowDrop_H and preset value SlowDrop_L are less than 0；Institute
Preset value AddSlowDrop_H is stated less than or equal to preset value AddStable_L.
14. data processing method according to claim 7, it is characterised in that: the data mode of described " increasing rapidly " is logical
Cross the realization of following analysis method: the variable quantity between continuous m4 group data is greater than preset value BigRise_L.
15. data processing method according to claim 14, it is characterised in that: the m4 is the natural number greater than 1, and
And m4≤m.
16. data processing method according to claim 14, it is characterised in that: the preset value BigRise_L is greater than pre-
If value SlowRise_H.
17. data processing method according to claim 10, it is characterised in that: the data mode of " decline rapidly " is logical
Cross the realization of following analysis method: the variable quantity between continuous m5 group data is less than default BigDrop_H.
18. data processing method according to claim 17, it is characterised in that: the m5 is the natural number greater than 1, and
And m5≤m.
19. data processing method according to claim 17, it is characterised in that: the preset value BigDrop_H is less than pre-
If value SlowDrop_L.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000304742A (en) * | 1999-04-22 | 2000-11-02 | Daido Steel Co Ltd | Method and apparatus for measurement of oil amount on surface or wire material |
| CN201340001Y (en) * | 2009-01-08 | 2009-11-04 | 张国胜 | Smoke exhaust ventilator capable of automatically adjusting air exhaust speed according to oil smoke concentration |
| US20100225477A1 (en) * | 2007-06-13 | 2010-09-09 | Oy Halton Group Ltd. | Duct grease deposit detection devices, systems, and methods |
| CN102121906A (en) * | 2011-01-31 | 2011-07-13 | 无锡中星微电子有限公司 | Smoke detection system as well as intelligent control system and intelligent control method of range hood |
| CN102538047A (en) * | 2012-03-01 | 2012-07-04 | 佛山市顺德万和电气配件有限公司 | Intelligent smoke exhaust ventilator and control method thereof |
| CN104849242A (en) * | 2015-06-09 | 2015-08-19 | 广东美的厨房电器制造有限公司 | Smoke quantity detection device and range hood |
| JP2016031169A (en) * | 2014-07-28 | 2016-03-07 | パナソニックＩｐマネジメント株式会社 | Heating cooker |
| CN107420951A (en) * | 2017-06-16 | 2017-12-01 | 广东海信家电有限公司 | A kind of method, apparatus and range hood for controlling range hood |
-
2017
- 2017-12-13 CN CN201711329383.7A patent/CN109916855B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000304742A (en) * | 1999-04-22 | 2000-11-02 | Daido Steel Co Ltd | Method and apparatus for measurement of oil amount on surface or wire material |
| US20100225477A1 (en) * | 2007-06-13 | 2010-09-09 | Oy Halton Group Ltd. | Duct grease deposit detection devices, systems, and methods |
| CN201340001Y (en) * | 2009-01-08 | 2009-11-04 | 张国胜 | Smoke exhaust ventilator capable of automatically adjusting air exhaust speed according to oil smoke concentration |
| CN102121906A (en) * | 2011-01-31 | 2011-07-13 | 无锡中星微电子有限公司 | Smoke detection system as well as intelligent control system and intelligent control method of range hood |
| CN102538047A (en) * | 2012-03-01 | 2012-07-04 | 佛山市顺德万和电气配件有限公司 | Intelligent smoke exhaust ventilator and control method thereof |
| JP2016031169A (en) * | 2014-07-28 | 2016-03-07 | パナソニックＩｐマネジメント株式会社 | Heating cooker |
| CN104849242A (en) * | 2015-06-09 | 2015-08-19 | 广东美的厨房电器制造有限公司 | Smoke quantity detection device and range hood |
| CN107420951A (en) * | 2017-06-16 | 2017-12-01 | 广东海信家电有限公司 | A kind of method, apparatus and range hood for controlling range hood |
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