CN109915867B - Data processing method of oil smoke sensor - Google Patents
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Abstract
A data processing method of an oil fume sensor is characterized by comprising the following steps: starting the range hood, collecting m1 groups of data and sequentially caching according to time sequence; secondly, initializing collected data; thirdly, removing previous data, caching latest m1 groups of collected data, analyzing the current latest m1 groups of collected data by taking every m2 groups of data as a data processing unit, and obtaining latest n2 data states; judging the data state, and correspondingly adjusting m1 groups of reference data according to different conditions; and fifthly, calculating and outputting the oil smoke data corresponding to the latest acquired data, updating and storing the minimum reference data value in the working process of the sensor, and then returning to the third step. According to the invention, the characteristic difference between the hearth environment interference signal and the pure oil fume signal is utilized, the data state is analyzed firstly for the data acquired by the sensor, then the reference data is adjusted, and finally the oil fume data is output.
Description
Technical Field
The invention relates to a data processing method, in particular to a data processing method of an oil smoke sensor.
Background
The existing range hood is usually provided with a plurality of gears for adjustment, gear switching is generally realized by manual intervention according to the environment of kitchen oil smoke, the intelligent degree is low, manual operation is usually lagged, and the gear switching of the range hood is difficult to obtain timely response, so that the kitchen oil smoke is heavy in flavor, and the use environment of a user is poor.
Among the prior art, infrared signal processing technology is mature, the cost is lower, and the oil smoke sensor based on infrared reflection principle can avoid with greasy dirt direct contact to effectively guarantee the life of sensor, moreover, the oil smoke sensor is installed on the glass panels that the lampblack absorber is close to the top of a kitchen range for the most part, can discover the change of oil smoke fast, consequently, utilizes infrared reflection principle to detect the oil smoke to be a feasible oil smoke concentration detection scheme.
However, the sensors based on infrared reflection technology have major problems in that: the range hood is easy to be interfered by the surrounding environment when detecting oil smoke, particularly the interference condition of the range hood environment is complex and various, for example, the range hood reflection, the pan reflection, the cooker uncovering or covering the pan cover, the cooker standing beside the range hood for frying or leaving the range hood, and the like, the change of the range hood environment can cause interference to the oil smoke detection data, thereby outputting the detection result with deviation or even error, and the range hood can not be accurately controlled to work at a proper gear, so that the oil smoke removing effect of the range hood is poor, and the product use performance is low.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for processing the data of the oil smoke sensor, which can effectively filter the interference of the cooking bench environment, aiming at the current technical situation.
The technical scheme adopted by the invention for solving the technical problems is as follows: a data processing method of an oil fume sensor is characterized by comprising the following steps:
step one, starting a range hood, collecting m1 groups of data by an oil smoke sensor and sequentially caching according to time sequence;
initializing collected data, wherein m1 groups of collected data correspond to m1 groups of reference data one by one, and in the initialization process, the minimum value in the initial m1 groups of collected data is set as the initial value of all m1 groups of reference data;
removing the previous collected data, caching the latest m1 groups of collected data, analyzing the current latest m1 groups of collected data by taking every m2 groups of data as a data processing unit, and obtaining the latest n2 data states, wherein n2 is m1/m2, and m1 and n2 are natural numbers larger than 1 respectively; the data state is a state containing one or more data signals of 'stable', 'fluctuating', 'severe fluctuating', 'standard data higher', 'rapid increasing' and 'rapid decreasing' obtained by analyzing the latest collected data;
step four, judging the obtained data state, and correspondingly adjusting m1 groups of reference data according to the following conditions:
a. if the latest n2 data states contain "strong fluctuations"; or, N consecutive data states of the latest N2 data states include "the reference data is higher" and do not include "stationary", where N is a natural number greater than 1; alternatively, the data state corresponding to the previous data processing unit whose output has been completed includes "rapid fall", and the latest n2 data states include "fluctuation"; all the reference data are adjusted to the minimum reference data value stored in the working process of the sensor;
b. if "grow rapidly" is contained in the last n2 data states; alternatively, the latest n2 data states contain a "fast droop"; estimating the oil smoke data corresponding to each set of current collected data according to the trend of the previously output oil smoke data, and calculating the reference data corresponding to each set of collected data according to the current collected data and each estimated oil smoke data, namely adjusting each reference data into a corresponding reference data value obtained by calculation;
c. if the latest n2 data states contain 'stationary' and the absolute value of the difference between the current collected data and the reference data corresponding to the previous group of collected data which is output is larger than the set threshold, adjusting each reference data to the current collected data corresponding to the reference data;
or the latest n2 data states include "steady", and the absolute value of the difference between the current collected data and the reference data corresponding to the previous group of collected data that has been output is less than or equal to the set threshold, then all the reference data are adjusted to the reference data value corresponding to the previous group of collected data;
wherein, the priority order of the situations is a > b > c;
and step five, calculating and outputting the oil smoke data corresponding to the latest acquired data, updating and storing the minimum reference data value in the working process of the sensor, and then returning to the step three.
Preferably, the "stationary" data state is achieved by the following analytical method: the variation between the continuous m3 groups of data is within a preset range (Stable _ L, Stable _ H), and the accumulated variation of the m3 groups of data is within a preset range (AddStable _ L, AddStable _ H).
More preferably, m3 is a natural number greater than 1, and m 2. ltoreq. m3 < m1 is preferable.
The lower limit value of Stable _ L and the upper limit value of Stable _ H can be specifically set according to the fluctuation condition of data acquired by the oil smoke sensor under the conditions of no oil smoke and oil smoke, preferably, the preset values of Stable _ L and Stable _ H are respectively smaller values close to 0, wherein Stable _ L is less than 0, and Stable _ H is more than 0; the preset values AddStable _ L and AddStable _ H are respectively smaller values close to 0, wherein AddStable _ L is less than 0 and AddStable _ H is more than 0, AddStable _ L is more than or equal to Stable _ L, and AddStable _ H is less than or equal to Stable _ H.
Preferably, the "strongly fluctuating" data state is achieved by the following analytical method: at least the variation of n4 groups of data in m2 groups of collected data of one data processing unit is not in a preset range (Stable _ L, Stable _ H), and simultaneously, the accumulated variation of m2 groups of data is in the preset range (AddBigflu _ L, AddBigflu _ H).
More preferably, n4 is a natural number greater than 1, and n4 > m2/2 is preferable.
Preferably, the "fluctuating" data state is achieved by the following analytical method: at least the variation between n5 groups of data in m2 groups of collected data of one data processing unit is not within a preset range (Stable _ L, Stable _ H).
More preferably, n5 is a natural number greater than 1, and n5 < m2/2 is more preferable.
Preferably, the data state of "the reference data is higher" is realized by the following analysis method: the consecutive m5 sets of acquired data are smaller than the corresponding baseline data.
More preferably, m5 is a natural number greater than 1, m2 ≦ m5 < m1, and m5 ═ m3 is preferable.
Preferably, the "rapidly growing" data state is achieved by the following analytical method: the variation between the continuous m6 groups of data in the m2 groups of collected data of one data processing unit is larger than a preset value BigRed _ L.
More preferably, m6 is a natural number of 1 or more, and m6 < m2/2 is more preferable.
Preferably, the "rapidly decreasing" data state is achieved by the following analytical method: the variation between the continuous m7 groups of data in the m2 groups of collected data of one data processing unit is smaller than the preset value BigDrop _ H.
More preferably, m7 is a natural number of 1 or more, m7 < m2/2, and m7 is preferably m 6.
Preferably, m1 is T _ delay/T _ sample, where T _ delay is an output data lag time tolerable by the sensor, and T _ sample is a sensor acquisition data period.
Further preferably, the T _ delay is not greater than 5s, and the T _ sample is not greater than 500 ms.
Preferably, m2 is T _ min/T _ sample, where T _ min is a minimum time of a cooktop environment interference data characteristic, and T _ sample is a sensor data acquisition period.
Further preferably, the T _ min is not less than 500ms, and the T _ sample is not more than 500 ms.
Compared with the prior art, the invention has the advantages that: the sensor data processing method is characterized in that characteristic differences exist between cooking bench environment interference signals and pure cooking fume signals, the collected data of the sensor generally comprise cooking fume data and reference data, after initialization, the data state of the collected data of the sensor is analyzed firstly, then the reference data is adjusted and corrected, and finally the cooking fume data is output.
Drawings
Fig. 1 is a general flowchart of a method for processing data of a soot sensor according to an embodiment of the present invention.
Fig. 2 is a flow chart of data analysis of the embodiment of the present invention in which the collected signal is in a "steady" state.
FIG. 3 is a flow chart of data analysis of the embodiment of the present invention in which the collected signal is in a "surge" state.
Fig. 4 is a flow chart of data analysis of the embodiment of the present invention in which the collected signal is in a "sharp fluctuation" state.
Fig. 5 is a flow chart of data analysis when the collected signal is in a "reference data high" state according to an embodiment of the present invention.
FIG. 6 is a flow chart of data analysis for acquiring a "fast-growing" state of a signal according to an embodiment of the present invention.
FIG. 7 is a flow chart of data analysis for acquiring a "rapid-fall" state of a signal according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying examples.
The embodiment relates to a data processing method of an oil smoke sensor, in particular to a data processing method of an oil smoke sensor based on an infrared reflection technology.
The collected data of the oil smoke sensor generally comprises oil smoke data and reference data, wherein the oil smoke data is converted data of the intensity of infrared reflection signals generated by oil smoke particles, and the reference data is converted data of the intensity of the infrared reflection signals generated by the surrounding environment of the oil smoke sensor. When the detection area of the sensor is inside the hob, the reference data are mainly influenced by the hob environment: when the environment of the cooking bench is stable, the reference data is basically unchanged, the collected data can be used as the reference data when the data is stable, and the oil smoke data can be obtained by subtracting the reference data from the collected data when the oil smoke is detected; however, in the detection process of the sensor, the environment of the cooking bench may change, such as taking away the pot, putting on the pot, covering the pot cover or uncovering the pot cover, and the like, and these actions all change the reference data, and the oil smoke data obtained by directly subtracting the fixed and unchangeable reference data from the collected data has a larger error.
As shown in fig. 1, which is a general flowchart of a data processing method of a soot sensor in the present embodiment, the data processing method includes the following steps:
step one, starting the range hood, acquiring m1 groups of data through an oil smoke sensor, sequentially caching according to acquisition time, and then, removing the earliest acquired data every time new data are acquired, so that the acquired data in the cache are the latest m1 groups of acquired data, wherein m1 is a natural number greater than 1, and is determined according to tolerable output data lag time T _ delay and an acquired data period T _ sample of the sensor, and m1 is T _ delay/T _ sample; in general: t _ delay is not more than 5s, and T _ sample is not more than 500 ms.
And step two, initializing the acquired data, wherein m1 groups of acquired data correspond to m1 groups of reference data one by one, and in the initialization process, the minimum value in the initial m1 groups of acquired data is set as the initial value of all m1 groups of reference data.
Removing the previous acquired data, caching the latest m1 groups of acquired data, analyzing the current latest m1 groups of acquired data by taking every m2 groups of data as a data processing unit, and generating a corresponding data state after processing, wherein the m1 groups of acquired data comprise n2 groups of data processing units, and n 2-m 1/m2 is used for obtaining the latest n2 data states, wherein n2 is a natural number greater than 1, m2 is determined according to the minimum time T _ min containing the characteristics of the hearth environment interference data and the acquired data period T _ sample, and m 2-T _ min/T _ sample. In general: t _ min is not less than 500 ms.
The data state in this embodiment is a state including one or more of "stationary", "fluctuating", "drastic fluctuation", "reference data higher", "rapid increase", "rapid decrease", which is obtained by analyzing the latest acquired data. After data analysis, the latest n8 data states are saved, n8 is greater than n2, generally: n8 is n2+1, which includes the data state corresponding to n2 processing units in the latest buffer data and the data state corresponding to the data processing unit that has completed outputting recently.
Step four, judging the obtained data state, and correspondingly adjusting m1 groups of reference data according to several conditions that the priority order is a > b > c:
a. if the data state contains one or more of the following signals, the latest n2 data states contain "sharp fluctuations"; or, N consecutive data states of the latest N2 data states include "the reference data is higher" and do not include "stationary", where N is a natural number greater than 1; alternatively, the data state corresponding to the previous data processing unit whose output has been completed includes "rapid fall", and the latest n2 data states include "fluctuation"; then the minimum reference data value in the working process of the sensor is taken as each reference data corresponding to the m1 groups of cache data, and the priority of the reference data adjustment mode is highest;
b. if "grow rapidly" is contained in the last n2 data states; alternatively, the latest n2 data states contain a "fast droop"; estimating the oil smoke data corresponding to each set of current collected data according to the trend of the previously output oil smoke data (specifically, the trend of the oil smoke data output by one data processing unit which has been processed recently), and calculating the reference data corresponding to each set of collected data according to the current collected data and each estimated oil smoke data, namely, adjusting each reference data to a corresponding reference data value obtained by calculation, wherein the priority of the adjustment mode of the reference data is medium;
c. if the latest n2 data states contain 'stationary' and the absolute value of the difference between the current collected data and the reference data corresponding to the previous group of collected data which is output is larger than the set threshold, adjusting each reference data to the current collected data corresponding to the reference data;
or the latest n2 data states include "steady", and the absolute value of the difference between the current collected data and the reference data corresponding to the previous group of collected data that has been output is less than or equal to the set threshold, then all the reference data are adjusted to the reference data value corresponding to the previous group of collected data; such reference data adjustment mode has the lowest priority.
When a plurality of reference data adjustment modes are simultaneously present, the reference data is adjusted in an adjustment mode with a high priority.
And fifthly, calculating and outputting the oil smoke data corresponding to the latest acquired data, updating and storing the minimum reference data value in the working process of the sensor, and then returning to the third step. The method for calculating the oil smoke data is to subtract the corresponding reference data from the collected data.
FIGS. 2-7 are flow charts of preferred analysis methods for different data states, respectively.
The data state is "stable" which means that the current detection environment is relatively stable, basically has no oil smoke, and has no dynamic interference, for example, after the pot cover is covered, the oil smoke is basically absent, the pot cover is still, and the current detection environment is in a stable state. As shown in fig. 2, the "stationary" data state is achieved by the following analysis method: the variation between the continuous m3 groups of data is within a preset range (Stable _ L, Stable _ H), and the accumulated variation of the m3 groups of data is within a preset range (AddStable _ L, AddStable _ H). m3 is a natural number greater than 1, and m2 is not less than m3 < m 1.
The lower limit value of Stable _ L and the upper limit value of Stable _ H can be specifically set according to fluctuation conditions of data acquired by the oil smoke sensor under the conditions of no oil smoke and oil smoke, the preset values of Stable _ L and Stable _ H are respectively smaller values close to 0, wherein Stable _ L is less than 0 and Stable _ H is more than 0; the preset values AddStable _ L and AddStable _ H are respectively smaller values close to 0, wherein AddStable _ L is less than 0 and AddStable _ H is more than 0, AddStable _ L is more than or equal to Stable _ L, and AddStable _ H is less than or equal to Stable _ H.
Both "fluctuations" and "sharp fluctuations" are directed to the presence of oil smoke. The "fluctuation" signal indicates that the signal is not "stationary" and there may be less oil smoke, as shown in fig. 3, the data state of "fluctuation" is achieved by the following analysis method: at least the variation between n5 groups of data in m2 groups of collected data of one data processing unit is not within a preset range (Stable _ L, Stable _ H). n5 is a natural number greater than 1, typically: n5 < m 2/2.
The "sharp fluctuation" signal indicates that the possible oil smoke is large, as shown in fig. 4, the data state of the "sharp fluctuation" is realized by the following analysis method: at least the variation of n4 groups of data in m2 groups of collected data of one data processing unit is not in a preset range (Stable _ L, Stable _ H), and simultaneously, the accumulated variation of m2 groups of data is in the preset range (AddBigflu _ L, AddBigflu _ H). n4 is a natural number greater than 1, typically: n4 > m 2/2.
The 'higher reference data' indicates that the interference changes to cause the actual collected data to be reduced, but because of the oil smoke relationship, the reference cannot be recalibrated, so that the reference is higher, for example, when a pot cover is covered, the reference data is in a stable state, and the reference data is 500 at the moment; when the pot cover is uncovered, the collected data is reduced to about 200, the actual reference data is 150, the oil smoke data is about 50, the stable state cannot be achieved due to large fluctuation, the reference data cannot be calibrated to the actual reference data, and the situation that the reference data is higher can occur. As shown in fig. 5, the data state of "the reference data is high" is realized by the following analysis method: the consecutive m5 sets of acquired data are smaller than the corresponding baseline data. m5 is a natural number greater than 1, m2 is not more than m5 < m1, and m5 can be m 3.
The 'rapid increase' and 'rapid decrease' are aimed at the interference of large objects, such as the actions of lifting a pot cover, covering the pot cover and the like. For example, when the lid is opened, the data will first "increase rapidly" and then "decrease rapidly", and a plurality of data states are combined to be judged.
The "rapid increase" signal indicates that a possible object enters the detection area, such as a pot put on a cooking bench, cover a pot cover, stretch a hand to take off the pot cover, etc. As shown in FIG. 6, the "rapidly growing" data state is achieved by the following analytical method: the variation between the continuous m6 groups of data in the m2 groups of collected data of one data processing unit is larger than a preset value BigRed _ L. m6 is a natural number greater than or equal to 1, typically: m6 < m 2/2. In order to distinguish the large concentration oil smoke signal, BigRise _ L can be appropriately selected to have a larger value.
The "rapid fall" signal indicates that a possible object has left the detection zone, such as a pot moving out of the cooktop, a pot lid being uncovered and removed, a person leaving the pot lid, etc. As shown in fig. 7, the "rapidly decreasing" data state is achieved by the following analysis method: the variation between the continuous m7 groups of data in the m2 groups of collected data of one data processing unit is smaller than the preset value BigDrop _ H. m7 is a natural number of 1 or more, preferably m7 ═ m 6. In order to distinguish the large concentration oil smoke signal, BigDrop _ H can be properly selected to be smaller.
In the data state analysis process, the variable quantity among the data is a difference value obtained by subtracting earlier acquired data from newer acquired data among adjacent acquired data; the sensor judges the data state with oil smoke state prior to the data state with interference, for example, the data state judges that large oil smoke is in 'sharp fluctuation', and then the interference at the moment is ignored.
By utilizing the characteristic difference between the hearth environment interference signal and the pure oil fume signal, the data state is analyzed firstly through the collected data of the sensor, then the datum data is adjusted and corrected, and finally the oil fume data is output in the data processing process, so that the purpose of filtering the hearth environment interference can be effectively realized, and the output accuracy of the oil fume data is improved.
Claims (18)
1. A data processing method of an oil fume sensor is characterized by comprising the following steps:
step one, starting a range hood, collecting m1 groups of data by an oil smoke sensor and sequentially caching according to time sequence;
initializing collected data, wherein m1 groups of collected data correspond to m1 groups of reference data one by one, and in the initialization process, the minimum value in the initial m1 groups of collected data is set as the initial value of all m1 groups of reference data;
removing the previous collected data, caching the latest m1 groups of collected data, analyzing the current latest m1 groups of collected data by taking every m2 groups of data as a data processing unit, and obtaining the latest n2 data states, wherein n2 is m1/m2, and m1 and n2 are natural numbers larger than 1 respectively; the data state is a state containing one or more data signals of 'stable', 'fluctuating', 'severe fluctuating', 'standard data higher', 'rapid increasing' and 'rapid decreasing' obtained by analyzing the latest collected data;
step four, judging the obtained data state, and correspondingly adjusting m1 groups of reference data according to the following conditions:
a. if the latest n2 data states contain "strong fluctuations"; or, N consecutive data states of the latest N2 data states include "the reference data is higher" and do not include "stationary", where N is a natural number greater than 1; alternatively, the data state corresponding to the previous data processing unit whose output has been completed includes "rapid fall", and the latest n2 data states include "fluctuation"; all the reference data are adjusted to the minimum reference data value stored in the working process of the sensor;
b. if "grow rapidly" is contained in the last n2 data states; alternatively, the latest n2 data states contain a "fast droop"; estimating the oil smoke data corresponding to each set of current collected data according to the trend of the previously output oil smoke data, and calculating the reference data corresponding to each set of collected data according to the current collected data and each estimated oil smoke data, namely adjusting each reference data into a corresponding reference data value obtained by calculation;
c. if the latest n2 data states contain 'stationary' and the absolute value of the difference between the current collected data and the reference data corresponding to the previous group of collected data which is output is larger than the set threshold, adjusting each reference data to the current collected data corresponding to the reference data;
or the latest n2 data states include "steady", and the absolute value of the difference between the current collected data and the reference data corresponding to the previous group of collected data that has been output is less than or equal to the set threshold, then all the reference data are adjusted to the reference data value corresponding to the previous group of collected data;
the priority order of the above situations is a > b > c, and when multiple reference data adjusting modes occur simultaneously, the reference data are adjusted in the adjusting mode with high priority;
and step five, calculating and outputting the oil smoke data corresponding to the latest acquired data, updating and storing the minimum reference data value in the working process of the sensor, and then returning to the step three.
2. The data processing method of claim 1, wherein: the "stationary" data state is achieved by the following analytical method: the variation between the continuous m3 groups of data is within a preset range (Stable _ L, Stable _ H), and the accumulated variation of the m3 groups of data is within a preset range (AddStable _ L, AddStable _ H).
3. The data processing method of claim 2, wherein: m3 is a natural number greater than 1, and m2 is not less than m3 and is more than m 1.
4. The data processing method of claim 2, wherein: the preset values of Stable _ L and Stable _ H are smaller values close to 0 respectively, wherein the Stable _ L is less than 0, and the Stable _ H is more than 0; the preset values AddStable _ L and AddStable _ H are smaller values close to 0 respectively, wherein AddStable _ L is less than 0 and AddStable _ H is more than 0, AddStable _ L is more than or equal to Stable _ L, and AddStable _ H is less than or equal to Stable _ H.
5. The data processing method of claim 1, wherein: the "strongly fluctuating" data state is achieved by the following analytical method: at least the variation of n4 groups of data in m2 groups of collected data of one data processing unit is not in a preset range (Stable _ L, Stable _ H), and simultaneously, the accumulated variation of m2 groups of data is in the preset range (AddBigflu _ L, AddBigflu _ H).
6. The data processing method of claim 5, wherein: the n4 is a natural number greater than 1, and n4 > m 2/2.
7. The data processing method of claim 1, wherein: the "fluctuating" data state is achieved by the following analytical method: at least the variation between n5 groups of data in m2 groups of collected data of one data processing unit is not within a preset range (Stable _ L, Stable _ H).
8. The data processing method of claim 7, wherein: the n5 is a natural number greater than 1, and n5 < m 2/2.
9. The data processing method of claim 1, wherein: the data state of the reference data higher is realized by the following analysis method: the consecutive m5 sets of acquired data are smaller than the corresponding baseline data.
10. The data processing method of claim 9, wherein: m5 is a natural number greater than 1, and m2 is not less than m5 and is more than m 1.
11. The data processing method of claim 1, wherein: the "rapidly growing" data state is achieved by the following analytical method: the variation between the continuous m6 groups of data in the m2 groups of collected data of one data processing unit is larger than a preset value BigRed _ L.
12. The data processing method of claim 11, wherein: m6 is a natural number of 1 or more, and m6 < m 2/2.
13. The data processing method of claim 1, wherein: the "rapidly decreasing" data state is achieved by the following analytical method: the variation between the continuous m7 groups of data in the m2 groups of collected data of one data processing unit is smaller than the preset value BigDrop _ H.
14. The data processing method of claim 13, wherein: m7 is a natural number of 1 or more, and m7 < m 2/2.
15. The data processing method of claim 1, wherein: the m1 is T _ delay/T _ sample, where T _ delay is an output data lag time tolerable by the sensor, and T _ sample is a sensor data acquisition period.
16. The data processing method of claim 15, wherein: the T _ delay is not greater than 5s, and the T _ sample is not greater than 500 ms.
17. The data processing method of claim 1, wherein: and m2 is T _ min/T _ sample, wherein T _ min is the minimum time of the characteristics of the cooking bench environment interference data, and T _ sample is the data acquisition period of the sensor.
18. The data processing method of claim 17, wherein: the T _ min is not less than 500ms, and the T _ sample is not more than 500 ms.
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| 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 |
| CN204649623U (en) * | 2015-06-09 | 2015-09-16 | 广东美的厨房电器制造有限公司 | Smoke which pick-up unit and cigarette machine |
| 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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