CN110966731B - Method for regulating operating parameters - Google Patents
Method for regulating operating parameters Download PDFInfo
- Publication number
- CN110966731B CN110966731B CN201811141701.1A CN201811141701A CN110966731B CN 110966731 B CN110966731 B CN 110966731B CN 201811141701 A CN201811141701 A CN 201811141701A CN 110966731 B CN110966731 B CN 110966731B
- Authority
- CN
- China
- Prior art keywords
- working parameters
- air
- neural network
- network model
- parameters
- 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.)
- Active
Links
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
Abstract
The application belongs to the field of smart homes, and particularly provides a method and a device for adjusting working parameters. The method comprises the following steps: the method comprises the steps of obtaining set working parameters of the air purifier and an air index of a space where the air purifier is located; processing the air index and the set working parameters based on a neural network model to obtain target working parameters to be adjusted, wherein the neural network model is a model generated by training sample data of the air purifier; the set operating parameters are adjusted based on the target operating parameters. The application solves the technical problems that when a user controls household appliances, the running state of the household appliances needs to be manually adjusted, more user time is wasted, and user experience is poor.
Description
Technical Field
The application relates to the field of smart home, in particular to a method for adjusting working parameters.
Background
At present, along with the reduction of air quality, the application of air purifier is more and more extensive, and when using air purifier, the user generally adjusts air purifier's power and length of time of operation according to current air index.
In the prior art, when a user controls an air purifier, the user needs to select the operation state of the air purifier required by the user through a mobile phone, a remote controller or a key on the air purifier; the time of the user is wasted, and the user experience is poor.
Disclosure of Invention
The embodiment of the application provides a method for adjusting working parameters, and the method is used for at least solving the technical problems that when a user controls household appliances, the running state of the household appliances needs to be manually adjusted, the time of the user is wasted, and the user experience is poor.
According to an aspect of an embodiment of the present application, there is provided a method of adjusting an operating parameter, including: acquiring set working parameters of the air purifier and an air index of a space where the air purifier is located; processing the air index and the set working parameters based on a neural network model to obtain target working parameters to be adjusted, wherein the neural network model is a model generated by training sample data of the air purifier; the set operating parameters are adjusted based on the target operating parameters.
Optionally, the sample data is data generated by operating the air purifier based on set historical operating parameters and an air index in a historical time period, wherein the historical operating parameters include at least one of: the set working time, working power, working duration and adjusting data.
Optionally, under the condition that different air indexes of the air purifier are obtained, different set working parameters corresponding to the different air indexes are called, so that the neural network model is processed according to the different air indexes and the corresponding working parameters to obtain different target working parameters.
Optionally, processing the air index and the set operating parameters based on the neural network model, and obtaining the target operating parameters to be adjusted includes: a, a first neuron in a neural network model receives an air index and set working parameters to obtain a first processing result; step B, performing clustering analysis on the first processing result to obtain a classification result; step C, determining the next neuron receiving the first processing result in the neural network model based on the classification result; step D, sending the first processing result to the next neuron, and processing the first processing result by the next neuron to obtain a second processing result; and E, circularly executing the step B to the step D based on the second processing result until nodes corresponding to the neural network model are traversed, and generating target working parameters.
Optionally, the cluster analysis is to calculate data correlation in the first processing result by using a euclidean distance method, and perform classification based on the correlation to obtain a classification result.
Optionally, before processing the air index and the set operating parameters based on the neural network model to obtain the target operating parameters to be adjusted, the method further comprises: constructing a neural network model, which comprises the following steps: acquiring sample data, wherein the sample data is subjected to data storage according to an M-by-N column mode, M represents the number of samples, and N represents the number of historical working parameters, air indexes and adjustment data; processing the sample data based on a K mean value clustering algorithm to obtain the sample data with the redundancy removed; and based on the correlation among columns in the column mode, carrying out cluster division on the sample data with the redundancy removed, and constructing to obtain a neural network model.
Optionally, the neural network model is trained to obtain a training result, and the training result is trained by adopting a cross validation method.
Optionally, after obtaining the set operating parameters of the air purifier and the air index of the space in which the air purifier is located, the method further comprises: and normalizing the set working parameters and/or the air index.
Optionally, adjusting the set operating parameter based on the target operating parameter comprises: converting the target working parameters to obtain control data allowing the air purifier to identify; based on the control data, an adjustment command for adjusting the set operating parameter is generated.
According to another aspect of the embodiments of the present application, there is also provided an apparatus for adjusting an operating parameter, the apparatus including: the acquisition module is used for acquiring the set working parameters of the air purifier and the air index in the space where the air purifier is located; the determining module is used for processing the air index and the set working parameters based on a neural network model to obtain target working parameters needing to be adjusted, wherein the neural network model is a model generated by training sample data of the air purifier; and the adjusting module is used for adjusting the set working parameters based on the target working parameters.
According to another aspect of the embodiments of the present application, there is also provided a storage medium including a stored program, wherein when the program is executed, the apparatus on which the storage medium is located is controlled to perform the method for adjusting the operating parameters.
According to another aspect of the embodiments of the present application, there is also provided a processor, configured to execute a program, where the program executes the method for adjusting the operating parameters.
In the embodiment of the application, the set working parameters of the air purifier and the air index in the space where the air purifier is located are obtained; processing the air index and the set working parameters based on a neural network model to obtain target working parameters to be adjusted, wherein the neural network model is a model generated by training sample data of the air purifier; the set operating parameters are adjusted based on the target operating parameters. The automatic air purifier adjusting device has the advantages that the effect of automatically adjusting the air purifier according to the currently obtained operating parameters of the air purifier and the current air index is achieved, user experience is improved, the problem that when a user controls household appliances, the operating state of the household appliances needs to be manually adjusted is solved, the time of the user is wasted, and the user experience is poor.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart diagram of an alternative method of adjusting an operating parameter in accordance with an embodiment of the present application;
fig. 2 is a schematic structural diagram of an alternative device for adjusting an operating parameter according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any inventive step based on the embodiments in the present application, shall fall within the scope of protection of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an aspect of an embodiment of the present application, there is provided a method of adjusting an operating parameter, as shown in fig. 1: the method comprises at least steps S102-S106.
Step S102, acquiring the set working parameters of the air purifier and the air index of the space where the air purifier is located;
in some optional embodiments of the present application, the operating parameters of the air purifier include at least one of: the opening time, the working time and the working power of the air purifier are as follows: the air index in the space where the air purifier is located is an air pollution index, namely, according to the influence of the ambient air quality and various pollutants on human health, ecology and environment, the concentration of the air pollutants which are monitored conventionally is simplified into a single conceptual index value form, the air pollution degree and the air quality condition are represented in a grading way, and the air purifier is suitable for representing the short-term air quality condition and the variation trend of a city. An air quality score is also specified for a single pollutant. The main pollutants participating in the air quality evaluation are fine particulate matters, inhalable particulate matters, sulfur dioxide, carbon dioxide, ozone, carbon monoxide and the like.
In some optional embodiments of the present application, the smart home system obtains the set working parameters of the air purifier, and the air index; the intelligent home system can be any household appliance in the house and can also be other intelligent equipment.
Step S104, processing the air index and the set working parameters based on a neural network model to obtain target working parameters needing to be adjusted, wherein the neural network model is a model generated by training sample data of the air purifier;
in some optional embodiments of the present application, the sample data is data generated by the air purifier operating based on set historical operating parameters and air indexes in a historical time period, wherein the historical operating parameters include at least one of: the set working time, working power, working duration and adjusting data.
In some alternative embodiments of the present application, the historical time period may be set to a time period of the past year, and the adjustment data is the reset operation time, the reset operation power, the reset operation time adjusted from the set operation time and the reset operation power adjusted from the set operation time adjusted from the set. And the adjusted reset working time, working power and working duration still belong to historical data. The adjustment data also includes time information for making adjustments to the historical operating parameters.
After the working parameters of the air purifier which are set and the air index in the space where the air purifier is positioned are obtained, the method further comprises the following steps: and normalizing the set working parameters and/or the air index.
Before the air index and the set working parameters are processed based on the neural network model to obtain the target working parameters to be adjusted, the step of constructing the neural network model is required to be executed, and the step of constructing the neural network model comprises the following steps:
step S1042, sample data is obtained, wherein the sample data is subjected to data storage according to an M-by-N column mode, M represents the number of samples, and N represents the number of historical working parameters, air indexes and adjustment data;
in an alternative embodiment of the present application, the sample data has 7 columns of 365 rows, column 1 is the air index, and columns 2-4 are in the historical operating parameters: the set working time, working power and working duration, and the 5 th to 7 th columns are in the adjusted working parameters: and resetting the working time, the working power and the working duration.
Step S1044, processing the sample data based on the K mean value clustering algorithm to obtain the sample data with the redundancy removed;
in order to avoid the interference of abnormal data in sample data on model training, the sample data needs to be processed based on a K mean value algorithm; in an alternative embodiment of the present application, the sample data is processed by:
in step S10442, the 1-4 columns of data in the ith row are marked as point Pi＝(x1,x2,x3,x4) Dimension is equal to 4, and column 5-7 data is Yi＝(y1，y2，y3). All rows will perform the operation.
In step S10444, at point P where M is 365iSelecting 10 from the K points as initial clustering centers, and sequentially calculating Euclidean distances between the remaining points and the K points
And including the category corresponding to the minimum distance.
And step S10446, after the division is finished, taking the mean value of all the points in each category as a new clustering center, comparing whether the new clustering center and the old clustering center are changed, if not, executing step S10448, and if so, switching to step S10444, and dividing again by using the new clustering center.
Step S10448, after finishing clustering, each point P in the same categoryiCan be regarded as having similar historical working parameters and air index information, so its correspondent regulation data are basically identical, and the Y correspondent to every point in the same category can be calculatediMean value
And standard deviation sigmaiDeleting ones of the class that are not located in
Data within the interval. Where i takes on values of 1, 2, 3.
And S1046, based on the correlation among the columns in the column mode, clustering and dividing the sample data with the redundancy removed, and constructing to obtain a neural network model.
And S1048, training the neural network model to obtain a training result, and training the training result by adopting a cross validation method.
In an alternative embodiment, the cross-validation method comprises the following steps:
in step S10482, the historical operating parameters, the air index, and the adjustment data for the given 365 days are equally divided into 10 parts, which may be approximately equally divided.
And S10484, selecting the ith data as a test set, and inputting the rest K-1 data as a training set into a neural network for training. Inputting a test set after the training is finished, and calculating the accuracy rate lambda of the network trainingi。
And step S10486, repeating the step S10484 until each piece of data is subjected to a test set, and obtaining the corresponding network training accuracy.
Step S10488, calculating the average value of the accuracy rates of the 10 network models
And standard deviation σλ。
Step S104810, judge
And standard deviation σλIf the value is lower than the given threshold value, the training is finished if the value is lower than the given threshold value; otherwise, a set of training set data with the highest accuracy is selected to replace the original data, and the step S10484 is performed.
The air index and the set working parameters are processed based on the neural network model, and the target working parameters to be adjusted can be obtained through the following steps:
a, a first neuron in a neural network model receives an air index and set working parameters to obtain a first processing result;
in some optional embodiments of the present application, the air index is an air index of a space where a current air purifier is located, which is obtained by the smart home system in real time, and the set working parameters are working parameters of the current air purifier, which are obtained by the smart home system in real time. The first processing result is a matrix which forms the air index, the set working time, the working power and the working time length in the set working parameters into a row and four columns.
Step B, performing clustering analysis on the first processing result to obtain a classification result;
and the clustering analysis is to calculate the data correlation in the first processing result by adopting an Euclidean distance mode and classify based on the correlation to obtain a classification result.
Step C, determining the next neuron receiving the first processing result in the neural network model based on the classification result;
step D, sending the first processing result to the next neuron, and processing the first processing result by the next neuron to obtain a second processing result;
and E, circularly executing the step B to the step D based on the second processing result until nodes corresponding to the neural network model are traversed, and generating target working parameters.
And step S106, adjusting the set working parameters based on the target working parameters.
Adjusting the set working parameters based on the target working parameters can be realized by the following steps: converting the target working parameters to obtain control data allowing the air purifier to identify; based on the control data, an adjustment command for adjusting the set operating parameter is generated.
In an optional embodiment, under the condition that different air indexes of the air purifier are obtained, different set working parameters corresponding to the different air indexes are called, so that the neural network model is processed according to the different air indexes and the corresponding working parameters to obtain different target working parameters. For example: the intelligent home system acquires a current air index and set working parameters, and inputs the air index and the set working parameters into the neural network model to acquire target working parameters; after the target working parameters are obtained, the intelligent home system converts the target working parameters, generates an adjusting instruction for adjusting the set working parameters, and sends the adjusting instruction to the air purifier.
In the embodiment of the application, the set working parameters of the air purifier and the air index in the space where the air purifier is located are obtained; processing the air index and the set working parameters based on a neural network model to obtain target working parameters to be adjusted, wherein the neural network model is a model generated by training sample data of the air purifier; the set operating parameters are adjusted based on the target operating parameters. The automatic air purifier adjusting device has the advantages that the effect of automatically adjusting the air purifier according to the currently obtained operating parameters of the air purifier and the current air index is achieved, user experience is improved, the problem that when a user controls household appliances, the operating state of the household appliances needs to be manually adjusted is solved, the time of the user is wasted, and the user experience is poor.
According to an aspect of an embodiment of the present application, there is provided an apparatus for adjusting an operating parameter, as shown in fig. 2: the device comprises: an acquisition module 22, a determination module 24, an adjustment module 26; wherein:
the acquisition module 22 is used for acquiring the set working parameters of the air purifier and the air index of the space where the air purifier is located;
a determining module 24, configured to process the air index and the set working parameters based on a neural network model to obtain target working parameters to be adjusted, where the neural network model is a model generated by training sample data of the air purifier;
and an adjusting module 26 for adjusting the set operating parameter based on the target operating parameter.
It should be noted that, reference may be made to the relevant description of fig. 1 for a preferred implementation of the above-described embodiment, and details are not described here again.
According to an aspect of the embodiments of the present application, there is provided a storage medium, wherein the storage medium includes a stored program, and wherein when the program runs, the apparatus on which the storage medium is located is controlled to execute the above method for adjusting the operating parameters.
According to an aspect of the embodiments of the present application, there is provided a processor, wherein the processor is configured to execute a program, and the program executes the method for adjusting the operating parameters.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit may be a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be provided in one place, or may be distributed over a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A method of adjusting an operating parameter, comprising:
acquiring set working parameters of an air purifier and an air index in a space where the air purifier is located;
processing the air index and the set working parameters based on a neural network model to obtain target working parameters needing to be adjusted, wherein the neural network model is a model generated by training sample data of the air purifier;
adjusting the set working parameters based on the target working parameters;
under the condition that different air indexes of the air purifier are obtained, different working parameters corresponding to the different air indexes are called, so that the neural network model is processed according to the different air indexes and the corresponding working parameters to obtain different target working parameters;
processing the air index and the set working parameters based on a neural network model to obtain target working parameters needing to be adjusted, wherein the target working parameters comprise:
step A, a first neuron in the neural network model receives the air index and the set working parameters to obtain a first processing result;
step B, performing clustering analysis on the first processing result to obtain a classification result;
step C, determining the next neuron in the neural network model for receiving the first processing result based on the classification result;
step D, sending the first processing result to the next neuron, and processing the first processing result by the next neuron to obtain a second processing result;
and E, circularly executing the steps B to D based on the second processing result until nodes corresponding to the neural network model are traversed, and generating the target working parameters.
2. The method of claim 1, wherein the sample data is data generated by the air purifier operating over a historical period of time based on set historical operating parameters and air indices, wherein the historical operating parameters include at least one of: the set working time, working power, working duration and adjusting data.
3. The method according to claim 1, wherein the cluster analysis is to calculate the data correlation in the first processing result by using Euclidean distance method, and classify the first processing result based on the correlation to obtain the classification result.
4. The method of claim 1, wherein prior to processing the air index and the set operating parameters based on a neural network model to obtain target operating parameters to be adjusted, the method further comprises:
constructing the neural network model, wherein the steps comprise:
acquiring the sample data, wherein the sample data is subjected to data storage according to an M-by-N column mode, M represents the number of samples, and N represents the number of historical working parameters, air indexes and adjustment data;
processing the sample data based on a K mean value clustering algorithm to obtain the sample data with the redundancy removed;
and performing cluster division on the sample data with the redundancy removed based on the correlation among the columns in the column mode to construct and obtain the neural network model.
5. The method of claim 4, wherein the neural network model is trained to obtain training results, and wherein the training results are trained using a cross-validation method.
6. The method of claim 1, wherein after obtaining the operating parameters of the air purifier that have been set and the air index of the space in which the air purifier is located, the method further comprises:
and normalizing the set working parameters and/or the air index.
7. The method of claim 1, wherein adjusting the set operating parameter based on the target operating parameter comprises:
converting the target working parameters to obtain control data allowing the air purifier to identify;
and generating an adjusting instruction for adjusting the set working parameters based on the control data.
8. An apparatus for adjusting an operating parameter, comprising:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring set working parameters of the air purifier and an air index in a space where the air purifier is located;
the determining module is used for processing the air index and the set working parameters based on a neural network model to obtain target working parameters needing to be adjusted, wherein the neural network model is a model generated by training sample data of the air purifier;
the adjusting module is used for adjusting the set working parameters based on the target working parameters;
the device for adjusting the working parameters is also used for executing the following steps:
under the condition that different air indexes of the air purifier are obtained, different working parameters corresponding to the different air indexes are called, so that the neural network model is processed according to the different air indexes and the corresponding working parameters to obtain different target working parameters;
the determining module is further configured to perform the steps of:
step A, a first neuron in the neural network model receives the air index and the set working parameters to obtain a first processing result;
step B, performing clustering analysis on the first processing result to obtain a classification result;
step C, determining the next neuron in the neural network model for receiving the first processing result based on the classification result;
step D, sending the first processing result to the next neuron, and processing the first processing result by the next neuron to obtain a second processing result;
and E, circularly executing the steps B to D based on the second processing result until nodes corresponding to the neural network model are traversed, and generating the target working parameters.
9. A storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the storage medium is located to perform the method of adjusting operating parameters of any one of claims 1 to 7.
10. A processor, characterized in that the processor is configured to run a program, wherein the program is configured to perform the method of adjusting operating parameters according to any one of claims 1 to 7 when running.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811141701.1A CN110966731B (en) | 2018-09-28 | 2018-09-28 | Method for regulating operating parameters |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811141701.1A CN110966731B (en) | 2018-09-28 | 2018-09-28 | Method for regulating operating parameters |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110966731A CN110966731A (en) | 2020-04-07 |
| CN110966731B true CN110966731B (en) | 2020-12-22 |
Family
ID=70026965
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811141701.1A Active CN110966731B (en) | 2018-09-28 | 2018-09-28 | Method for regulating operating parameters |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110966731B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113413722B (en) * | 2021-08-05 | 2023-05-30 | 科大讯飞股份有限公司 | Air purification method, purification device and purification system |
| CN114597766B (en) * | 2022-03-15 | 2023-01-03 | 深圳市艾森智控科技有限公司 | Generation parameter adjusting method and system for nano water ions |
| CN114882030A (en) * | 2022-07-11 | 2022-08-09 | 南通金丝楠膜材料有限公司 | Gluing machine working parameter adjusting method and system based on neural network |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05126391A (en) * | 1991-11-07 | 1993-05-21 | Daikin Ind Ltd | Operation controller for air conditioner |
| CN104536473A (en) * | 2014-10-29 | 2015-04-22 | 小米科技有限责任公司 | Control method and device of air purification |
| CN105509227A (en) * | 2015-03-20 | 2016-04-20 | 霍尼韦尔国际公司 | Air purifier and method for air purifier |
| CN105550744A (en) * | 2015-12-06 | 2016-05-04 | 北京工业大学 | Nerve network clustering method based on iteration |
| CN106322656A (en) * | 2016-08-23 | 2017-01-11 | 海信（山东）空调有限公司 | Air conditioner control method, server and air conditioner system |
| CN107664338A (en) * | 2017-09-20 | 2018-02-06 | 中国计量大学 | Smart home air quality monitoring system based on fuzzy neural network |
| CN108030502A (en) * | 2017-07-12 | 2018-05-15 | 深圳联影医疗科技有限公司 | System and method for Air correction |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108154118B (en) * | 2017-12-25 | 2018-12-18 | 北京航空航天大学 | A kind of target detection system and method based on adaptive combined filter and multistage detection |
-
2018
- 2018-09-28 CN CN201811141701.1A patent/CN110966731B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05126391A (en) * | 1991-11-07 | 1993-05-21 | Daikin Ind Ltd | Operation controller for air conditioner |
| CN104536473A (en) * | 2014-10-29 | 2015-04-22 | 小米科技有限责任公司 | Control method and device of air purification |
| CN105509227A (en) * | 2015-03-20 | 2016-04-20 | 霍尼韦尔国际公司 | Air purifier and method for air purifier |
| CN105550744A (en) * | 2015-12-06 | 2016-05-04 | 北京工业大学 | Nerve network clustering method based on iteration |
| CN106322656A (en) * | 2016-08-23 | 2017-01-11 | 海信（山东）空调有限公司 | Air conditioner control method, server and air conditioner system |
| CN108030502A (en) * | 2017-07-12 | 2018-05-15 | 深圳联影医疗科技有限公司 | System and method for Air correction |
| CN107664338A (en) * | 2017-09-20 | 2018-02-06 | 中国计量大学 | Smart home air quality monitoring system based on fuzzy neural network |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110966731A (en) | 2020-04-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110966731B (en) | Method for regulating operating parameters | |
| Ge et al. | Mixture Bayesian regularization method of PPCA for multimode process monitoring | |
| US11216741B2 (en) | Analysis apparatus, analysis method, and non-transitory computer readable medium | |
| Sawatsky et al. | Partial least squares regression in the social sciences | |
| CN109117353B (en) | Fault diagnosis result fusion method and device | |
| JP2008538429A (en) | Model optimization method and system using zeta statistics | |
| CN109858714B (en) | Tobacco shred quality inspection index prediction method, device and system based on improved neural network | |
| CN111174370A (en) | Fault detection method and device, storage medium and electronic device | |
| CN110956277A (en) | Interactive iterative modeling system and method | |
| CN109872262A (en) | A kind of attendance system and method | |
| CN108960486B (en) | Interactive set evolution method for predicting adaptive value based on gray support vector regression | |
| US20190079472A1 (en) | System and method for selecting controllable parameters for equipment operation safety | |
| CN110953721A (en) | Control method and device of water heater and water heater | |
| CN114021665A (en) | Cigarette batch data range reduction time approximation method and device | |
| Muhamad et al. | Criteria selection for an mba programme based on the mahalanobis taguchi system and the kanri distance calculator | |
| US20140052425A1 (en) | Method and apparatus for evaluating a model of an industrial plant process | |
| JP2009175925A (en) | Unit, method and control program for collation parameter optimization | |
| JP2010049392A (en) | Device for tuning pid controller, program for turning pid controller, and method for tuning pid controller | |
| Espinosa | A genetic algorithm for the calibration of a micro-simulation model | |
| CN113313615A (en) | Method and device for quantitatively grading and grading enterprise judicial risks | |
| CN111951033A (en) | House decoration data matching method and decoration management platform | |
| CN114764741A (en) | Method and system for predicting running wind power of wind driven generator | |
| KR101680241B1 (en) | Method for predicting human personality based on data regarding human position having specific data type | |
| Luque et al. | Time series prediction evolving Voronoi regions | |
| CN117371876B (en) | Index data analysis method and system based on keywords |
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 |