CN109269036B - Cloud control method of multi-split air conditioner and multi-split air conditioner system - Google Patents
Cloud control method of multi-split air conditioner and multi-split air conditioner system Download PDFInfo
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Abstract
The invention provides a cloud control method of a multi-split air conditioner and a multi-split air conditioner system, wherein the control method comprises four steps of constructing an operation database, constructing a multi-dimensional array, constructing an SVM (support vector machine) model, constructing an ANN (artificial neural network) model and constructing a decision tree model, and priority execution parameters meeting the requirements of comfort level and energy conservation are output through the four steps; the multi-split air conditioning system comprises a plurality of indoor units, a cloud line controller, a cloud centralized controller and a cloud end, wherein the cloud line controller is arranged corresponding to each indoor unit and can collect operation parameters set by a user for the indoor units, the cloud centralized controller can collect the operation environment parameters of each indoor unit, and the cloud end can collect and process data information of the cloud line controller and the cloud centralized controller. According to the multi-split air conditioning system, different training models are set, so that the control logic of each indoor unit in a complex multi-split air conditioner is simplified, the real-time performance of the control of the multi-split air conditioning system is higher, and the control is more effective.
Description
Technical Field
The invention belongs to the technical field of air conditioners, and particularly relates to a multi-split air conditioning system.
Background
At present, along with the demand of air conditioning unit intellectuality is higher and higher, leads to the intellectuality of some accessories of air conditioning system also to improve rapidly, like some pronunciation line accuse wares, equipment such as intelligent people, more energy-conserving, comfortable that single unit also upgrades.
The invention discloses an intelligent air conditioner, an intelligent air conditioner temperature automatic adjustment cloud platform and a system, wherein the intelligent air conditioner comprises a first communication module, an intelligent air conditioner control module, an environmental parameter sensing module and a use state sensing module, the environmental parameter sensing module is connected with the first communication module, the use state sensing module is respectively connected with the intelligent air conditioner control module and the first communication module, and the intelligent air conditioner control module is connected with the first communication module.
Through above-mentioned current patent, what it can realize is that the user need not manual regulation, and more convenience of customers is applicable to intelligent air conditioner. However, the method has the disadvantages that the set temperature is adjusted only by detecting the parameter of the ambient temperature, the input quantity is single, and the method is separated from the operation control logic of the unit and has low feasibility.
Disclosure of Invention
In order to solve the problems, the invention provides a cloud control method of a multi-split air conditioner and a multi-split air conditioning system.
In order to achieve the purpose, the invention adopts the technical scheme that:
a cloud control method of a multi-split air conditioner comprises the following steps:
constructing an operation database: collecting operation indexes and corresponding operation parameters of each indoor unit in the multi-split air conditioner and uploading the operation indexes and the corresponding operation parameters to a cloud end to form an operation database;
constructing a multi-dimensional array: the cloud end takes time as a reference axis to establish a multi-dimensional array between the time and each operation index;
constructing an SVM model: constructing an SVM model by taking the multi-dimensional array as a reference, and classifying the operation environment scene of each indoor unit in the SVM model so as to classify the collected operation indexes and parameters of the indoor units into corresponding scenes according to the operation environment of the indoor units;
constructing an ANN model: constructing an ANN model aiming at different scenes formed by each SVM model, and screening out reference operation parameters of the indoor unit through the pre-judgment of the ANN model;
constructing a decision tree model: sending the reference operation parameter to a decision tree model, judging whether the reference operation parameter receives an instruction for adjusting the reference operation parameter within a preset time, if not, outputting the reference operation parameter, and setting the reference operation parameter as a priority execution parameter; otherwise, the adjusted parameter operation parameter is set as the priority execution parameter.
As a further optimization of the present invention, in the step of constructing the SVM model, the following steps are specifically performed: according to different time periods of the operation of the indoor unit, different user set parameters are counted to obtain different unit operation parameters, the operation parameters collected each time are automatically classified, and the operation parameters automatically enter corresponding scenes according to preset operation environment scenes.
As a further optimization of the present invention, in the step of constructing the SVM model, the following steps are specifically performed: the preset operation environment scene is a set containing common characteristic data.
As a further optimization of the present invention, in the step of constructing the ANN model, the following steps are specifically performed: counting previous set operation parameter values of a user, and selecting the set operation parameter value with the maximum set times as a habit parameter; calculating a theoretical operation parameter value as a theoretical parameter according to the current operation environment of the indoor unit; collecting the current set operation parameters of a user; and fitting and correcting the habitual parameters, the theoretical parameters and the set operation parameters to output the reference operation parameters of the indoor unit.
As a further optimization of the present invention, in the step of constructing the decision tree model, the following steps are specifically performed: and judging whether the reference operation parameter receives an adjustment instruction of a user within a preset time, if not, determining that the comfort level is 1, otherwise, determining that the comfort level is 0.
As a further optimization of the present invention, in the step of constructing the decision tree model, the following steps are specifically performed: and judging whether the power consumption of the reference operation parameter and the power consumption of the previous sampling period are reduced preset values or not, if so, determining that the energy-saving coefficient is 1, otherwise, determining that the energy-saving coefficient is 0.
As a further optimization of the present invention, in the step of constructing the decision tree model, the following steps are specifically performed: boundary operation parameters corresponding to different operation indexes are preset in the decision tree model, whether the prior execution parameters are in the boundary operation parameters or not is judged, and if yes, the prior execution parameters are output; otherwise, alarm information is output.
The multi-split air conditioner system comprises a plurality of indoor units, a cloud line controller, a cloud centralized controller and a cloud end, wherein the cloud line controller is arranged corresponding to each indoor unit and can collect operation parameters set by a user for the indoor units, the cloud centralized controller can collect the operation environment parameters of each indoor unit, the cloud end can collect and process data information of the cloud line controller and the cloud centralized controller, and the cloud end is provided with an actuator for executing the cloud end control method of the multi-split air conditioner.
As a further optimization of the present invention, the indoor unit includes a user controller capable of executing a user setting mode, and an intelligent controller capable of executing the cloud control method of the multi-split air conditioner according to any of the above embodiments.
Compared with the prior art, the invention has the advantages and positive effects that:
1. according to the control method of the multi-split air conditioner, different training models are set, the control logic of each indoor unit in a complex multi-split air conditioner is simplified, the control real-time performance of the multi-split air conditioner system is higher, the control is more effective, and therefore the requirement of people for comfort is met;
2. according to the invention, the operation parameters are corrected after the user adjusts the set parameters, so that the improvement on the current requirements of people is realized; meanwhile, the damage of the air conditioner is effectively prevented by controlling the operation parameters within the alarm value; energy conservation is effectively realized by controlling energy consumption.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a model schematic diagram of a control method of a multi-split air conditioner according to the present invention;
fig. 2 is a schematic diagram illustrating an example of a multi-split air conditioning system according to the present invention.
Detailed Description
The invention is described in detail below by way of exemplary embodiments. It should be understood, however, that elements, structures and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.
Referring to fig. 1, the present invention provides a cloud control method for a multi-split air conditioner, including the steps of:
constructing an operation database: collecting operation indexes and corresponding operation parameters of each indoor unit in the multi-split air conditioner and uploading the operation indexes and the corresponding operation parameters to a cloud end to form an operation database;
constructing a multi-dimensional array: the cloud end takes time as a reference axis to establish a multi-dimensional array between the time and each operation index;
constructing an SVM model: constructing an SVM model by taking the multi-dimensional array as a reference, and classifying the operation environment scene of each indoor unit in the SVM model so as to classify the collected operation indexes and parameters of the indoor units into corresponding scenes according to the operation environment of the indoor units;
constructing an ANN model: constructing an ANN model aiming at different scenes formed by each SVM model, and screening out reference operation parameters of the indoor unit through the pre-judgment of the ANN model;
constructing a decision tree model: sending the reference operation parameter to a decision tree model, judging whether the reference operation parameter receives an instruction for adjusting the reference operation parameter within a preset time, if not, outputting the reference operation parameter, and setting the reference operation parameter as a priority execution parameter; otherwise, the adjusted parameter operation parameter is set as the priority execution parameter.
In the above, it should be noted that the operation index of the multi-split air conditioner refers to an index that is set on the air conditioner and is adjustable or controllable by a user, such as temperature, humidity, swing vane position, operation mode, power on/off, and the like, which is not exhaustive, and is based on all indexes that can be realized by the air conditioner in the prior art. Meanwhile, whether the operation indexes are increased or decreased does not influence the execution of the steps in the method, and only the corresponding operation indexes are increased or decreased when the factors are examined. Correspondingly, the operation parameter in the present invention refers to an operation parameter corresponding to a current operation index, and if the operation index is temperature, the user inputs 25 ℃, and then 25 ℃ is the operation parameter.
Specifically, in constructing the multidimensional array, the following table is used as an example: in the table, taking one indoor unit in the multi-split air conditioner as an example, the service condition of the indoor unit is counted, that is, a multi-dimensional array of time points and operation parameters such as set temperature, set air volume, set mode, on-off state and the like is established by taking time as a reference axis.
In the step of constructing the SVM model, the method specifically comprises the following steps: according to different time periods of the operation of the indoor unit, different user set parameters are counted to obtain different unit operation parameters, the operation parameters collected each time are automatically classified, and the operation parameters automatically enter corresponding scenes according to preset operation environment scenes. In the above, the preset operating environment scenario is a set including common characteristic data. If the air volume is set to be the middle wind or the low wind in the refrigeration mode, the temperature change is defined as a scene 1 within 20-26 degrees, the collected operation parameters of the indoor unit are counted, if the operation parameters conform to the scene 1, namely the operation parameters have the common characteristic data in the scene 1, the operation parameters are classified into the scene 1, and then the subsequent steps are continuously carried out in the scene 1.
In the step of constructing the ANN model, the method specifically comprises the following steps: counting previous set operation parameter values of a user, and selecting the set operation parameter value with the maximum set times as a habit parameter; calculating a theoretical operation parameter value as a theoretical parameter according to the current operation environment of the indoor unit; collecting the current set operation parameters of a user; and fitting and correcting the habitual parameters, the theoretical parameters and the set operation parameters to output the reference operation parameters of the indoor unit. In this step, taking the user set temperature T as an example, a habit temperature value T1 according to the user habit counted before is taken as the set temperature with the largest number of times of use of the user before counting, where the habit temperature value T1 is the set temperature with the largest number of times of use of the user before counting; meanwhile, a theoretical temperature value T2 is calculated according to a temperature calculation formula, collected parameters in the current environment and the like, finally, the set temperature T, the habitual temperature value T1 and the theoretical temperature value T2 are subjected to fitting correction to obtain a reference temperature value T3, and the reference temperature value T3 is output to the next step.
In order to meet the comfort requirement of the user and facilitate the comfort adjustment of the user, in the step of constructing the decision tree model, the following steps are specifically performed: and judging whether the reference operation parameter receives an adjustment instruction of a user within a preset time, if not, determining that the comfort level is 1, otherwise, determining that the comfort level is 0. When the reference temperature value T3 is entered into the decision tree model, it is determined whether the user will adjust the temperature value within a preset time period T3, and if so, the user is considered to be unsatisfied with the current temperature, that is, the temperature does not satisfy the comfort requirement of the user; otherwise, the user is considered to agree that the temperature is a temperature that can meet his comfort requirements.
In addition, in order to meet the current requirements for energy conservation and emission reduction, in the step of constructing the decision tree model, the following steps are specifically performed: and judging whether the power consumption of the reference operation parameter and the power consumption of the previous sampling period are reduced preset values or not, if so, determining that the energy-saving coefficient is 1, otherwise, determining that the energy-saving coefficient is 0. When the energy saving coefficient is 0, the user needs to be reminded whether to switch the energy saving operation parameters or not.
In addition, in the step of constructing the decision tree model, specifically, the following steps are performed: boundary operation parameters corresponding to different operation indexes are preset in the decision tree model, whether the prior execution parameters are in the boundary operation parameters or not is judged, and if yes, the prior execution parameters are output; otherwise, alarm information is output. By setting the boundary operation parameters, the air conditioner is effectively prevented from being stopped due to the fact that the operation parameters exceed the boundary values, and user experience is prevented from being influenced.
As shown in fig. 2, the present invention provides a multi-split air conditioning system, which includes a plurality of indoor units, a cloud controller corresponding to each indoor unit and capable of collecting operating parameters set by a user for the indoor unit, a cloud centralized controller capable of collecting operating environment parameters of each indoor unit, and a cloud capable of collecting and processing data information of the cloud controller and the cloud centralized controller, wherein the cloud has an actuator for executing the cloud control method of the multi-split air conditioner in any of the embodiments.
Referring to fig. 2, a household multi-split air conditioning system with four rooms is taken as an example, and the indoor units are a main bedroom indoor unit, a living room indoor unit, a sub-bedroom indoor unit, and a study indoor unit, and the corresponding operating frequency and setting parameters are different because the environment of each indoor unit is different. Each indoor unit corresponds to one cloud line controller, each cloud line controller collects set parameters corresponding to user habits in the indoor unit and uploads the set parameters to the cloud end in real time, and the cloud centralized controller uploads current operating parameters of the indoor unit and current operating parameters of the outdoor unit to the cloud end in real time. The cloud end can process various uploaded data in a classified mode, and meanwhile control strategies are directly issued to the line controller and the centralized controller by combining control logics of all the whole machines, so that energy-saving and comfortable user experience effects are achieved.
In addition, the multi-split air conditioning system has two modes, namely a user adjustment mode for the existing air conditioner and an intelligent mode for correspondingly realizing the intelligent mode, and the users can select the modes according to requirements. Specifically, the indoor unit includes a user controller capable of executing a user setting mode, and an intelligent controller capable of executing the cloud control method of the multi-split air conditioner according to any one of the above embodiments.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (9)
1. A cloud control method of a multi-split air conditioner is characterized by comprising the following steps: the method comprises the following steps:
constructing an operation database: collecting operation indexes and corresponding operation parameters of each indoor unit in the multi-split air conditioner and uploading the operation indexes and the corresponding operation parameters to a cloud end to form an operation database;
constructing a multi-dimensional array: the cloud end takes time as a reference axis to establish a multi-dimensional array between the time and each operation index;
constructing an SVM model: constructing an SVM model by taking the multi-dimensional array as a reference, and classifying the operation environment scene of each indoor unit in the SVM model so as to classify the collected operation indexes and parameters of the indoor units into corresponding scenes according to the operation environment of the indoor units;
constructing an ANN model: constructing an ANN model aiming at different scenes formed by each SVM model, and screening out reference operation parameters of the indoor unit through the pre-judgment of the ANN model;
constructing a decision tree model: sending the reference operation parameter to a decision tree model, judging whether the reference operation parameter receives an instruction for adjusting the reference operation parameter within a preset time, if not, outputting the reference operation parameter, and setting the reference operation parameter as a priority execution parameter; otherwise, the adjusted parameter operation parameter is set as the priority execution parameter.
2. The cloud control method of a multi-split air conditioner according to claim 1, wherein: in the step of constructing the SVM model, the method specifically comprises the following steps: according to different time periods of the operation of the indoor unit, different user set parameters are counted to obtain different unit operation parameters, the operation parameters collected each time are automatically classified, and the operation parameters automatically enter corresponding scenes according to preset operation environment scenes.
3. The cloud control method of a multi-split air conditioner according to claim 2, wherein: in the step of constructing the SVM model, the method specifically comprises the following steps: the preset operation environment scene is a set containing common characteristic data.
4. The cloud control method of a multi-split air conditioner according to claim 1, wherein: in the step of constructing the ANN model, the method specifically comprises the following steps: counting previous set operation parameter values of a user, and selecting the set operation parameter value with the maximum set times as a habit parameter; calculating a theoretical operation parameter value as a theoretical parameter according to the current operation environment of the indoor unit; collecting the current set operation parameters of a user; and fitting and correcting the habitual parameters, the theoretical parameters and the set operation parameters to output the reference operation parameters of the indoor unit.
5. The cloud control method of a multi-split air conditioner according to claim 1, wherein: in the step of constructing the decision tree model, the steps are specifically as follows: and judging whether the reference operation parameter receives an adjustment instruction of a user within a preset time, if not, determining that the comfort level is 1, otherwise, determining that the comfort level is 0.
6. The cloud control method of a multi-split air conditioner according to claim 1, wherein: in the step of constructing the decision tree model, the steps are specifically as follows: and judging whether the power consumption of the reference operation parameter and the power consumption of the previous sampling period are reduced preset values or not, if so, determining that the energy-saving coefficient is 1, otherwise, determining that the energy-saving coefficient is 0.
7. The cloud control method of a multi-split air conditioner according to claim 1, wherein: in the step of constructing the decision tree model, the steps are specifically as follows: boundary operation parameters corresponding to different operation indexes are preset in the decision tree model, whether the prior execution parameters are in the boundary operation parameters or not is judged, and if yes, the prior execution parameters are output; otherwise, alarm information is output.
8. The utility model provides a many online air conditioning system, includes a plurality of indoor sets, its characterized in that: a cloud line controller which can collect the set operation parameters of the indoor unit set by a user, a cloud centralized controller which can collect the operation environment parameters of each indoor unit, and a cloud end which can collect and process the data information of the cloud line controller and the cloud centralized controller are arranged corresponding to each indoor unit, wherein the cloud end is provided with an actuator for executing the cloud end control method of the multi-split air conditioner in any one of claims 1 to 7.
9. A multi-split air conditioning system as recited in claim 8, wherein: the indoor unit comprises a user controller capable of executing a user setting mode and an intelligent controller capable of executing the cloud control method of the multi-split air conditioner as claimed in any one of claims 1 to 7.
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| CN112178861B (en) * | 2020-09-28 | 2021-08-27 | 珠海格力电器股份有限公司 | Control method and device of gymnasium air conditioner, controller and air conditioning system |
| CN113587389A (en) * | 2021-07-21 | 2021-11-02 | 青岛海信日立空调系统有限公司 | Air conditioning system |
| CN113983652A (en) * | 2021-11-05 | 2022-01-28 | 万合智联(浙江)科技有限公司 | Energy-saving consumption-reducing central air conditioner management method and system |
| CN113917211A (en) * | 2021-11-05 | 2022-01-11 | 万合智联(浙江)科技有限公司 | Central air-conditioning charging and distributing system |
| CN116336625B (en) * | 2023-05-18 | 2023-08-18 | 广州永昱节能科技股份有限公司 | Central air conditioning cold station group control energy-saving control method, device, equipment and storage medium |
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