CN110736229A - Running state control method and device of air conditioner, processor and air conditioning equipment - Google Patents
Running state control method and device of air conditioner, processor and air conditioning equipment Download PDFInfo
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- CN110736229A CN110736229A CN201911039282.5A CN201911039282A CN110736229A CN 110736229 A CN110736229 A CN 110736229A CN 201911039282 A CN201911039282 A CN 201911039282A CN 110736229 A CN110736229 A CN 110736229A
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- 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
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- 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
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2120/00—Control inputs relating to users or occupants
- F24F2120/10—Occupancy
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Abstract
The invention discloses an air conditioner running state control method, a device, a processor and air conditioner equipment, wherein the method comprises the steps of obtaining a parameter set and a second parameter set in a preset three-dimensional space, wherein the parameter set is used for describing the characteristics of a static object in the preset three-dimensional space, the second parameter set is used for describing the characteristics of a dynamic object in the preset three-dimensional space, and analyzing the parameter set and the second parameter set by using a neural network control model to determine the running state of the air conditioner, wherein the neural network control model is obtained by using multiple groups of data through machine learning training, and each group of data in the multiple groups of data comprises the parameter set, the second parameter set and the running state parameter set of the air conditioner.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to an operation state control method and device of air conditioners, a processor and air conditioning equipment.
Background
Currently, a cooling mode and a heating mode provided by an air conditioner are configured before the air conditioner leaves a factory, and a user generally receives the influence of self-demand, weather factors and the like during the use process, and then sends an instruction to the air conditioner through a remote controller so that the air conditioner works in a specific mode, and the energy saving effect of the air conditioner in the specific mode is determined accordingly.
However, the above control method has the following technical drawbacks: the working mode manually selected by the remote controller lacks consideration of various factors such as indoor structure, indoor distribution condition of placed articles, indoor user number, indoor user residence area and the like, so that the air conditioner is difficult to work in the optimal running state, and the energy-saving effect is poor. For example: no matter in a scene that the indoor space is narrow and the distribution of indoor placed articles is concentrated, or in a scene that the indoor space is wide and the distribution of indoor placed articles is dispersed, the air conditioner can only work in a fixed mode under the triggering of a control command of the remote controller, and can not be combined with different environments to be adaptively adjusted to an optimal operation state.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
At least some embodiments of the present invention provide an operation state control method, an apparatus, a processor, and an air conditioning device for air conditioners, so as to at least solve the technical problem that the air conditioning device provided in the related art can only manually select an operation mode through a remote controller, and cannot adaptively adjust an operation state according to an environmental change.
According to an embodiment of the present invention, there is provided an operation state control method of air conditioners, including:
the method comprises the steps of obtaining an th parameter set and a second parameter set in a preset three-dimensional space, wherein the th parameter set is used for describing the characteristics of a static object in the preset three-dimensional space, the second parameter set is used for describing the characteristics of a dynamic object in the preset three-dimensional space, analyzing the th parameter set and the second parameter set by using a neural network control model, and determining the running state of the air conditioner, wherein the neural network control model is obtained by using multiple groups of data through machine learning training, and each group of data in the multiple groups of data comprises the th parameter set, the second parameter set and the running state parameter set of the air conditioner.
Optionally, the obtaining th parameter set and the second parameter set in the preset three-dimensional space includes receiving th parameter set and the second parameter set sensed by a plurality of sensors deployed in the preset three-dimensional space through an information acquisition component, where the th parameter set includes structural information of the preset three-dimensional space and distribution information of placed articles in the preset three-dimensional space, and the second parameter set includes number information of life bodies and resident area information of the life bodies.
Optionally, the th parameter set and the second parameter set are analyzed by using the neural network control model, and the determining of the operation state includes obtaining a third parameter set corresponding to the th parameter set and the second parameter set, wherein the third parameter set is used for describing attributes of internal components of the air conditioner, setting the third parameter set as input of the neural network control model for analysis, outputting an operation state parameter set, and determining the operation state according to the operation state parameter set.
Optionally, before analyzing the th parameter set and the second parameter set by using the neural network control model and determining the operation states, the method further comprises the steps of configuring multiple operation states for the air conditioner in advance, wherein each operation state in the multiple operation states corresponds to a different parameter combination of the th parameter set and the second parameter set, and configuring a corresponding third parameter set for each operation state in the multiple operation states.
Optionally, after analyzing the th parameter set and the second parameter set by using the neural network control model and determining the operating state, the method further includes obtaining an updated th parameter set and an updated second parameter set in the preset three-dimensional space again, wherein the updated th parameter set is used for describing the changed features of the static object, and the updated second parameter set is used for describing the changed features of the dynamic object, and analyzing the updated th parameter set and the updated second parameter set by using the neural network control model again to adjust the operating state.
According to an embodiment of the present invention, there is also provided an operation state control apparatus of kinds of air conditioners, including:
the device comprises an acquisition module and a control module, wherein the acquisition module is used for acquiring a th parameter set and a second parameter set in a preset three-dimensional space, the th parameter set is used for describing the characteristics of a static object in the preset three-dimensional space, the second parameter set is used for describing the characteristics of a dynamic object in the preset three-dimensional space, and the control module is used for analyzing the th parameter set and the second parameter set by using a neural network control model and determining the running state of the air conditioner, the neural network control model is obtained by using a plurality of groups of data through machine learning training, and each group of data in the plurality of groups of data comprises a th parameter set, the second parameter set and the running state parameter set of the air conditioner.
Optionally, the acquisition module is configured to receive, by the information acquisition component, th parameter sets and second parameter sets sensed by a plurality of sensors deployed in a preset three-dimensional space, where the th parameter set includes structural information of the preset three-dimensional space and distribution information of placed articles in the preset three-dimensional space, and the second parameter set includes number information of living bodies and resident area information of the living bodies.
Optionally, the control module includes an obtaining unit configured to obtain a third parameter set corresponding to the th parameter set and the second parameter set, where the third parameter set is used to describe attributes of internal components of the air conditioner, a processing unit configured to set the third parameter set as an input of the neural network control model for analysis and output an operation state parameter set, and a determining unit configured to determine an operation state according to the operation state parameter set.
Optionally, the apparatus further includes a configuration module configured to configure a plurality of operation states for the air conditioner in advance, where each of the plurality of operation states corresponds to a different parameter combination of the -th parameter set and the second parameter set, and configure a corresponding third parameter set for each of the plurality of operation states.
Optionally, the obtaining module is further configured to obtain an updated th parameter set and an updated second parameter set again in the preset three-dimensional space, where the updated th parameter set is used to describe characteristics of the static object after change, and the updated second parameter set is used to describe characteristics of the dynamic object after change, and the control module is configured to analyze the updated th parameter set and the updated second parameter set by using the neural network control model again, and adjust the operating state.
According to an embodiment of the present invention, there is further provided storage media in which a computer program is stored, wherein the computer program is configured to execute the operation state control method of the air conditioner in any of above when running.
According to an embodiment of the present invention, there are also provided kinds of processors for running a program, wherein the program is configured to execute the operation state control method of the air conditioner of any item described above when running.
According to the invention, wherein embodiment, there is also provided air conditioning equipment, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to execute the method for controlling the operating state of the air conditioner in any item.
In at least some embodiments of the present invention, a mode of acquiring an th parameter set and a second parameter set in a preset three-dimensional space, where the th parameter set is used to describe characteristics of a static object in the preset three-dimensional space and the second parameter set is used to describe characteristics of a dynamic object in the preset three-dimensional space is adopted, and a neural network control model is used to analyze the th parameter set and the second parameter set to determine an operating state of an air conditioner, where the neural network control model is obtained by using multiple sets of data through machine learning training, and each set of data in the multiple sets of data includes the th parameter set, the second parameter set and an operating state parameter set of the air conditioner, so as to achieve a purpose of adaptively adjusting an operating state of the air conditioner according to the acquired characteristics of the static object in the preset three-dimensional space and the characteristics of the dynamic object in the preset three-dimensional space, thereby achieving a technical effect of improving cooling or heating precision, making an operation of the air conditioner more reasonable and energy saving, and further solving a technical problem that an air conditioner provided in related art can only select an operating mode manually through a remote controller, but cannot adaptively adjust an operating state according to environmental change.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and constitute a part of this application , illustrate embodiments of the invention and together with the description serve to explain the invention without limiting it.
Fig. 1 is a flowchart of an operation state control method of an air conditioner according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a neural network control model according to an alternate embodiment of of the present invention;
fig. 3 is a block diagram of an operation state control apparatus of an air conditioner according to an embodiment of the present invention;
fig. 4 is a block diagram of an operation state control apparatus of an air conditioner in which is an alternative embodiment according to the present invention.
Detailed Description
For those skilled in the art to better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a partial embodiment of of the present invention, rather than a complete embodiment.
Furthermore, the terms "comprises" and "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a series of steps or elements of is not necessarily limited to the expressly listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided an embodiment of an operation state control method for air conditioners, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as sets of computer executable instructions and that, while a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than that presented herein.
The air conditioning equipment can comprise or more processors (the processors can comprise but are not limited to a Microprocessor (MCU), a Central Processing Unit (CPU), a Graphic Processing Unit (GPU), a Digital Signal Processing (DSP) chip, a processing device such as a programmable logic device (FPGA)) and a memory for storing data.
The memory may be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to the operation state control method of the air conditioner in the embodiment of the present invention, and the processor executes various functional applications and data processing by executing the computer programs stored in the memory, thereby implementing the operation state control method of the air conditioner as described above.
The transmission device may include Network adapters (NIC) connected to other Network devices through a base station so as to communicate with the internet, in , the transmission device may be a Radio Frequency (RF) module for communicating with the internet in a wireless manner.
, embodiments of an air conditioning device having a Graphical User Interface (GUI) with which a user may interact by touching finger contacts and/or gestures on a touch-sensitive surface, where human interaction functions optionally include activating an air conditioner, deactivating an air conditioner, adjusting a cooling or heating temperature, adjusting an air conditioner operating mode, etc. executable instructions for performing the above-described human interaction functions are configured/stored in a computer program product or readable storage medium executable by or more processors.
The information acquisition equipment is used for receiving parameter information transmitted by a plurality of sensors deployed in a preset three-dimensional space. The present invention does not impose strict restrictions on the deployment locations of multiple sensors. Taking the example of the application of the air conditioning equipment to an intelligent home location, a plurality of sensors can be respectively deployed in a plurality of intelligent household appliances (such as an intelligent desk lamp, an intelligent sound box, an intelligent sofa seat and the like). Types of sensors may include, but are not limited to: microwave radar sensor, laser sensor, photosensitive sensor.
In the present embodiment, operation state control methods of air conditioners operating in the above air conditioning equipment are provided, and fig. 1 is a flowchart of an operation state control method of an air conditioner according to embodiment of the present invention, as shown in fig. 1, the method includes the steps of:
step S11, acquiring a parameter set and a second parameter set in a preset three-dimensional space, wherein the parameter set is used for describing the characteristics of static objects in the preset three-dimensional space, and the second parameter set is used for describing the characteristics of dynamic objects in the preset three-dimensional space;
and S14, analyzing the parameter set and the second parameter set by using a neural network control model, and determining the operating state of the air conditioner, wherein the neural network control model is obtained by using a plurality of groups of data through machine learning training, and each group of data in the plurality of groups of data comprises a parameter set, the second parameter set and the operating state parameter set of the air conditioner.
Through the steps, a mode of acquiring th parameter set and second parameter set in the preset three-dimensional space, wherein the th parameter set is used for describing the characteristics of a static object in the preset three-dimensional space and the second parameter set is used for describing the characteristics of a dynamic object in the preset three-dimensional space, and a neural network control model is used for analyzing the th parameter set and the second parameter set to determine the operating state of the air conditioner, wherein the neural network control model is obtained by using multiple groups of data through machine learning training, each group of the multiple groups of data comprises the th parameter set, the second parameter set and the operating state parameter set of the air conditioner, so that the purpose of adaptively adjusting the operating state of the air conditioner according to the acquired characteristics of the static object in the preset three-dimensional space and the characteristics of the dynamic object in the preset three-dimensional space is achieved, the cooling or heating precision is improved, the technical effect of enabling the air conditioner to operate more reasonably and saving energy is achieved, and the technical problem that the operating state of the air conditioner provided in the related technology can only be manually selected by a remote controller and cannot be adaptively adjusted according to environmental changes is solved.
The preset three-dimensional space may include, but is not limited to: smart home locations, workplaces, and crowded public locations. The preset three-dimensional space can be a fully-closed space or a semi-closed space. The static object refers to an object which is relatively fixed in placement position in a preset three-dimensional space and does not have vital signs. For example: household appliances and furniture. The dynamic object refers to a living body with vital signs, the position of which is possibly changed at any time in a preset three-dimensional space. For example: human and pet.
Optionally, in step S12, the obtaining parameter set and the second parameter set in the preset three-dimensional space may include the following steps:
step S121, receiving th parameter sets and second parameter sets sensed by a plurality of sensors deployed in a preset three-dimensional space through an information acquisition assembly, wherein the th parameter set comprises structure information of the preset three-dimensional space and distribution information of placed articles in the preset three-dimensional space, and the second parameter set comprises number information of life bodies and resident area information of the life bodies.
The parameter set can include, but is not limited to, structural information of the preset three-dimensional space (for example, size information of a living room), distribution information of placed articles in the preset three-dimensional space (for example, placement positions of various intelligent household appliances in the living room), and the second parameter set can include, but is not limited to, information of the number of living bodies (for example, the number of people living in the living room), and information of resident areas of the living bodies (for example, a sofa seat area centrally surrounding a tea table during rest).
Optionally, in step S14, before the step of analyzing the parameter set and the second parameter set by using the neural network control model and determining the operation state, the method may further include the following steps:
step S12, configuring a plurality of operation states for the air conditioner in advance, wherein each operation state in the plurality of operation states corresponds to different parameter combinations of the parameter set and the second parameter set respectively;
and step S13, configuring a corresponding third parameter set for each operation state in the multiple operation states, wherein the third parameter set is used for describing the attributes of the internal components of the air conditioner.
The method comprises the steps that multiple operation states can be configured for the air conditioner in advance according to different parameter combinations of parameter set and second parameter set, namely, multiple operation states are respectively set by comprehensively considering multiple factors such as structure information of a preset three-dimensional space, distribution information of placed articles in the preset three-dimensional space, quantity information of life bodies and resident area information of the life bodies, so that the air conditioner selects an optimal operation state for the combination of parameter set and second parameter set after receiving parameter set and second parameter set through an information acquisition component.
Optionally, in step S14, analyzing the parameter set and the second parameter set using a neural network control model, and determining the operation state may include performing the steps of:
step S141, acquiring a third parameter set corresponding to the th parameter set and the second parameter set;
step S142, setting the third parameter set as the input of the neural network control model for analysis, and outputting an operation state parameter set;
and step S143, determining the operation state according to the operation state parameter set.
Fig. 2 is a schematic diagram of a neural network control model according to an alternative embodiment of the present invention, where as shown in fig. 2, the input layer includes a compressor rotation speed, an expansion valve opening degree, an inner fan rotation speed, the output layer includes a position of an air deflector and an intermediate temperature of an evaporator, and the hidden layer may include layers or multiple layers (only layers are shown in the figure), and the corresponding compressor rotation speed, expansion valve opening degree, and inner fan rotation speed may be obtained by comprehensively considering various factors such as structural information of a preset three-dimensional space, distribution information of placed articles in the preset three-dimensional space, quantity information of life bodies, and resident area information of the life bodies.
Optionally, in step S14, after the step parameter set and the second parameter set are analyzed by using the neural network control model to determine the operation state, the method may further include the following steps:
step S15, re-acquiring an updated parameter set and an updated second parameter set in the preset three-dimensional space, wherein the updated parameter set is used for describing the changed characteristics of the static object, and the updated second parameter set is used for describing the changed characteristics of the dynamic object;
and step S16, analyzing the updated th parameter set and the updated second parameter set by using the neural network control model again, and adjusting the running state.
After the position of the air deflector and the intermediate temperature of the evaporator are output through the neural network control model each time, various factors such as structural information of a preset three-dimensional space, distribution information of placed articles in the preset three-dimensional space, quantity information of life bodies, information of a resident area of the life bodies and the like can be changed continuously, for example, the placed positions of various intelligent household appliances in a living room are changed, the number of the living people in the living room is changed, and the resident area is changed from a sofa seat area which is centrally surrounded around a tea table to a dining chair area which is centrally surrounded around a dining table when a meal is taken at rest.
Based on the understanding that the technical solution of the present invention per se or parts contributing to the prior art can be embodied in the form of software products stored in storage media (such as ROM/RAM, magnetic disk, optical disk) and including instructions for causing terminal devices (which may be mobile phones, computers, servers, or network devices) to execute the methods according to the embodiments of the present invention.
The operation state control device of air conditioners is also provided in the present embodiment, which is used to realize the above-mentioned embodiments and preferred embodiments, and has been explained without further description.
Fig. 3 is a block diagram of an operation state control apparatus of an air conditioner according to an embodiment of the present invention, as shown in fig. 3, the apparatus includes an obtaining module 10 configured to obtain a -th parameter set and a second parameter set in a preset three-dimensional space, where the -th parameter set is used to describe characteristics of a static object in the preset three-dimensional space, and the second parameter set is used to describe characteristics of a dynamic object in the preset three-dimensional space, and a control module 20 configured to analyze the -th parameter set and the second parameter set using a neural network control model to determine an operation state of the air conditioner, where the neural network control model is obtained through machine learning training using multiple sets of data, and each set of the multiple sets of data includes a -th parameter set, the second parameter set, and an operation state parameter set of the air conditioner.
Optionally, the obtaining module 10 is configured to receive, by an information collecting component, -th parameter sets and second parameter sets sensed by a plurality of sensors deployed in a preset three-dimensional space, where the -th parameter set includes structural information of the preset three-dimensional space and distribution information of placed articles in the preset three-dimensional space, and the second parameter set includes number information of living bodies and resident area information of the living bodies.
Optionally, the control module 20 includes an obtaining unit (not shown in the figure) configured to obtain a third parameter set corresponding to the th parameter set and the second parameter set, where the third parameter set is used to describe attributes of internal components of the air conditioner, a processing unit (not shown in the figure) configured to set the third parameter set as an input of the neural network control model for analysis and output an operation state parameter set, and a determining unit (not shown in the figure) configured to determine an operation state according to the operation state parameter set.
Optionally, fig. 4 is a block diagram of an operation state control apparatus of an air conditioner according to an alternative embodiment of the present invention, which includes, as shown in fig. 4, a configuration module 30 configured to configure a plurality of operation states for the air conditioner in advance, wherein each of the plurality of operation states corresponds to a different parameter combination of the parameter set and the second parameter set, and a corresponding third parameter set for each of the plurality of operation states, in addition to all the modules shown in fig. 3.
The obtaining module 10 is further configured to obtain an updated th parameter set and an updated second parameter set again in the preset three-dimensional space, where the updated th parameter set is used to describe characteristics of the static object after change, and the updated second parameter set is used to describe characteristics of the dynamic object after change, and the control module 20 is configured to analyze the updated th parameter set and the updated second parameter set by using the neural network control model again, and adjust the operating state.
It should be noted that the above modules may be implemented by software or hardware, and for the latter, the modules may be implemented by, but are not limited to, being located in the same processor, or being located in different processors in any combination.
An embodiment of the present invention further provides storage media having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s1, acquiring a parameter set and a second parameter set in a preset three-dimensional space, wherein the parameter set is used for describing the characteristics of static objects in the preset three-dimensional space, and the second parameter set is used for describing the characteristics of dynamic objects in the preset three-dimensional space;
and S2, analyzing the parameter set and the second parameter set by using a neural network control model, and determining the operating state of the air conditioner, wherein the neural network control model is obtained by using a plurality of groups of data through machine learning training, and each group of data in the plurality of groups of data comprises a parameter set, the second parameter set and the operating state parameter set of the air conditioner.
Optionally, the storage medium is further configured to store a computer program for executing the following steps of receiving th parameter set and a second parameter set sensed by a plurality of sensors deployed in a preset three-dimensional space through an information acquisition component, wherein the th parameter set comprises structure information of the preset three-dimensional space and distribution information of placed articles in the preset three-dimensional space, and the second parameter set comprises number information of life bodies and resident area information of the life bodies.
Optionally, the storage medium is further configured to store a computer program for executing the steps of obtaining a third parameter set corresponding to the th parameter set and the second parameter set, wherein the third parameter set is used for describing the attributes of the internal components of the air conditioner, setting the third parameter set as the input of the neural network control model for analysis, outputting an operation state parameter set, and determining the operation state according to the operation state parameter set.
Optionally, the storage medium is further configured to store a computer program for configuring a plurality of operating states for the air conditioner in advance, wherein each of the plurality of operating states corresponds to a different -th parameter set and a parameter combination of the second parameter set, respectively, and a corresponding third parameter set is configured for each of the plurality of operating states, respectively.
Optionally, the storage medium is further configured to store a computer program for performing the steps of retrieving an updated th parameter set and an updated second parameter set in the preset three-dimensional space, wherein the updated th parameter set is used for describing the changed features of the static objects, and the updated second parameter set is used for describing the changed features of the dynamic objects, and analyzing the updated th parameter set and the updated second parameter set by using the neural network control model again to adjust the operation state.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide air conditioning apparatuses, comprising a memory having a computer program stored therein and a processor configured to execute the computer program to perform the steps of any of the method embodiments described above.
Optionally, the air conditioning equipment may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, acquiring a parameter set and a second parameter set in a preset three-dimensional space, wherein the parameter set is used for describing the characteristics of static objects in the preset three-dimensional space, and the second parameter set is used for describing the characteristics of dynamic objects in the preset three-dimensional space;
and S2, analyzing the parameter set and the second parameter set by using a neural network control model, and determining the operating state of the air conditioner, wherein the neural network control model is obtained by using a plurality of groups of data through machine learning training, and each group of data in the plurality of groups of data comprises a parameter set, the second parameter set and the operating state parameter set of the air conditioner.
Optionally, the processor may be further configured to execute, by the computer program, the step of receiving, by the information acquisition component, th parameter set and a second parameter set sensed by a plurality of sensors deployed in a preset three-dimensional space, where the th parameter set includes structural information of the preset three-dimensional space and distribution information of placed items in the preset three-dimensional space, and the second parameter set includes information of the number of living bodies and information of resident areas of the living bodies.
Optionally, the processor may be further configured to execute, by a computer program, the steps of obtaining a third parameter set corresponding to the th parameter set and the second parameter set, where the third parameter set is used to describe the attributes of the internal components of the air conditioner, setting the third parameter set as an input of the neural network control model for analysis, outputting an operation state parameter set, and determining an operation state according to the operation state parameter set.
Optionally, the processor may be further configured to execute, via the computer program, the steps of configuring a plurality of operating states for the air conditioner in advance, wherein each of the plurality of operating states corresponds to a different -th parameter set and a parameter combination of the second parameter set, respectively, and configuring a corresponding third parameter set for each of the plurality of operating states, respectively.
Optionally, the processor may be further configured to perform, by using the computer program, the steps of retrieving an updated th parameter set and an updated second parameter set in the preset three-dimensional space, where the updated th parameter set is used to describe the changed features of the static object, and the updated second parameter set is used to describe the changed features of the dynamic object, and analyzing the updated th parameter set and the updated second parameter set by using the neural network control model again to adjust the operating state.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, 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 several embodiments provided in this application, it should be understood that the disclosed technology can be implemented in other manners, wherein the above-described device embodiments are merely illustrative, for example, the division of the units can be logical function divisions, and other divisions can be realized in practice, for example, multiple units or components can be combined or integrated into another systems, or features can be omitted or not executed, in another point, the shown or discussed coupling or direct coupling or communication connection between each other can be through interfaces, indirect coupling or communication connection of units or modules, and can be electric or other forms.
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, that is, may be located in places, or may also be distributed on multiple units.
In addition, the functional units in the embodiments of the present invention may be integrated into processing units, or each unit may exist alone physically, or two or more units are integrated into units.
Based on the understanding, the technical solution of the present invention, which is essentially or partially contributed to by the prior art, or all or part of the technical solution, may be embodied in the form of a software product stored in storage media, which includes several instructions for making computer devices (which may be personal computers, servers, or network devices) execute all or part of the steps of the methods described in the embodiments of the present invention.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (13)
1, A method for controlling the operation state of an air conditioner, comprising:
acquiring th parameter set and a second parameter set in a preset three-dimensional space, wherein the th parameter set is used for describing the characteristics of static objects in the preset three-dimensional space, and the second parameter set is used for describing the characteristics of dynamic objects in the preset three-dimensional space;
analyzing the th parameter set and the second parameter set by using a neural network control model to determine the operating state of the air conditioner, wherein the neural network control model is obtained by using a plurality of groups of data through machine learning training, and each group of data in the plurality of groups of data comprises the th parameter set, the second parameter set and the operating state parameter set of the air conditioner.
2. The method of claim 1, wherein obtaining the th set of parameters and the second set of parameters in the predetermined three-dimensional space comprises:
receiving th parameter set and second parameter set sensed by a plurality of sensors deployed in the preset three-dimensional space through an information acquisition component, wherein the th parameter set comprises structure information of the preset three-dimensional space and distribution information of placed articles in the preset three-dimensional space, and the second parameter set comprises number information of life bodies and resident area information of the life bodies.
3. The method of claim 1, wherein the th and second sets of parameters are analyzed using the neural network control model, and determining the operating state comprises:
acquiring a third parameter set corresponding to the th parameter set and the second parameter set, wherein the third parameter set is used for describing the attributes of the air conditioner internal components;
setting the third parameter set as the input of the neural network control model for analysis, and outputting the running state parameter set;
and determining the operation state according to the operation state parameter set.
4. The method of claim 3, further comprising, prior to analyzing the th and second sets of parameters using the neural network control model to determine the operating condition:
configuring a plurality of operation states for the air conditioner in advance, wherein each operation state in the plurality of operation states corresponds to different parameter combinations of the th parameter set and the second parameter set respectively;
and respectively configuring a corresponding third parameter set for each operation state in the multiple operation states.
5. The method of claim 1, wherein after analyzing the th and second sets of parameters using the neural network control model to determine the operating condition, further comprising:
the updated th parameter set and the updated second parameter set in the preset three-dimensional space are obtained again, wherein the updated th parameter set is used for describing the changed features of the static object, and the updated second parameter set is used for describing the changed features of the dynamic object;
and analyzing the updated th parameter set and the updated second parameter set by using the neural network control model again, and adjusting the operation state.
An operation state control device of air conditioners, comprising:
the acquisition module is used for acquiring an th parameter set and a second parameter set in a preset three-dimensional space, wherein the th parameter set is used for describing the characteristics of static objects in the preset three-dimensional space, and the second parameter set is used for describing the characteristics of dynamic objects in the preset three-dimensional space;
the control module is used for analyzing the th parameter set and the second parameter set by using a neural network control model and determining the operating state of the air conditioner, wherein the neural network control model is obtained by using a plurality of groups of data through machine learning training, and each group of data in the plurality of groups of data comprises the th parameter set, the second parameter set and the operating state parameter set of the air conditioner.
7. The apparatus of claim 6, wherein the obtaining module is configured to receive, through an information collecting component, the th parameter set and the second parameter set sensed by a plurality of sensors deployed in the preset three-dimensional space, wherein the th parameter set includes structural information of the preset three-dimensional space and distribution information of placed items in the preset three-dimensional space, and the second parameter set includes information of number of living bodies and information of resident areas of the living bodies.
8. The apparatus of claim 6, wherein the control module comprises:
the acquisition unit is used for acquiring a third parameter set corresponding to the th parameter set and the second parameter set, wherein the third parameter set is used for describing the attributes of the air conditioner internal components;
the processing unit is used for setting the third parameter set as the input of the neural network control model for analysis and outputting the running state parameter set;
and the determining unit is used for determining the operating state according to the operating state parameter set.
9. The apparatus of claim 8, further comprising:
the configuration module is used for configuring a plurality of operation states for the air conditioner in advance, wherein each operation state in the plurality of operation states corresponds to different parameter combinations of the th parameter set and the second parameter set respectively, and a third parameter set corresponding to each operation state in the plurality of operation states is configured respectively.
10. The apparatus of claim 6,
the obtaining module is further configured to obtain an updated th parameter set and an updated second parameter set again in the preset three-dimensional space, where the updated th parameter set is used to describe a changed feature of the static object, and the updated second parameter set is used to describe a changed feature of the dynamic object;
and the control module is used for analyzing the updated th parameter set and the updated second parameter set by using the neural network control model again and adjusting the running state.
Storage medium of 11, , wherein the storage medium has stored therein a computer program, wherein the computer program is arranged to execute the method of controlling the operating state of an air conditioner according to any of claims 1 to 5 through when running.
12, kinds of processors, characterized in that, the processor is used for running a program, wherein the program is arranged to execute the running state control method of the air conditioner according to any of claims 1 to 5 when running.
air conditioner comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to execute the operation state control method of the air conditioner as claimed in any of claims 1 to 5 .
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