Air conditioning equipment, parameter prediction method thereof and computer storage medium

Abstract

The invention discloses air conditioning equipment, a parameter prediction method of the air conditioning equipment and a computer storage medium, and relates to the technical field of air conditioning equipment control. The parameter prediction method of the air conditioning equipment comprises the steps that working parameters of a working space where the current air conditioning equipment is located are obtained, the working parameters comprise environment parameters and / or operation parameters; inputting the obtained working parameters into a preset parameter prediction model to obtain an environmental parameter prediction value of the next period; and taking the environmental parameter prediction value as a new working parameter, and returning to execute the operation of inputting the obtained working parameter into the preset parameter prediction model to obtain the environmental parameter prediction value of the next period. The problem that the user experience is poor due to the fact that the user cannot know the environment parameter change condition in the operation process of the air conditioning equipment in advance is solved.

Description

Technical Field

[0001] This application relates to the technical field of air conditioning equipment control, and particularly to an air conditioning equipment, a parameter prediction method thereof, and a computer storage medium. Background Art

[0002] Currently, during the process of users using air conditioning equipment, users usually only know in advance the current set working parameters, working modes of the air conditioning equipment, and the environmental parameters that the working space where the air conditioning equipment is located finally needs to reach, etc., but cannot know in advance the changes in environmental parameters during the operation of the air conditioning equipment, which results in a poor user experience. Summary of the Invention

[0003] The main purpose of this application is to provide a parameter prediction method, device, air conditioning equipment, and computer storage medium for air conditioning equipment, aiming to solve the technical problem that due to users' inability to know in advance the changes in environmental parameters during the operation of the air conditioning equipment, the user experience is poor.

[0004] To achieve the above purpose, this application provides a parameter prediction method for air conditioning equipment, and the parameter prediction method for air conditioning equipment includes:

[0005] Obtain the working parameters of the working space where the current air conditioning equipment is located, and the working parameters include environmental parameters and / or operating parameters;

[0006] Input the obtained working parameters into a preset parameter prediction model to obtain the predicted value of the environmental parameters for the next cycle;

[0007] Use the predicted value of the environmental parameters as the new working parameters, and return to execute the step of inputting the obtained working parameters into a preset parameter prediction model to obtain the predicted value of the environmental parameters for the next cycle.

[0008] Optionally, the parameter prediction method for air conditioning equipment further includes:

[0009] Input the working parameters of each cycle into a preset energy consumption prediction model to obtain the predicted value of the energy consumption for each cycle.

[0010] Optionally, the parameter prediction method for air conditioning equipment further includes:

[0011] Display the predicted value of the energy consumption obtained for each cycle.

[0012] Optionally, the parameter prediction model includes a temperature prediction model and / or a humidity prediction model.

[0013] Optionally, the parameter prediction method for air conditioning equipment further includes:

[0014] Display the predicted values of the environmental parameters for each obtained cycle.

[0015] Optionally, the parameter prediction method for the air conditioning device further includes:

[0016] Obtain the actual values of the environmental parameters collected by the environmental sensors in the current cycle, display the actual values of the environmental parameters, and add the actual values of the environmental parameters in the current cycle to the preset energy consumption prediction model.

[0017] Optionally, the parameter prediction method for the air conditioning device further includes:

[0018] Obtain the actual energy consumption values collected by the power module in the current cycle, display the actual energy consumption values, and add the actual energy consumption values in the current cycle to the preset parameter prediction model.

[0019] Optionally, the parameter prediction method for the air conditioning device further includes:

[0020] When the preset time arrives or the operating mode is switched, re-execute the step of obtaining the working parameters of the working space where the current air conditioning device is located.

[0021] This application also provides a parameter prediction device for an air conditioning device, and the parameter prediction device for the air conditioning device includes:

[0022] An acquisition module, configured to acquire the working parameters of the working space where the current air conditioning device is located, where the working parameters include environmental parameters and / or operating parameters;

[0023] A prediction module, configured to input the acquired working parameters into a preset parameter prediction model to obtain predicted values of environmental parameters for the next cycle;

[0024] A loop module, configured to use the predicted values of the environmental parameters as new working parameters, and return to execute the step of inputting the acquired working parameters into a preset parameter prediction model to obtain predicted values of environmental parameters for the next cycle.

[0025] This application also provides an air conditioning device, where the air conditioning device is a physical device, and the air conditioning device includes a memory, a processor, and a parameter prediction program for the air conditioning device that is stored in the memory and can run on the processor. When the control program is executed by the processor, the steps of the parameter prediction method for the air conditioning device as described above are implemented.

[0026] This application also provides a computer storage medium, where the computer storage medium stores an operating program for a smart home system that can run on a processor. The operating program is called by the processor to implement the steps of the parameter prediction method for the air conditioning device as described above.

[0027] The present application also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the parameter prediction method for the air conditioning device as described above.

[0028] The present application provides a parameter prediction method for an air conditioning device. First, the present application obtains the working parameters of the working space where the current air conditioning device is located, and the working parameters include environmental parameters and / or operating parameters. Then, the working parameters are input into a preset parameter prediction model to obtain the predicted value of the environmental parameters for the next cycle. Next, the predicted value of the environmental parameters is used as the new working parameters, and the step of inputting the obtained working parameters into the preset parameter prediction model to obtain the predicted value of the environmental parameters for the next cycle is returned for execution.

[0029] Therefore, the present application continuously uses the predicted value of the environmental parameters obtained by prediction as the new working parameters and inputs them into the parameter prediction model, so as to realize the prediction of the environmental parameters for each cycle through the continuous input and output of the parameter prediction model. Moreover, since the predicted environmental parameters for the next cycle each time are predicted based on the environmental parameters predicted in the current cycle (i.e., the previous cycle relative to the next cycle), the prediction accuracy of the environmental parameters can also be ensured to a certain extent. Thus, the present application continuously and highly accurately predicts the environmental parameters through the parameter prediction model, enabling the user to know in advance the change situation of the environmental parameters during the operation of the air conditioning device, and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0032] Figure 1 It is a schematic flowchart provided for the first embodiment of the parameter prediction method for the air conditioning device of the present application;

[0033] Figure 2 It is a schematic diagram showing the relationship between power consumption and time provided for the first embodiment of the present application;

[0034] Figure 3 It is a schematic diagram showing the relationship between environmental temperature, environmental humidity and time provided for the first embodiment of the present application;

[0035] Figure 4Schematic diagram of the module structure of the parameter prediction device for the air conditioning equipment according to the embodiment of the present application;

[0036] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the parameter prediction method of the air conditioning equipment according to the embodiment of the present application.

[0037] The realization of the purpose, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0038] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] Currently, during the process of users using air conditioning equipment, users usually can only know in advance the working parameters, working modes set by the air conditioning equipment at present, and the environmental parameters that the working space where the air conditioning equipment is located finally needs to reach, etc., but cannot know in advance the changes in environmental parameters during the operation of the air conditioning equipment, which results in a poor user experience.

[0040] Based on this, the present application proposes a parameter prediction method for the air conditioning equipment in the first embodiment. Please refer to Figure 1 , the parameter prediction method of the air conditioning equipment includes:

[0041] Step S10, obtaining the working parameters of the working space where the current air conditioning equipment is located, where the working parameters include environmental parameters and / or operating parameters;

[0042] In this embodiment, the air conditioning equipment can be the execution subject of the parameter prediction method of the air conditioning equipment. The air conditioning equipment refers to the equipment that processes the air in the working space to keep the set temperature, humidity in the working space and control the content of dust and harmful gases in the working space. The air conditioning equipment can be an air conditioner, an air dehumidifier, an air humidifier, etc. This embodiment does not make specific limitations on this.

[0043] It should be noted that the working parameters may include environmental parameters and / or operating parameters, etc. The environmental parameters can be used to characterize the relevant parameters in the working space that will be affected by the air conditioning device. The environmental parameters can include environmental temperature, environmental humidity, etc.; the operating parameters are used to characterize the device-related parameters during the operation of the air conditioning device. The operating parameters can include operating frequency, fan speed, etc. The operating frequency refers to the frequency at which the compressor of the air conditioning device operates, and the fan speed refers to the speed at which the internal fan of the air conditioning device operates.

[0044] When obtaining the working parameters of the working space where the current air conditioning device is located, it can be obtained from the air conditioning device or from other devices connected to the air conditioning device. This embodiment does not make specific limitations on this.

[0045] For the initial working parameters, that is, the working parameters initially input to the parameter prediction model, they can be the working parameters collected by sensors or the working parameters input by users. This embodiment also does not make specific limitations on this.

[0046] Step S20: Input the obtained working parameters into a preset parameter prediction model to obtain the predicted value of the environmental parameters for the next cycle;

[0047] It should be noted that the parameter prediction model is used to predict environmental parameters. The training samples can be composed of historical environmental parameters and historical operating parameters involved in the previous operation of the air conditioning device, and then the parameter prediction model can be constructed through these training samples; it is also possible to combine the simulated environmental parameters, simulated operating parameters, and simulated energy consumption involved in the simulated operation process by simulating the operation of the air conditioning device, and then construct the parameter prediction model through these training samples. This embodiment does not make specific limitations on the construction method of the parameter prediction model.

[0048] In addition, it should be noted that the parameter prediction model can include a temperature prediction model and / or a humidity prediction model. The temperature prediction model is used to predict the environmental temperature, and the humidity prediction model is used to predict the environmental humidity.

[0049] Step S30: Use the predicted value of the environmental parameters as the new working parameters, and return to execute the step of inputting the obtained working parameters into a preset parameter prediction model to obtain the predicted value of the environmental parameters for the next cycle.

[0050] Before using the predicted value of the environmental parameters as the new working parameters to predict the environmental parameters for the next cycle, to ensure the accuracy of the prediction, the accuracy of the currently predicted value of the environmental parameters can be detected first. The specific detection method can be as follows:

[0051] As an example, the prediction parameter difference between the working parameters currently input to the parameter prediction model and the predicted value of the environmental parameters for the next cycle currently predicted by the parameter prediction model can be determined first; if it is detected that the prediction parameter difference is within the parameter change amplitude range, it indicates that the accuracy of the currently predicted value of the environmental parameters is relatively high, and then the predicted value of the environmental parameters for the next cycle currently predicted can be used as the new working parameter to normally perform subsequent difference prediction operations; if it is detected that the prediction parameter difference is outside the parameter change amplitude range, it indicates that the accuracy of the currently predicted value of the environmental parameters is relatively low, and then the maximum parameter change amplitude or the minimum parameter change amplitude within the parameter change amplitude range can be used as the target parameter change amplitude; the difference between the working parameters currently input to the parameter prediction model and the target parameter change amplitude is determined as the new working parameter. This example does not specifically limit the method for detecting the accuracy of the predicted value of the environmental parameters.

[0052] As an example, the method for determining the parameter change amplitude range can be as follows: First, determine the parameter difference between the initial environmental parameters and the environmental parameters that need to be finally achieved; then determine the ratio of this parameter difference to the prediction cycle to obtain the parameter change amplitude; then, based on this parameter change amplitude and the preset parameter offset value, calculate the maximum parameter change amplitude and the minimum parameter change amplitude; then, based on the minimum parameter change amplitude and the maximum parameter change amplitude, generate the parameter change amplitude range. In other examples, the parameter change amplitude range can also be set by default. This example does not specifically limit the method for determining the parameter change amplitude range.

[0053] As an example, the step of using the maximum parameter change amplitude or the minimum parameter change amplitude within the parameter change amplitude range as the target parameter change amplitude can include: determining the smaller value among the differences between the maximum parameter change amplitude, the minimum parameter change amplitude within the parameter change amplitude range, and the working parameters; when the difference between the maximum parameter change amplitude and the working parameters is small, then the maximum parameter change amplitude is used as the target parameter change amplitude; when the difference between the minimum parameter change amplitude and the working parameters is small, then the minimum parameter change amplitude is used as the target parameter change amplitude. In other examples, it can also be set to default to select a certain parameter change amplitude as the target parameter change amplitude, that is, default to select the maximum parameter change amplitude within the parameter change amplitude range as the target parameter change amplitude, or default to select the minimum parameter change amplitude within the parameter change amplitude range as the target parameter change amplitude. This example does not specifically limit the method for determining the target parameter change amplitude.

[0054] An embodiment of the present application provides a method for predicting parameters of an air conditioning device. First, the working parameters of the working space where the current air conditioning device is located are obtained, and the working parameters include environmental parameters and / or operating parameters. Then, the working parameters are input into a preset parameter prediction model to obtain a predicted value of the environmental parameters for the next cycle. Next, the predicted value of the environmental parameters is used as the new working parameters, and the step of inputting the obtained working parameters into the preset parameter prediction model to obtain the predicted value of the environmental parameters for the next cycle is returned and executed.

[0055] Therefore, in the embodiment of the present application, by continuously using the predicted value of the environmental parameters obtained by prediction as the new working parameters and inputting them into the parameter prediction model, the prediction of the environmental parameters for each cycle is realized through the continuous input and output of the parameter prediction model. Moreover, since the predicted environmental parameters for the next cycle each time are predicted based on the environmental parameters predicted in the current cycle (i.e., the previous cycle relative to the next cycle), the prediction accuracy of the environmental parameters can be ensured to a certain extent. Thus, in the embodiment of the present application, the parameter prediction model is used to continuously and accurately predict the environmental parameters, enabling the user to know in advance the changes in the environmental parameters during the operation of the air conditioning device, and improving the user experience.

[0056] In a possible implementation manner, the method for predicting parameters of the air conditioning device further includes:

[0057] Step S101: Input the working parameters of each cycle into a preset energy consumption prediction model to obtain the predicted value of the energy consumption for each cycle.

[0058] It should be noted that the energy consumption prediction model is used to predict the energy consumption of the air conditioning device. The training samples can be composed of historical environmental parameters, historical operating parameters, and historical energy consumption involved in the previous operation of the air conditioning device, and then the energy consumption prediction model is constructed through these training samples; alternatively, by simulating the operation of the air conditioning device, the simulated environmental parameters, simulated operating parameters, and simulated energy consumption involved in the simulated operation process are combined to obtain the training samples, and then the energy consumption prediction model is constructed through these training samples. The present embodiment does not specifically limit the construction method of the energy consumption prediction model.

[0059] In this embodiment, the prediction of the energy consumption of the air conditioning device for each cycle is realized through the energy consumption prediction model, so that the user can know in advance the energy consumption of the air conditioning device during operation. Therefore, for users with power-saving requirements, they can adjust the operating parameters of the air conditioning device in a timely manner based on the energy consumption of the air conditioning device that they know in advance, so as to further improve the user experience.

[0060] Further, the parameter prediction method for the air conditioning equipment further includes:

[0061] Step S102, display the predicted energy consumption values for each period obtained.

[0062] It should be noted that when displaying the predicted energy consumption values for each period obtained, it can be displayed in the form of a table, or in the form of a curve graph, or in other display forms. This embodiment does not specifically limit the specific display form of the predicted energy consumption values for each period.

[0063] Exemplarily, when displaying the predicted energy consumption values for each period obtained in the form of a curve graph, a schematic diagram of the relationship between the cumulative value of the power consumption of all periods before the current period and time as shown in Figure 2 can be obtained. From the curve shown in Figure 2 , it can be seen that as time increases, the power consumption of the air conditioning equipment is also continuously increasing.

[0064] In this embodiment, by displaying the predicted energy consumption values for each period obtained, the user can more intuitively understand the energy consumption situation generated by the air conditioning equipment during future operation, so as to improve the user's intuitive use experience.

[0065] Further, the parameter prediction method for the air conditioning equipment further includes:

[0066] Step S103, obtain the actual environmental parameter values collected by the environmental sensor in the current period, display the actual environmental parameter values, and add the actual environmental parameter values of the current period to the preset energy consumption prediction model.

[0067] It should be noted that the environmental sensor is used to collect the environmental parameters of the working space where the air conditioning equipment is located. The actual environmental parameter value refers to the environmental parameter value of the working space actually affected by the air conditioning equipment. The actual environmental parameter value can include the actual environmental temperature value, the actual environmental humidity value, etc. This embodiment does not specifically limit this.

[0068] The number of environmental sensors can be one or multiple. This embodiment does not specifically limit this. When the number of environmental sensors is multiple, the mode or average value of the environmental parameter values collected by each environmental sensor can be used as the actual environmental parameter value.

[0069] In this embodiment, by obtaining the actual values of the environmental parameters collected by the environmental sensors in the current cycle, and through displaying the actual values of the collected environmental parameters, the user can intuitively observe the changes in the environmental parameters of the working space actually affected by the air conditioning equipment. Moreover, by adding the actual values of the collected environmental parameters to the preset energy consumption prediction model, the energy consumption prediction model is corrected by the actual values of the environmental parameters, so as to improve the prediction accuracy of the energy consumption prediction model.

[0070] In a possible implementation manner, the parameter prediction method of the air conditioning equipment further includes:

[0071] Step S40: Display the predicted values of the environmental parameters in each cycle obtained.

[0072] It should be noted that when displaying the predicted values of the environmental parameters in the obtained cycle, it can be displayed in the form of a table, or in the form of a curve graph, or in other display forms. This embodiment does not specifically limit the specific display form of the predicted values of the environmental parameters in each cycle.

[0073] Exemplarily, when displaying the predicted values of the environmental parameters in each cycle obtained in the form of a curve graph, a schematic diagram of the relationship between the environmental temperature, environmental humidity and time in all cycles before the current cycle as shown in Figure 3 can be obtained. From the Figure 3 displayed environmental temperature curve and environmental humidity curve, it can be seen that as time increases, the environmental temperature and environmental humidity in the working space where the air conditioning equipment is located are continuously decreasing.

[0074] In this embodiment, by displaying the predicted values of the environmental parameters in each cycle obtained, the user can more intuitively understand the changes in the environmental parameters of the air conditioning equipment during future operation, so as to enhance the user's intuitive use experience.

[0075] Furthermore, the parameter prediction method of the air conditioning equipment further includes:

[0076] Step S50: Obtain the actual energy consumption value collected by the power module in the current cycle, display the actual energy consumption value, and add the actual energy consumption value in the current cycle to the preset parameter prediction model.

[0077] It should be noted that the power module is used to collect the energy consumption value of the air conditioning equipment. The power module can be an energy consumption sensor, an energy consumption collector or other devices that can collect the energy consumption value of the air conditioning equipment. The actual energy consumption value refers to the actual energy consumption value generated by the air conditioning equipment during operation.

[0078] In this embodiment, by obtaining the actual energy consumption value collected by the current cycle power module and displaying the collected actual energy consumption value, users can intuitively observe the actual energy consumption generated by the air conditioning equipment during operation. And by adding the collected actual energy consumption value to a preset parameter prediction model, the parameter prediction model can be corrected with the actual energy consumption value to improve the prediction accuracy of the parameter prediction model.

[0079] In a possible implementation manner, the parameter prediction method for the air conditioning equipment further includes:

[0080] Step S01, when the preset time arrives or the operation mode is switched, re-execute the operation of obtaining the working parameters of the working space where the current air conditioning equipment is located.

[0081] It can be understood that when the preset time arrives, it means that the air conditioning equipment has completed the operation of the set operation parameters or has reached the time when the air conditioning equipment starts to operate. Subsequently, the air conditioning equipment may enter a stable operation stage or operate according to the newly set operation parameters. Therefore, it is necessary to re-obtain the working parameters of the working space where the current air conditioning equipment is located to re-predict the change of environmental parameters. When the operation mode is switched, it means that the air conditioning equipment will operate in a new operation mode, that is, the air conditioning equipment will operate with new operation parameters. Subsequently, the change of the environmental parameters in the working space where the air conditioning equipment is located will also change. Therefore, it is also necessary to re-obtain the working parameters of the working space where the current air conditioning equipment is located to re-predict the change of environmental parameters.

[0082] The embodiment of the present application also provides a parameter prediction device for an air conditioning equipment. Please refer to Figure 4 , the parameter prediction device for the air conditioning equipment includes:

[0083] An acquisition module 10, configured to acquire the working parameters of the working space where the current air conditioning equipment is located, where the working parameters include environmental parameters and / or operation parameters;

[0084] A prediction module 20, configured to input the acquired working parameters into a preset parameter prediction model to obtain a predicted value of the environmental parameters for the next cycle;

[0085] A loop module 30, configured to use the predicted value of the environmental parameters as new working parameters, and return to execute the operation of inputting the acquired working parameters into the preset parameter prediction model to obtain a predicted value of the environmental parameters for the next cycle.

[0086] Optionally, the parameter prediction device for the air conditioning equipment further includes:

[0087] Input the working parameters of each cycle into a preset energy consumption prediction model to obtain the energy consumption prediction value of each cycle.

[0088] Optionally, the parameter prediction device of the air conditioning equipment further includes:

[0089] Display the obtained energy consumption prediction value of each cycle.

[0090] Optionally, the parameter prediction model includes a temperature prediction model and / or a humidity prediction model.

[0091] Optionally, the parameter prediction device of the air conditioning equipment further includes:

[0092] Display the obtained environmental parameter prediction values of each cycle.

[0093] Optionally, the parameter prediction device of the air conditioning equipment further includes:

[0094] Obtain the actual value of the environmental parameter collected by the environmental sensor in the current cycle, display the actual value of the environmental parameter, and add the actual value of the environmental parameter in the current cycle to the preset energy consumption prediction model.

[0095] Optionally, the parameter prediction device of the air conditioning equipment further includes:

[0096] Obtain the actual energy consumption value collected by the power consumption module in the current cycle, display the actual energy consumption value, and add the actual energy consumption value in the current cycle to the preset parameter prediction model.

[0097] Optionally, the parameter prediction device of the air conditioning equipment further includes:

[0098] When the preset time arrives or the operating mode is switched, re-execute the step of obtaining the working parameters of the working space where the current air conditioning equipment is located.

[0099] The parameter prediction device of the air conditioning equipment provided by the present invention adopts the parameter prediction method of the air conditioning equipment in the above embodiment, which can solve the technical problem that the user experience is poor because the user cannot know in advance the change of the environmental parameters during the operation of the air conditioning equipment. Compared with the prior art, the beneficial effects of the parameter prediction device of the air conditioning equipment provided by the embodiments of the present application are the same as those of the parameter prediction method of the air conditioning equipment provided by the above embodiment, and other technical features in the parameter prediction device of the air conditioning equipment are the same as those disclosed in the above embodiment method, and will not be elaborated here.

[0100] An embodiment of the present application provides an air conditioning device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the parameter prediction method of the air conditioning device in the above embodiment.

[0101] Reference is made below Figure 5 , which shows a schematic structural diagram of an air conditioning device suitable for implementing the embodiments of the present disclosure. The air conditioning device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The air conditioning device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present disclosure.

[0102] As Figure 5 shown, the air conditioning device may include a processing device 101 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 102 or a program loaded from a storage device 103 into a random access memory (RAM: Random Access Memory) 104. In the RAM 104, various programs and data required for the operation of the air conditioning device are also stored. The processing device 101, the ROM 102, and the RAM 104 are connected to each other through a bus 105. An input / output (I / O) interface 106 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 106: an input device 107 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 108 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 103 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 109. The communication device 109 may allow the air conditioning device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows an air conditioning device with various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems may be alternatively implemented or had.

[0103] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program code for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 103, or installed from a ROM 102. When the computer program is executed by a processing device 101, the above functions defined in the method of the embodiment of the present disclosure are performed.

[0104] The air conditioning device provided by the present invention adopts the parameter prediction method of the air conditioning device in the above embodiment, and can solve the technical problem that the user experience is poor because the user cannot know in advance the change of environmental parameters during the operation of the air conditioning device. Compared with the prior art, the beneficial effects of the air conditioning device provided by the embodiment of the present application are the same as those of the parameter prediction method of the air conditioning device provided by the above embodiment, and other technical features in the air conditioning device are the same as those disclosed in the method of the previous embodiment, and will not be described in detail here.

[0105] It should be understood that each part of the present disclosure can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0106] As described above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all of them should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0107] The embodiment of the present application provides a computer storage medium, which stores a running program of a smart home system that can run on a processor, and the computer-readable program instructions are used to execute the parameter prediction method of the air conditioning device in the above embodiment.

[0108] The computer storage medium provided by the embodiments of the present application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of the computer storage medium may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or component. The program code contained on the computer storage medium can be transmitted by any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0109] The above computer storage medium can be included in an air conditioning device; or it can exist independently without being assembled into the air conditioning device.

[0110] The above computer storage medium carries one or more programs. When the one or more programs are executed by the air conditioning device, the air conditioning device is caused to: obtain the working parameters of the working space where the current air conditioning device is located, the working parameters including environmental parameters and / or operating parameters; input the obtained working parameters into a preset parameter prediction model to obtain a predicted value of the environmental parameters for the next cycle; use the predicted value of the environmental parameters as new working parameters, and return to execute the step of inputting the obtained working parameters into the preset parameter prediction model to obtain a predicted value of the environmental parameters for the next cycle.

[0111] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0112] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0113] The modules described in the embodiments of the present disclosure may be implemented in software or in hardware. In some cases, the name of the module does not constitute a limitation on the unit itself.

[0114] The readable storage medium provided by the present invention is a computer storage medium. The computer storage medium stores computer-readable program instructions for performing the above-mentioned parameter prediction method of the air conditioning device, and can solve the technical problem that the user experience is poor because the user cannot know in advance the change of environmental parameters during the operation of the air conditioning device. Compared with the prior art, the beneficial effects of the computer storage medium provided by the embodiments of the present application are the same as those of the parameter prediction method of the air conditioning device provided by the above embodiments, and will not be elaborated herein.

[0115] An embodiment of the present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the parameter prediction method of the air conditioning device as described above are implemented.

[0116] The computer program product provided by the present application can solve the technical problem that the user experience is poor because the user cannot know in advance the change of environmental parameters during the operation of the air conditioning device. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present application are the same as those of the parameter prediction method of the air conditioning device provided by the above embodiment, and will not be elaborated here.

[0117] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent scope of the present application.

Claims

1. A method for predicting parameters of an air conditioning device, characterized in that, the method for predicting parameters of the air conditioning device includes: obtaining the working parameters of the working space where the current air conditioning device is located, the working parameters including environmental parameters and / or operating parameters; inputting the obtained working parameters into a preset parameter prediction model to obtain a predicted value of the environmental parameters for the next cycle; using the predicted value of the environmental parameters as new working parameters, and returning to execute the step of inputting the obtained working parameters into the preset parameter prediction model to obtain a predicted value of the environmental parameters for the next cycle.

2. The method for predicting parameters of an air conditioning device according to claim 1, characterized in that, the method for predicting parameters of the air conditioning device further includes: inputting the working parameters of each cycle into a preset energy consumption prediction model to obtain a predicted value of the energy consumption for each cycle.

3. The method for predicting parameters of an air conditioning device according to claim 2, characterized in that, the method for predicting parameters of the air conditioning device further includes: displaying the predicted value of the energy consumption obtained for each cycle.

4. The method for predicting parameters of an air conditioning device according to claim 1, characterized in that, the parameter prediction model includes a temperature prediction model and / or a humidity prediction model.

5. The method for predicting parameters of an air conditioning device according to claim 1, characterized in that, the method for predicting parameters of the air conditioning device further includes: displaying the predicted values of the environmental parameters obtained for each cycle.

6. The method for predicting parameters of an air conditioning device according to claim 3, characterized in that, the method for predicting parameters of the air conditioning device further includes: obtaining the actual value of the environmental parameters collected by the environmental sensor in the current cycle, displaying the actual value of the environmental parameters, and adding the actual value of the environmental parameters in the current cycle to the preset energy consumption prediction model.

7. The method for predicting parameters of an air conditioning device according to claim 5, characterized in that, the method for predicting parameters of the air conditioning device further includes: obtaining the actual value of the energy consumption collected by the power consumption module in the current cycle, displaying the actual value of the energy consumption, and adding the actual value of the energy consumption in the current cycle to the preset parameter prediction model.

8. The method for predicting parameters of an air conditioning device according to claim 1, characterized in that, the method for predicting parameters of the air conditioning device further includes: when the preset time arrives or when the operating mode is switched, re-executing the step of obtaining the working parameters of the working space where the current air conditioning device is located.

9. An air conditioning device, characterized in that, it includes a memory, a processor, and a parameter prediction program of the air conditioning device stored in the memory and executable on the processor, and when the program is executed by the processor, it implements the steps of the parameter prediction method according to any one of claims 1 to 8.

10. A computer storage medium, characterized in that, it stores a parameter prediction program of the air conditioning device that can be run on a processor, and the program is called by the processor to implement the parameter prediction method according to any one of claims 1 - 8.

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