Air conditioner control method and device, storage medium and air conditioner

Abstract

The invention discloses an air conditioner control method and device, a storage medium and an air conditioner, and relates to the technical field of air conditioners. The method comprises the steps that outdoor heat radiation data and indoor temperature data within a preset time interval are determined; calculating according to the outdoor thermal radiation data and the indoor temperature data to obtain a dynamic thermal inertia coefficient; performing thermal load prediction processing according to the dynamic thermal inertia coefficient to obtain a predicted future thermal load; and the air conditioner is pre-regulated and controlled according to the predicted future heat load. The air conditioner can be accurately pre-regulated and controlled in advance, the problem caused by the sudden change condition is reliably avoided, and the comfort experience of a user is improved.

Description

Technical Field

[0001] The present application relates to the technical field of air conditioning, and in particular to an air conditioning control method, device, storage medium and air conditioning. Background Art

[0002] Current air conditioning system control strategies primarily rely on real-time indoor temperature feedback or static thermal inertia models to regulate air conditioning. However, relying on real-time indoor temperature feedback means that control is delayed until sudden changes such as a sudden increase in heat load (such as a sudden increase in sunlight) have occurred. Furthermore, relying on static thermal inertia models means that multiple systems use fixed thermal inertia parameters (such as the theoretical heat capacity of building materials), resulting in poor control accuracy in a dynamically changing environment.

[0003] Therefore, the current air conditioning control method has a lag and poor control accuracy, which affects the user's comfort experience. Summary of the Invention

[0004] The embodiment of the present application provides an air conditioning control solution that can reliably achieve precise pre-control of the air conditioning and improve the user's comfort experience.

[0005] The embodiments of this application provide the following technical solutions:

[0006] According to one embodiment of the present application, an air conditioning control method includes: determining outdoor thermal radiation data and indoor temperature data within a predetermined time interval; performing calculations based on the outdoor thermal radiation data and the indoor temperature data to obtain a dynamic thermal inertia coefficient; performing heat load prediction processing based on the dynamic thermal inertia coefficient to obtain a predicted future heat load; and pre-regulating the air conditioning based on the predicted future heat load.

[0007] In some embodiments of the present application, the outdoor thermal radiation data includes the outdoor thermal radiation intensity value within the predetermined time interval, and the indoor temperature data includes the indoor temperature change within the predetermined time interval; the calculation based on the outdoor thermal radiation data and the indoor temperature data to obtain the dynamic thermal inertia coefficient includes: if the outdoor thermal radiation intensity value is greater than or equal to a predetermined threshold, then the dynamic thermal inertia coefficient is calculated according to the formula C=ΔT1*Δt*S, wherein C refers to the dynamic thermal inertia coefficient, ΔT1 refers to the indoor temperature change, S refers to the outdoor thermal radiation intensity value, and Δt refers to the predetermined time interval.

[0008] In some embodiments of the present application, the method further includes: if the outdoor thermal radiation intensity value is less than the predetermined threshold, calculating the indoor temperature cooling rate based on the indoor temperature data; and estimating the dynamic thermal inertia coefficient based on the indoor temperature cooling rate.

[0009] In some embodiments of the present application, the heat load prediction processing is performed based on the dynamic thermal inertia coefficient to obtain a predicted future heat load, including: obtaining environmental related data of the location of the air conditioner; estimating an estimated future heat radiation value based on the environmental related data; and predicting the predicted future heat load based on the estimated future heat radiation value and the dynamic thermal inertia coefficient.

[0010] In some embodiments of the present application, the predicted future heat load is obtained based on the estimated future thermal radiation value and the dynamic thermal inertia coefficient, including: calculating the estimated future temperature change value according to the formula U=C*S*Δt, wherein U refers to the estimated future temperature change value, C refers to the dynamic thermal inertia coefficient, S refers to the estimated future thermal radiation value, and Δt refers to the predetermined time interval; and determining the predicted future heat load based on the estimated future temperature change value.

[0011] In some embodiments of the present application, the environment-related data includes geographic location, weather forecast data and date; and estimating the estimated future thermal radiation value based on the environment-related data includes: estimating based on the geographic location, the weather forecast data and the date to obtain the estimated future thermal radiation value.

[0012] In some embodiments of the present application, the pre-controlling of the air conditioner according to the predicted future heat load includes: determining a pre-control operation and a pre-control time according to the predicted future heat load; performing the pre-control operation at the pre-control time, and the pre-control time is before the future time corresponding to the predicted future heat load.

[0013] According to one embodiment of the present application, an air conditioning control device includes: a determination module for determining outdoor thermal radiation data and indoor temperature data within a predetermined time interval; a calculation module for performing calculations based on the outdoor thermal radiation data and the indoor temperature data to obtain a dynamic thermal inertia coefficient; a prediction module for performing thermal load prediction processing based on the dynamic thermal inertia coefficient to obtain a predicted future thermal load; and a control module for pre-controlling the air conditioner based on the predicted future thermal load.

[0014] According to another embodiment of the present application, a storage medium stores a computer program thereon. When the computer program is executed by a processor of a control module, the control module executes the method described in the embodiment of the present application.

[0015] According to another embodiment of the present application, an air conditioner may include: a memory storing a computer program; and a processor reading the computer program stored in the memory to execute the method described in the embodiment of the present application.

[0016] According to another embodiment of the present application, a computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a control module reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the control module to perform the methods provided in the various optional implementations described in the embodiments of the present application.

[0017] In an embodiment of the present application, outdoor thermal radiation data and indoor temperature data within a predetermined time interval are determined; a dynamic thermal inertia coefficient is obtained by calculation based on the outdoor thermal radiation data and the indoor temperature data; a heat load prediction process is performed based on the dynamic thermal inertia coefficient to obtain a predicted future heat load; and the air conditioner is pre-regulated based on the predicted future heat load.

[0018] In this manner in the embodiments of the present application, first, the outdoor thermal radiation data and indoor temperature data within a predetermined time interval are determined. Since the outdoor thermal radiation intensity and the indoor temperature change are real-time dynamic, a dynamic thermal inertia coefficient that accurately reflects the hysteresis of indoor temperature changes under the action of outdoor thermal radiation can be calculated. Then, the predicted future heat load can be further accurately predicted in advance. Based on the predicted future heat load, the air conditioner can be accurately pre-regulated in advance, reliably avoiding problems caused by sudden changes and improving the user's comfort experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flow chart of an air conditioning control method according to an embodiment of the present application is shown.

[0021] Figure 2 A flow chart of dynamic thermal inertia coefficient calculation according to one embodiment of the present application is shown.

[0022] Figure 3 A heat load prediction flow chart according to an embodiment of the present application is shown.

[0023] Figure 4 A block diagram of an air conditioning control device according to an embodiment of the present application is shown.

[0024] Figure 5 A block diagram of an air conditioner according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] The present disclosure will be further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the examples provided herein are merely for explaining the present disclosure and are not intended to limit the present disclosure. In addition, the examples provided below are partial examples for implementing the present disclosure, rather than providing all examples for implementing the present disclosure. In the absence of conflict, the technical solutions described in the examples of the present disclosure may be implemented in any combination.

[0026] It should be noted that, in the embodiments of the present disclosure, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a method or apparatus comprising a series of elements includes not only the elements explicitly stated, but also other elements not explicitly listed, or also includes elements inherent to the implementation of the method or apparatus. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other related elements (such as steps in the method or units in the apparatus, for example, a unit may be part of a circuit, part of a processor, part of a program or software, etc.) in the method or apparatus comprising the element.

[0027] For example, the air-conditioning control method provided in the embodiment of the present disclosure includes a series of steps, but the air-conditioning control method provided in the embodiment of the present disclosure is not limited to the recorded steps. Similarly, the air-conditioning control device provided in the embodiment of the present disclosure includes a series of units, but the device provided in the embodiment of the present disclosure is not limited to including the units explicitly recorded, and may also include units that need to be set up to obtain relevant information or perform processing based on information.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure pertains. The terms used herein are for the purpose of describing specific embodiments only and are not intended to limit the present disclosure.

[0029] It is understandable that in the specific implementation of this application, relevant data is involved. When the embodiments in this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards.

[0030] Current air conditioning system control strategies primarily rely on real-time indoor temperature feedback or static thermal inertia models to regulate air conditioning. However, relying on real-time indoor temperature feedback means that control is delayed until sudden changes such as a sudden increase in heat load (such as a sudden increase in sunlight) have occurred. Furthermore, relying on static thermal inertia models means that multiple systems use fixed thermal inertia parameters (such as the theoretical heat capacity of building materials), resulting in poor control accuracy in a dynamically changing environment.

[0031] Therefore, the current air conditioning control method has a lag and poor control accuracy, which affects the user's comfort experience.

[0032] In order to solve these problems, this application provides the following air conditioning control solution, which can reliably achieve precise pre-control of the air conditioning and improve the user's comfort experience.

[0033] The following describes in detail the various embodiments of the air conditioning control solution provided in this application.

[0034] first, Figure 1 A flowchart schematically illustrates an air conditioning control method according to one embodiment of the present application. The method may be executed by a control module with processing capabilities. This control module may be installed in devices such as air conditioners, mobile phones, computers, smart watches, and other household appliances, and may include at least a memory and a processor.

[0035] In one embodiment of the present application, a control module, which executes the air conditioner control method, is specifically disposed in the air conditioner. The control module may include a processor and a memory. Specifically, the air conditioner includes the processor and the memory, and the memory stores a computer program. Thus, the processor in the air conditioner can read the computer program stored in the memory to execute the methods of various embodiments of the present application.

[0036] like Figure 1 As shown, the air conditioning control method may include steps S110 to S140.

[0037] Step S110, determining outdoor thermal radiation data and indoor temperature data within a predetermined time interval;

[0038] Step S120, calculating based on the outdoor thermal radiation data and the indoor temperature data to obtain a dynamic thermal inertia coefficient;

[0039] Step S130, performing heat load prediction processing based on the dynamic thermal inertia coefficient to obtain a predicted future heat load;

[0040] Step S140 , pre-regulating the air conditioner according to the predicted future heat load.

[0041] The outdoor thermal radiation intensity can be continuously detected in real time by pre-installed outdoor thermal radiation sensors. The outdoor thermal radiation intensity is the intensity value of outdoor thermal radiation, and its unit can be W / m 2 At the real-time i-th moment, the outdoor thermal radiation data within the predetermined time interval between the i-th moment and the previous i-th moment can be determined. The outdoor thermal radiation data can specifically be the outdoor thermal radiation intensity value within the predetermined time interval. The outdoor thermal radiation intensity value can be the average value or the change value of the outdoor thermal radiation intensity within the predetermined time interval. The value of n can be set according to actual conditions.

[0042] The indoor temperature can be detected and determined in real time by a temperature sensor pre-installed indoors, in units of ° C. At the real-time i-th moment, indoor temperature data for a predetermined time interval between the i-th moment and the previous in-th moment can be determined. The indoor temperature data can include data such as the indoor temperature change within the predetermined time interval (i.e., the indoor temperature at the i-th moment minus the indoor temperature at the in-th moment) or the indoor temperature change rate (i.e., the indoor temperature change divided by the predetermined time interval).

[0043] Since the outdoor thermal radiation intensity and indoor temperature change are real-time dynamic, at the i-th moment in real time, the dynamic thermal inertia coefficient (i.e., dynamic thermal inertia coefficient) can be accurately calculated based on the outdoor thermal radiation intensity and indoor temperature change determined at the i-th moment. This dynamic thermal inertia coefficient can accurately reflect the hysteresis of indoor temperature change under the action of outdoor thermal radiation.

[0044] Since the dynamic thermal inertia coefficient can accurately reflect the hysteresis of indoor temperature changes under the action of outdoor thermal radiation, the heat load forecasting processing is performed based on the dynamic thermal inertia coefficient calculated at the i-th moment, and the predicted future heat load at the i+G-th moment can be accurately predicted in advance. The predicted future heat load is the predicted indoor heat load (i.e., cooling capacity or heating capacity) at the i+G-th moment.

[0045] Based on the predicted future heat load at the future moment (i+G) obtained in advance, the air conditioner can be controlled in advance (i.e., pre-control) at a certain moment before i+G. This can avoid sudden changes in indoor temperature such as a sudden increase in heat load at i+G, and can also avoid the high energy consumption caused by sudden changes in the air conditioner. For example, the compressor frequency, fan speed, and auxiliary cooling can be adjusted in advance at i, reliably avoiding problems caused by sudden changes at i+G and reliably reducing control lag.

[0046] In summary, in this manner of the embodiments of the present application, first, the outdoor thermal radiation data and indoor temperature data within a predetermined time interval are determined. Since the outdoor thermal radiation intensity and the indoor temperature change are real-time dynamic, the dynamic thermal inertia coefficient that accurately reflects the hysteresis of the indoor temperature change under the action of outdoor thermal radiation can be calculated. Then, the predicted future heat load can be further accurately predicted in advance. Based on the predicted future heat load, the air conditioner can be accurately pre-regulated in advance, and problems caused by sudden changes can be reliably avoided, thereby improving the user's comfort experience.

[0047] Described below Figure 1 When performing air conditioning control under the embodiment, further optional specific embodiments are provided for each step performed.

[0048] In one embodiment, the outdoor thermal radiation data may include the outdoor thermal radiation intensity value within a predetermined time interval, and the indoor temperature data may include the indoor temperature change within a predetermined time interval; Figure 2 In step S120, a dynamic thermal inertia coefficient is obtained by calculation based on the outdoor thermal radiation data and the indoor temperature data, including: step S210, if the outdoor thermal radiation intensity value is greater than or equal to the predetermined threshold, the dynamic thermal inertia coefficient is calculated according to the formula C=ΔT1*Δt*S, where C refers to the dynamic thermal inertia coefficient, ΔT1 refers to the indoor temperature change, S refers to the outdoor thermal radiation intensity value, and Δt refers to the predetermined time interval.

[0049] If the outdoor thermal radiation intensity value within the predetermined time interval is greater than the predetermined threshold, it indicates that the outdoor thermal radiation is above a certain radiation amount. For example, during the day, the outdoor thermal radiation intensity value will be greater than a predetermined threshold. The predetermined threshold can be set to a smaller threshold according to actual conditions.

[0050] When the outdoor thermal radiation intensity value is greater than or equal to a predetermined threshold, the dynamic thermal inertia coefficient is calculated according to the formula C = ΔT1 * Δt * S, where C refers to the dynamic thermal inertia coefficient, ΔT1 refers to the indoor temperature change, S refers to the outdoor thermal radiation intensity value, and Δt refers to the predetermined time interval. The applicant has discovered that when the outdoor thermal radiation intensity value is greater than or equal to the predetermined threshold, this calculation method can quickly and efficiently calculate the dynamic thermal inertia coefficient. Furthermore, this dynamic thermal inertia coefficient, when used in the embodiments of this application, can effectively ensure the accuracy of future heat load predictions.

[0051] Among them, in some methods, the outdoor thermal radiation data and indoor temperature data can be smoothed in advance using smoothing methods such as moving average method or low-pass filtering method to eliminate instantaneous fluctuations of the outdoor thermal radiation data and indoor temperature data, and further improve the accuracy of the dynamic thermal inertia coefficient.

[0052] Optionally, in other embodiments, in step S120, calculation is performed based on outdoor thermal radiation data and indoor temperature data to obtain a dynamic thermal inertia coefficient, including: using a preset coefficient analysis model to analyze and calculate the outdoor thermal radiation data and indoor temperature data to obtain the dynamic thermal inertia coefficient output by the preset coefficient analysis model, wherein the preset coefficient analysis model can be a large language model after fine-tuning training.

[0053] Further, in one embodiment, see Figure 2 Step S120 may further include: step S220, if the outdoor thermal radiation intensity value is less than a predetermined threshold, calculating the indoor temperature cooling rate according to the indoor temperature data; and estimating the dynamic thermal inertia coefficient according to the indoor temperature cooling rate.

[0054] If the outdoor thermal radiation intensity value within the predetermined time interval is less than the predetermined threshold, it indicates that there is very weak or no thermal radiation outdoors. For example, at night, the outdoor thermal radiation intensity value will be less than a predetermined threshold. The predetermined threshold can be set to a smaller threshold according to actual conditions.

[0055] If the outdoor thermal radiation intensity value is less than a predetermined threshold, the indoor temperature cooling rate (i.e., the indoor temperature change rate, indoor temperature cooling rate = the indoor temperature change within a predetermined time interval divided by the predetermined time interval) is calculated based on the indoor temperature data; the dynamic thermal inertia coefficient is further estimated based on the indoor temperature cooling rate, and the corresponding dynamic thermal inertia coefficient can also be accurately estimated in this case (the outdoor thermal radiation intensity value is less than the predetermined threshold).

[0056] In one embodiment, the dynamic thermal inertia coefficient is estimated based on the indoor temperature cooling rate. The dynamic thermal inertia coefficient can be estimated and calculated according to the formula C = R / Δt, where Δt is the predetermined time interval, R is the indoor temperature cooling rate, and C is the dynamic thermal inertia coefficient. The applicant has discovered that when the outdoor thermal radiation intensity is less than a predetermined threshold, this calculation method can quickly and efficiently calculate the corresponding dynamic thermal inertia coefficient. Furthermore, when this dynamic thermal inertia coefficient is used in the embodiments of this application, it can effectively ensure the accuracy of future heat load predictions.

[0057] Optionally, in other embodiments, if the outdoor thermal radiation intensity value is less than a predetermined threshold, a preset coefficient analysis model can be used to analyze and calculate the indoor temperature data to obtain the dynamic thermal inertia coefficient output by the preset coefficient analysis model, wherein the preset coefficient analysis model can be a large language model after fine-tuning training.

[0058] In one embodiment, see Figure 3 In step S130, heat load prediction processing is performed according to the dynamic thermal inertia coefficient to obtain a predicted future heat load, including: step S310, obtaining environmental data related to the location of the air conditioner; step S320, obtaining an estimated future heat radiation value based on the environmental data; step S330, obtaining a predicted future heat load based on the estimated future heat radiation value and the dynamic thermal inertia coefficient.

[0059] The environment-related data of the location of the air conditioner may include the geographical location of the air conditioner (such as longitude and latitude), weather forecast data (such as the weather forecast data for the day, which may include whether it is cloudy, rainy, etc.) and the current date.

[0060] This environmental data can reflect the sun's trajectory, lighting conditions, and other factors at future moments (i+G). Furthermore, at moment i, based on the environmental data acquired at moment i, the outdoor thermal radiation intensity at moment i+G (i.e., the estimated future thermal radiation value) can be accurately predicted in advance.

[0061] Combining the estimated future thermal radiation value and the dynamic thermal inertia coefficient prediction to obtain the predicted future heat load can further improve the accuracy of the predicted future heat load.

[0062] Furthermore, in one embodiment, the environment-related data includes geographic location, weather forecast data and date; in step S320, the estimated future thermal radiation value is obtained based on the environment-related data, which may specifically include: estimating based on the geographic location, weather forecast data and date to obtain the estimated future thermal radiation value.

[0063] In one method, an estimated future thermal radiation value is obtained based on the geographical location, weather forecast data and date. The method may be as follows: at the i-th moment, the position of the sun at the future moment (i+G moment) is first determined based on the geographical location and date; then, based on the position difference between the sun position at the i-th moment and the sun position at the i+G moment, the outdoor thermal radiation intensity at the i-th moment is attenuated or enhanced based on the position difference to obtain a first outdoor thermal radiation intensity at the i+G moment; then, the weather conditions at the i+G moment are determined based on the weather forecast data, and the first outdoor thermal radiation intensity is attenuated or enhanced based on the weather conditions at the i+G moment to obtain a second outdoor thermal radiation intensity (i.e., the estimated future thermal radiation value).

[0064] Among them, a simplified formula or open source library (such as Python's pvlib) can be used to calculate and determine the sun's position (such as the solar altitude angle and azimuth angle, etc.) at a future moment (i+G moment). The outdoor thermal radiation intensity at the i-th moment is attenuated or enhanced according to the position difference. For example, when the sun's position at the i-th moment is at noon and the solar altitude angle is the maximum, the outdoor thermal radiation intensity is set to a peak value (such as 1000W / m2). Then, the position difference (such as the altitude difference between the solar altitude angle at the i-th moment and the solar altitude angle at the i+G moment) can be determined to determine the corresponding attenuation ratio or enhancement ratio. The outdoor thermal radiation intensity at the i-th moment is attenuated or enhanced according to the attenuation ratio or enhancement ratio to obtain the first outdoor thermal radiation intensity at the i+G moment (for example, when the altitude difference is 30°, the first outdoor thermal radiation intensity can be 50% of the peak value). In addition, the first outdoor thermal radiation intensity is attenuated or enhanced according to the weather conditions at the i+Gth moment to obtain the second outdoor thermal radiation intensity (i.e., the estimated future thermal radiation value). For example, when it is cloudy, the first outdoor thermal radiation intensity is attenuated by 50% to obtain the second outdoor thermal radiation intensity (i.e., the estimated future thermal radiation value).

[0065] Alternatively, in another embodiment, estimating the future thermal radiation value based on the geographic location, weather forecast data, and date may include: using a preset radiation estimation model to estimate the future thermal radiation value based on the geographic location, weather forecast data, and date. The preset radiation estimation model may be a fine-tuned large language model.

[0066] Furthermore, in one embodiment, in step S330, the predicted future heat load is obtained based on the estimated future thermal radiation value and the dynamic thermal inertia coefficient, which may include: calculating the estimated future temperature change value according to the formula U=C*S*Δt, wherein U refers to the estimated future temperature change value, C refers to the dynamic thermal inertia coefficient, S refers to the estimated future thermal radiation value, and Δt refers to a predetermined time interval; and determining the predicted future heat load based on the estimated future temperature change value.

[0067] The applicant discovered that at the i-th moment, according to the formula U = C*S*Δt, the temperature change value at the future moment (i+G moment) compared to the i-th moment (i.e., the estimated future temperature change value) can be accurately estimated. For example, if the estimated future temperature change value is 2 degrees, it means that the indoor temperature at the future moment (i+G moment) will rise by 2 degrees compared to the i-th moment.

[0068] The corresponding predicted future heat load can be set in advance in the heat load prediction table for different predicted future temperature change values. Then, the predicted future heat load corresponding to the predicted future temperature change value can be queried from the heat load prediction table (for example, the predicted future heat load corresponding to a 2-degree increase is 2kW of cooling capacity).

[0069] In one embodiment, in step S140, pre-regulating the air conditioner according to the predicted future heat load may include: determining the pre-regulation operation and the pre-regulation time according to the predicted future heat load; performing the pre-regulation operation at the pre-regulation time, and the pre-regulation time is before the future time corresponding to the predicted future heat load.

[0070] The pre-control operations and pre-control times corresponding to different heat load ranges can be specified in advance in the pre-control table. Subsequently, the pre-control operations and pre-control times corresponding to the heat load range in which the future heat load is predicted can be queried from the pre-control table. The pre-control operation can then be executed at the pre-control time, which is before the future time corresponding to the predicted future heat load, thereby enabling the air conditioner to be controlled in advance at a certain time before the i+Gth time (i.e., pre-control). The pre-control operation can include adjusting the compressor frequency, fan speed, auxiliary cooling, etc.

[0071] To facilitate better implementation of the air conditioning control method provided in the embodiment of the present application, the embodiment of the present application also provides an air conditioning control device based on the above air conditioning control method. The meanings of the terms herein are the same as those in the above air conditioning control method, and the specific implementation details can be referred to the description in the method embodiment. Figure 4 A block diagram of an air conditioning control device according to an embodiment of the present application is shown.

[0072] like Figure 4 As shown, the air conditioning control device 400 may include: a determination module 410 may be used to determine outdoor thermal radiation data and indoor temperature data within a predetermined time interval; a calculation module 420 may be used to perform calculations based on the outdoor thermal radiation data and the indoor temperature data to obtain a dynamic thermal inertia coefficient; a prediction module 430 may be used to perform thermal load prediction processing based on the dynamic thermal inertia coefficient to obtain a predicted future thermal load; and a control module 440 may be used to pre-control the air conditioner based on the predicted future thermal load.

[0073] In some embodiments of the present application, the outdoor thermal radiation data includes the outdoor thermal radiation intensity value within the predetermined time interval, and the indoor temperature data includes the indoor temperature change within the predetermined time interval; the calculation module 420 can be used to: if the outdoor thermal radiation intensity value is greater than or equal to the predetermined threshold, then the dynamic thermal inertia coefficient is calculated according to the formula C=ΔT1*Δt*S, wherein C refers to the dynamic thermal inertia coefficient, ΔT1 refers to the indoor temperature change, S refers to the outdoor thermal radiation intensity value, and Δt refers to the predetermined time interval.

[0074] In some embodiments of the present application, the calculation module 420 can also be used to: if the outdoor thermal radiation intensity value is less than the predetermined threshold, calculate the indoor temperature cooling rate based on the indoor temperature data; and estimate the dynamic thermal inertia coefficient based on the indoor temperature cooling rate.

[0075] In some embodiments of the present application, the prediction module 430 can be used to: obtain environmental data related to the location of the air conditioner; estimate the estimated future thermal radiation value based on the environmental data; and predict the predicted future heat load based on the estimated future thermal radiation value and the dynamic thermal inertia coefficient.

[0076] In some embodiments of the present application, the prediction module 430 can be used to: calculate the estimated future temperature change value according to the formula U=C*S*Δt, wherein U refers to the estimated future temperature change value, C refers to the dynamic thermal inertia coefficient, S refers to the estimated future thermal radiation value, and Δt refers to the predetermined time interval; and determine the predicted future heat load based on the estimated future temperature change value.

[0077] In some embodiments of the present application, the environment-related data includes geographic location, weather forecast data and date; the prediction module 430 can be used to: make an estimate based on the geographic location, the weather forecast data and the date to obtain the estimated future thermal radiation value.

[0078] In some embodiments of the present application, the control module 440 can be used to: determine the pre-control operation and the pre-control time based on the predicted future heat load; perform the pre-control operation at the pre-control time, and the pre-control time is before the future time corresponding to the predicted future heat load.

[0079] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0080] In addition, the embodiment of the present application also provides an air conditioner, such as Figure 5 As shown, Figure 5 A block diagram of an air conditioner according to an embodiment of the present application is shown, specifically:

[0081] The air conditioner may include one or more processing core processors 501, one or more computer readable storage media memories 502 and other components. Those skilled in the art will understand that Figure 5 The air conditioner structure shown in the figure does not constitute a limitation of the air conditioner, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0082] Processor 501 is the control center of the air conditioner. It connects the various components of the computer device using various interfaces and circuits. By running or executing software programs and / or modules stored in memory 502 and accessing data stored in memory 502, it performs various computer functions and processes data. Optionally, processor 501 may include one or more processing cores. Preferably, processor 501 integrates an application processor and a modem processor. The application processor primarily handles the operating system, user interfaces, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 501.

[0083] The memory 502 can be used to store software programs and modules. The processor 501 executes various functional applications and data processing by running the software programs and modules stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created based on the use of the air conditioner, etc. In addition, the memory 502 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory 502 may also include a memory controller to provide the processor 501 with access to the memory 502.

[0084] The air conditioner also includes a power supply 503 for supplying power to various components. Preferably, the power supply 503 can be logically connected to the processor 501 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The power supply 503 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0085] Although not shown, the air conditioner may further include a display unit, etc., which will not be described in detail herein. Specifically, in this embodiment, the processor 501 in the air conditioner may load executable files corresponding to one or more computer program processes into the memory 502 according to instructions, and the processor 501 may execute the computer programs stored in the memory 502, thereby implementing the various functions described in the aforementioned embodiments of the present application.

[0086] For example, the processor 501 may execute the following: determining outdoor thermal radiation data and indoor temperature data within a predetermined time interval; performing calculations based on the outdoor thermal radiation data and the indoor temperature data to obtain a dynamic thermal inertia coefficient; performing thermal load prediction processing based on the dynamic thermal inertia coefficient to obtain a predicted future thermal load; and pre-regulating the air conditioner based on the predicted future thermal load.

[0087] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by a computer program, or by controlling related hardware through a computer program. The computer program may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0088] To this end, an embodiment of the present application further provides a storage medium storing a computer program, which can be loaded by a processor to execute the steps of any method provided in the embodiment of the present application.

[0089] The storage medium may be a computer-readable storage medium, and the storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0090] Since the computer program stored in the storage medium can execute the steps of any method provided in the embodiments of the present application, the beneficial effects that can be achieved by the method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0091] According to another embodiment of the present application, a computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a control module reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the control module to perform the methods provided in the various optional implementations described in the embodiments of the present application.

[0092] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0093] It should be understood that the present application is not limited to the embodiments that have been described above and shown in the accompanying drawings, but various modifications and changes may be made without departing from the scope thereof.

Claims

1. An air conditioning control method, characterized in that: include: determining outdoor thermal radiation data and indoor temperature data within a predetermined time interval; Calculating according to the outdoor thermal radiation data and the indoor temperature data to obtain a dynamic thermal inertia coefficient; Performing heat load prediction processing according to the dynamic thermal inertia coefficient to obtain a predicted future heat load; The air conditioner is pre-regulated according to the predicted future heat load.

2. The method according to claim 1, characterized in that The outdoor heat radiation data includes the outdoor heat radiation intensity value within the predetermined time interval, and the indoor temperature data includes the indoor temperature change within the predetermined time interval; The calculation based on the outdoor heat radiation data and the indoor temperature data to obtain the dynamic thermal inertia coefficient includes: If the outdoor thermal radiation intensity value is greater than or equal to a predetermined threshold, the dynamic thermal inertia coefficient is calculated according to the formula C=ΔT1*Δt*S, where C refers to the dynamic thermal inertia coefficient, ΔT1 refers to the indoor temperature change, S refers to the outdoor thermal radiation intensity value, and Δt refers to the predetermined time interval.

3. The method according to claim 2, characterized in that The method further comprises: If the outdoor heat radiation intensity value is less than the predetermined threshold, the indoor temperature cooling rate is calculated based on the indoor temperature data; The dynamic thermal inertia coefficient is estimated based on the indoor temperature cooling rate.

4. The method according to claim 1, wherein The performing heat load prediction processing according to the dynamic thermal inertia coefficient to obtain a predicted future heat load includes: Obtaining environmental data related to the location of the air conditioner; Obtaining an estimated future thermal radiation value based on the environment-related data; The predicted future heat load is obtained according to the estimated future heat radiation value and the dynamic thermal inertia coefficient.

5. The method according to claim 4, characterized in that The step of predicting the predicted future heat load based on the estimated future heat radiation value and the dynamic thermal inertia coefficient includes: The estimated future temperature change value is calculated according to the formula U=C*S*Δt, where U refers to the estimated future temperature change value, C refers to the dynamic thermal inertia coefficient, S refers to the estimated future thermal radiation value, and Δt refers to the predetermined time interval; The predicted future heat load is determined according to the estimated future temperature change value.

6. The method according to claim 4, characterized in that The environment-related data includes geographic location, weather forecast data and date; The estimating the future thermal radiation value according to the environment-related data includes: An estimate is made based on the geographical location, the weather forecast data and the date to obtain the estimated future thermal radiation value.

7. The method according to claim 1, characterized in that The pre-regulating the air conditioner according to the predicted future heat load includes: determining a pre-control operation and a pre-control time according to the predicted future heat load; The pre-control operation is performed at the pre-control time, and the pre-control time is before a future time corresponding to the predicted future heat load.

8. An air conditioning control device, characterized in that: The device comprises: A determination module, configured to: determine outdoor thermal radiation data and indoor temperature data within a predetermined time interval; A calculation module, configured to calculate according to the outdoor thermal radiation data and the indoor temperature data to obtain a dynamic thermal inertia coefficient; A prediction module is used to: perform heat load prediction processing according to the dynamic thermal inertia coefficient to obtain a predicted future heat load; The control module is used to pre-control the air conditioner according to the predicted future heat load.

9. A storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by the processor of the control module, the control module executes the method according to any one of claims 1 to 7.

10. An air conditioner, characterized in that: include: a memory storing a computer program; A processor reads a computer program stored in a memory to execute the method according to any one of claims 1 to 7.

Images