Self-cleaning method and device of air conditioning equipment, air conditioning equipment and chip

Abstract

This disclosure relates to a self-cleaning method, apparatus, air conditioning equipment, and chip for air conditioning equipment, belonging to the technical field of air conditioning equipment. The method includes: determining the self-cleaning time of the air conditioning equipment based on the dust accumulation status; determining whether the self-cleaning time falls within a peak electricity consumption period; adjusting the self-cleaning time to an off-peak electricity consumption period if the self-cleaning time falls within a peak electricity consumption period; and maintaining the self-cleaning time if the self-cleaning time falls within an off-peak electricity consumption period. Therefore, the self-cleaning time of the air conditioning equipment can be determined considering the dust accumulation status, improving the flexibility of the self-cleaning time, avoiding over-cleaning or under-cleaning, and helping to extend the service life of the air conditioning equipment. Furthermore, it avoids the air conditioning equipment performing self-cleaning during peak electricity consumption periods, helping to reduce the grid load and improve the stability and reliability of grid operation.

Description

Technical Field

[0001] This disclosure relates to the field of air conditioning equipment technology, and in particular to a self-cleaning method, apparatus, air conditioning equipment, chip, and storage medium for air conditioning equipment. Background Technology

[0002] In related technologies, air conditioning equipment generates a certain electrical load during self-cleaning, which can lead to a large load on the power grid and affect the stability and reliability of the power grid operation. Summary of the Invention

[0003] This disclosure provides a self-cleaning method, air conditioning equipment, apparatus, chip, and storage medium for air conditioning devices, to at least solve the problem in related technologies where self-cleaning of air conditioning equipment leads to a large load on the power grid, affecting the stability and reliability of power grid operation. The technical solution of this disclosure is as follows:

[0004] According to a first aspect of the present disclosure, a self-cleaning method for an air conditioning device is provided, comprising: determining a self-cleaning time for the air conditioning device based on the dust accumulation state of the air conditioning device; determining whether the self-cleaning time falls within a peak electricity consumption period; adjusting the self-cleaning time to an off-peak electricity consumption period if the self-cleaning time falls within a peak electricity consumption period; and maintaining the self-cleaning time if the self-cleaning time falls within an off-peak electricity consumption period.

[0005] According to a second aspect of the present disclosure, a self-cleaning device for an air conditioning unit is provided, comprising: a first determining module configured to determine a self-cleaning time of the air conditioning unit based on the dust accumulation state of the air conditioning unit; a second determining module configured to determine whether the self-cleaning time falls within a peak electricity consumption period; an adjusting module configured to adjust the self-cleaning time to an off-peak electricity consumption period if the self-cleaning time falls within a peak electricity consumption period; and a processing module configured to maintain the self-cleaning time if the self-cleaning time falls within an off-peak electricity consumption period.

[0006] According to a third aspect of the present disclosure, an air conditioning device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the self-cleaning method of the air conditioning device according to the first aspect of the present disclosure.

[0007] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the self-cleaning method for an air conditioning device as described in the first aspect of the present disclosure.

[0008] According to a fifth aspect of the present disclosure, a chip is provided, the chip including an interface circuit and a processing circuit coupled to each other, the interface circuit being used to input or output signals, and the processing circuit being configured to implement the steps of the self-cleaning method for an air conditioning device according to the first aspect of the present disclosure.

[0009] According to a sixth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the self-cleaning method for an air conditioning device described in the first aspect of the present disclosure.

[0010] The technical solution provided by the embodiments of this disclosure offers at least the following beneficial effects: Based on the dust accumulation state of the air conditioning equipment, the self-cleaning time of the air conditioning equipment is determined; it is determined whether the self-cleaning time falls within a peak electricity consumption period; if the self-cleaning time falls within a peak electricity consumption period, it is adjusted to a low electricity consumption period; if the self-cleaning time falls within a low electricity consumption period, it is maintained. Therefore, the self-cleaning time of the air conditioning equipment can be determined considering its dust accumulation state, meaning different self-cleaning times can be adopted under different dust accumulation states, improving the flexibility of the self-cleaning time, avoiding over-cleaning or under-cleaning, and helping to extend the service life of the air conditioning equipment.

[0011] In addition, it can be determined whether the self-cleaning time falls during peak electricity consumption periods. If the self-cleaning time falls during peak electricity consumption periods, adjusting the self-cleaning time to off-peak electricity consumption periods can avoid the air conditioning equipment from performing self-cleaning during peak electricity consumption periods, which helps to reduce the grid load and improve the stability and reliability of grid operation.

[0012] Maintain the self-cleaning time when it coincides with a period of low power consumption.

[0013] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0015] Figure 1 This is a schematic flowchart illustrating a self-cleaning method for an air conditioning device according to an exemplary embodiment.

[0016] Figure 2 This is a schematic flowchart illustrating a self-cleaning method for an air conditioning device according to another exemplary embodiment.

[0017] Figure 3This is a schematic flowchart illustrating a self-cleaning method for an air conditioning device according to another exemplary embodiment.

[0018] Figure 4 This is a schematic flowchart illustrating a self-cleaning method for an air conditioning device according to another exemplary embodiment.

[0019] Figure 5 This is a schematic flowchart illustrating a self-cleaning method for an air conditioning device according to another exemplary embodiment.

[0020] Figure 6 This is a schematic diagram illustrating the structure of a self-cleaning device for an air conditioning unit according to an exemplary embodiment.

[0021] Figure 7 This is a schematic diagram of the structure of a chip according to an exemplary embodiment. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0023] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0024] The following description, with reference to the accompanying drawings, outlines a self-cleaning method, apparatus, air conditioning device, chip, and storage medium for air conditioning equipment according to embodiments of the present disclosure.

[0025] Figure 1 This is a schematic flowchart illustrating a self-cleaning method for an air conditioning device according to an exemplary embodiment, such as... Figure 1 As shown in the figure, the self-cleaning method of the air conditioning equipment in this embodiment includes the following steps.

[0026] S101, Determine the self-cleaning time of the air conditioning equipment based on the dust accumulation status of the air conditioning equipment.

[0027] It should be noted that the self-cleaning method for air conditioning equipment in this embodiment of the present disclosure is performed by an electronic device, such as an air conditioning unit. The self-cleaning method for air conditioning equipment in this embodiment of the present disclosure can be executed by a self-cleaning device for air conditioning equipment in this embodiment of the present disclosure. This self-cleaning device for air conditioning equipment in this embodiment of the present disclosure can be configured in any air conditioning unit to execute the self-cleaning method for air conditioning equipment in this embodiment of the present disclosure.

[0028] The dust accumulation status of air conditioning equipment is not subject to strict limitations. It can be determined through parameters such as dust accumulation amount, dust accumulation rate, filter resistance, and dust concentration. The specific timeframe corresponding to the dust accumulation status is also not strictly limited. This includes the dust accumulation status of the air conditioning equipment at historical, current, and future times. Historical times refer to moments earlier than the current time, such as moments 4 hours or 24 hours earlier than the current time. Future times refer to moments later than the current time, such as moments 4 hours or 24 hours later than the current time.

[0029] The self-cleaning moment refers to the moment when the air conditioning equipment performs self-cleaning, including the start time of the self-cleaning process.

[0030] Optionally, the dust accumulation status of the air conditioning equipment can be obtained, including determining the dust accumulation status based on the operating parameters of the air conditioning equipment and environmental parameters. This allows for a comprehensive consideration of both the operating parameters and environmental parameters of the air conditioning equipment when determining the dust accumulation status, thus improving the accuracy of the dust accumulation assessment.

[0031] It should be noted that there are no strict limitations on the operating parameters and environmental parameters of the air conditioning equipment. Operating parameters include filter resistance, fan speed, operating time, and self-cleaning data. Self-cleaning data can include the self-cleaning time and duration for any given self-cleaning cycle. Environmental parameters include ambient humidity, ambient temperature, dust concentration (e.g., PM2.5 concentration), ambient wind speed, and climate parameters (e.g., seasonal temperature, probability of precipitation). PM2.5 concentration refers to the concentration of airborne particles with a diameter of 2.5 micrometers or less.

[0032] In related technologies, the self-cleaning strategies of air conditioning equipment are relatively simple. For example, self-cleaning is performed according to a preset self-cleaning time, which can easily lead to over-cleaning or under-cleaning, affecting the service life of the air conditioning equipment.

[0033] In this disclosure, the self-cleaning time of the air conditioning equipment can be determined by taking into account the dust accumulation state of the air conditioning equipment. That is, different self-cleaning times can be adopted under different dust accumulation states, which improves the flexibility of the self-cleaning time, avoids over-cleaning or under-cleaning, and helps to extend the service life of the air conditioning equipment.

[0034] Optionally, the self-cleaning time of the air conditioning equipment is determined based on the dust accumulation status of the equipment, including setting the current time as the self-cleaning time in response to the dust accumulation parameter of the air conditioning equipment being greater than a set threshold. Thus, the air conditioning equipment can be self-cleaned immediately when the dust accumulation parameter is high at the current time.

[0035] Optionally, the self-cleaning time of the air conditioning equipment is determined based on the dust accumulation status of the equipment. This includes determining the future time as the self-cleaning time in response to the dust accumulation parameter of the air conditioning equipment being greater than a set threshold, or obtaining a target time earlier than the future time as the self-cleaning time. Thus, when the dust accumulation parameter of the air conditioning equipment is large in the future, the future time can be used as the self-cleaning time, thereby performing self-cleaning of the air conditioning equipment at that future time; or, obtaining a target time earlier than the future time can be used as the self-cleaning time, thereby performing self-cleaning of the air conditioning equipment before the future time.

[0036] Optionally, the self-cleaning time of the air conditioning equipment can be determined based on the dust accumulation status of the equipment, including determining the self-cleaning time based on the dust accumulation status, as well as the energy consumption parameters and / or environmental parameters of the air conditioning equipment. This allows for the determination of the self-cleaning time by taking into account the dust accumulation status of the air conditioning equipment, as well as the energy consumption parameters and / or environmental parameters, thus improving the accuracy of the self-cleaning time.

[0037] It should be noted that there are no excessive restrictions on energy consumption parameters, such as electricity price, cooling capacity, heating capacity, energy efficiency ratio, and input power.

[0038] Optionally, based on the dust accumulation state and the energy consumption parameters and / or environmental parameters of the air conditioning equipment, the self-cleaning time is determined. This includes constructing an objective function based on the dust accumulation state and the energy consumption parameters and / or environmental parameters of the air conditioning equipment, with the self-cleaning time as an independent variable. The objective function is then optimized to obtain the self-cleaning time when the objective function is minimized, which is taken as the self-cleaning time. Therefore, by considering the dust accumulation state and the energy consumption parameters and / or environmental parameters of the air conditioning equipment, constructing an objective function, and solving for the self-cleaning time when the objective function is minimized, this helps to minimize the energy consumption and dust accumulation of the air conditioning equipment, thus saving energy and extending the service life of the air conditioning equipment.

[0039] For example, optimizing the objective function can be achieved using the following formula:

[0040]

[0041] D th,adj =D th ×γ(C)

[0042] Where Φ is the objective function, and D predTo predict dust accumulation parameters, D th,adj To predict the updated threshold corresponding to the dust accumulation parameters, D th To predict the threshold values ​​before the update for dust accumulation parameters, E represents the energy consumption parameter, C represents the environmental parameter, and Δt represents the threshold value. clean The self-cleaning time interval is the time interval between two consecutive self-cleaning operations of the air conditioning unit, and is related to the self-cleaning time t. clean Related.

[0043] Used to characterize the search for an optimal Δt clean This minimizes Φ.

[0044] λ, μ, and v are all coefficients. γ(C) is a climate compensation factor, which can be adjusted to different ranges based on factors such as season and humidity.

[0045] S102, determine whether the self-cleaning time falls during peak electricity usage periods.

[0046] S103, when the self-cleaning time falls during peak electricity consumption periods, adjusts the self-cleaning time to off-peak electricity consumption periods.

[0047] S104 maintains the self-cleaning time when the self-cleaning time is during a low-power period.

[0048] It should be noted that peak electricity consumption periods refer to times when the power grid load is high, while off-peak electricity consumption periods refer to times when the power grid load is low. No strict limitations are imposed on peak and off-peak electricity consumption periods; the strategies for defining peak and off-peak electricity consumption periods may differ depending on the region and season.

[0049] In related technologies, air conditioning equipment generates a certain electrical load during self-cleaning, which can lead to a large load on the power grid and affect the stability and reliability of the power grid operation.

[0050] In this disclosure, it can be determined whether the self-cleaning time falls during peak electricity consumption periods. If the self-cleaning time falls during peak electricity consumption periods, adjusting the self-cleaning time to off-peak electricity consumption periods can prevent air conditioning equipment from performing self-cleaning during peak electricity consumption periods, which helps reduce the grid load and improve the stability and reliability of grid operation.

[0051] In addition, the self-cleaning time is maintained even when the self-cleaning time coincides with a period of low electricity consumption.

[0052] Optionally, the self-cleaning time can be adjusted to a low-power consumption period, including obtaining the low-power consumption period that is later than the self-cleaning time and has the shortest interval with the self-cleaning time, as the first low-power consumption period, and postponing the self-cleaning time to the first low-power consumption period.

[0053] Alternatively, the off-peak electricity consumption period that is earlier than the self-cleaning time and has the shortest interval with the self-cleaning time can be obtained as the second off-peak electricity consumption period, and the self-cleaning time can be moved forward to the second off-peak electricity consumption period.

[0054] Therefore, the self-cleaning time can be postponed to the first off-peak electricity period, or the self-cleaning time can be brought forward to the second off-peak electricity period.

[0055] For example, peak electricity usage hours are from 8:00 AM to 9:00 PM, and off-peak hours are from 9:00 PM to 8:00 AM the next day. If the self-cleaning time is 10:00 AM on July 1st, the self-cleaning time can be postponed to 9:00 PM on July 1st, or the self-cleaning time can be brought forward to 7:00 AM on July 1st.

[0056] Optionally, postponing the self-cleaning time to before the first off-peak electricity consumption period also includes determining that the postponement period for the self-cleaning time is less than or equal to a set duration. Thus, the self-cleaning time can be postponed to the first off-peak electricity consumption period only if the postponement period is determined to be less than or equal to the set duration.

[0057] The method also includes updating the delay time to the set time when the delay duration is longer than the set time. Therefore, updating the delay time to the set time when the delay duration is long can prevent the self-cleaning time from being delayed for an extended period.

[0058] It should be noted that this disclosure does not limit the execution sequence of steps S101 to S104. For example, steps S101 to S103 can be implemented as independent embodiments, and steps S101, S102, and S104 can be implemented as independent embodiments.

[0059] The self-cleaning method for air conditioning equipment provided in this disclosure determines the self-cleaning time based on the dust accumulation status of the air conditioning equipment, and determines whether the self-cleaning time falls during peak electricity consumption periods. If the self-cleaning time falls during peak electricity consumption periods, it is adjusted to during off-peak electricity consumption periods; if the self-cleaning time falls during off-peak electricity consumption periods, it is maintained. Therefore, the self-cleaning time can be determined taking into account the dust accumulation status of the air conditioning equipment, meaning different self-cleaning times can be adopted under different dust accumulation statuses. This improves the flexibility of the self-cleaning time, avoids over-cleaning or under-cleaning, and helps extend the service life of the air conditioning equipment.

[0060] In addition, it can be determined whether the self-cleaning time falls during peak electricity consumption periods. If the self-cleaning time falls during peak electricity consumption periods, adjusting the self-cleaning time to off-peak electricity consumption periods can avoid the air conditioning equipment from performing self-cleaning during peak electricity consumption periods, which helps to reduce the grid load and improve the stability and reliability of grid operation.

[0061] Maintain the self-cleaning time when it coincides with a period of low power consumption.

[0062] Figure 2 This is a schematic flowchart illustrating a self-cleaning method for an air conditioning device according to another exemplary embodiment, such as... Figure 2 As shown in the figure, the self-cleaning method of the air conditioning equipment in this embodiment includes the following steps.

[0063] S201, Obtain datasets at multiple time points, including historical and current time points. The datasets include at least one of the following parameters: actual dust accumulation parameters of the air conditioning equipment, operating parameters of the air conditioning equipment, environmental parameters, and electricity price parameters.

[0064] S202, by processing the dataset at multiple time points using the first model, the predicted dust accumulation parameters of the air conditioning equipment at future time points are obtained to determine the dust accumulation status.

[0065] In this embodiment, the first model can be used to process the dataset at multiple time points to obtain the predicted dust accumulation parameters of the air conditioning equipment at future time points, so as to determine the dust accumulation state, such as determining the dust accumulation state of the air conditioning equipment at future time points.

[0066] The first model is not subject to many restrictions. For example, it can include mechanistic models, machine learning models, etc. Machine learning models include LSTM (Long Short-Term Memory) models.

[0067] For example, the dataset X(t) at time t is as follows:

[0068] X(t)={D(t),R(t),T(t),H(t),W(t),E(t),C(t)}

[0069] Where D(t) is the actual dust accumulation parameter of the air conditioning equipment at time t (such as dust accumulation amount and filter resistance), R(t) is the actual dust concentration at time t, T(t) is the ambient temperature at time t, H(t) is the ambient humidity at time t, W(t) is the ambient wind speed at time t, E(t) is the electricity price parameter at time t, and C(t) is the climate parameter at time t.

[0070] If the current time is t, the predicted dust accumulation parameters of the air conditioning equipment at future times can be obtained using the following formula:

[0071] D pred (t+ΔT)=f(X(t),X(t-Δt),…)

[0072] Among them, D pred(t+ΔT) represents the predicted dust accumulation parameter of the air conditioning equipment at a future time (t+ΔT), f(·) is used to characterize the LSTM model, ΔT is the prediction time window, and Δt is the sampling period of the dataset.

[0073] S203, the environmental parameters at the current moment and the cumulative running time of the air conditioning equipment at the current moment are processed by the second model to obtain the predicted dust accumulation parameters of the air conditioning equipment at the current moment, so as to determine the dust accumulation status.

[0074] In this embodiment, the environmental parameters at the current moment and the cumulative running time of the air conditioning equipment at the current moment can be processed by the second model to obtain the predicted dust accumulation parameters of the air conditioning equipment at the current moment, so as to determine the dust accumulation status, such as determining the dust accumulation status of the air conditioning equipment at the current moment.

[0075] There are no strict limitations on the second model; it can include mechanistic models, machine learning models, etc.

[0076] For example, if the current time is time t, the predicted dust accumulation parameters of the air conditioning equipment at the current time can be obtained using the following formula:

[0077] v 积尘 (t)=αC PM2.5 (t) / C 基准 +βH(t) / H 基准 +γA(t) / t 基准

[0078] Among them, v 积尘 (t) represents the predicted dust accumulation rate of the air conditioning equipment at the current moment, C PM2.5 H(t) represents the PM2.5 concentration at the current moment, H(t) represents the ambient humidity at the current moment, A(t) represents the cumulative operating time of the air conditioning equipment at the current moment, and C(t) represents the ambient humidity at the current moment. 基准 H is the baseline value for PM2.5 concentration. 基准 This serves as a baseline value for ambient humidity, such as the critical humidity for mold growth, t. 基准 This refers to the average daily operating time of the air conditioning equipment.

[0079] α, β, and γ are all model parameters of the second model. For example, α + β + γ = 1.

[0080] Optionally, the method further includes collecting the actual dust accumulation parameters of the air conditioning equipment at the current moment, and updating the model parameters of the first model and / or the second model based on the actual dust accumulation parameters of the air conditioning equipment at the current moment. Thus, by taking into account the actual dust accumulation parameters of the air conditioning equipment at the current moment and updating the model parameters of the first model and / or the second model, online optimization of the first model and / or the second model can be achieved, making the first model and / or the second model more closely reflect the actual environment.

[0081] Optionally, the first model can be used to process datasets from multiple historical moments to obtain the predicted dust accumulation parameters of the air conditioning equipment at the current moment. Based on the predicted dust accumulation parameters of the air conditioning equipment at the current moment and the actual dust accumulation parameters of the air conditioning equipment at the current moment, the model parameters of the first model can be updated.

[0082] Optionally, the environmental parameters at the current moment and the cumulative running time of the air conditioning equipment at the current moment can be processed by the second model to obtain the predicted dust accumulation parameters of the air conditioning equipment at the current moment. Based on the predicted dust accumulation parameters of the air conditioning equipment at the current moment and the actual dust accumulation parameters of the air conditioning equipment at the current moment, the model parameters of the second model are updated.

[0083] For example, the processing procedure for the second model is as follows:

[0084] v 积尘 (t)=αC PM2.5 (t) / C 基准 +βH(t) / H 基准 +γA(t) / t 基准

[0085] Taking α as an example, the update process of α is as follows:

[0086] ∈=∣D 实际 -D pred |

[0087]

[0088] Among them, D 实际 D represents the actual dust accumulation parameters. pred To predict dust accumulation parameters, ∈ is the loss function, |·| is the absolute value sign, and α 新 For the updated α, α 旧 For α before the update, The learning rate α controls the step size for each α update.

[0089] For ∈ to α 旧 The partial derivatives of .

[0090] S204, Determine the self-cleaning time of the air conditioning equipment based on the dust accumulation status of the air conditioning equipment.

[0091] S205 determines whether the self-cleaning time falls during peak electricity usage periods.

[0092] S206, if the self-cleaning time falls during peak electricity consumption periods, adjust the self-cleaning time to off-peak electricity consumption periods.

[0093] S207 maintains the self-cleaning time even when the self-cleaning time falls during off-peak electricity usage periods.

[0094] The details of steps S204-S407 can be found in the above embodiments and will not be repeated here.

[0095] It should be noted that this disclosure does not limit the execution sequence of steps S201 to S207. For example, steps S201, S202, S204 to S206 can be implemented as independent embodiments, steps S201, S202, S204, S205, and S207 can be implemented as independent embodiments, steps S203 to S206 can be implemented as independent embodiments, and steps S203 to S205 and S207 can be implemented as independent embodiments.

[0096] The self-cleaning method for air conditioning equipment provided in the embodiments of this disclosure acquires datasets at multiple time points, including historical time points and the current time point. The datasets include at least one of the following parameters: actual dust accumulation parameters of the air conditioning equipment, operating parameters of the air conditioning equipment, environmental parameters, and electricity price parameters. A first model processes the datasets at multiple time points to obtain predicted dust accumulation parameters for the air conditioning equipment at future time points, thereby determining the dust accumulation state. And / or, a second model processes the environmental parameters at the current time point and the cumulative operating time of the air conditioning equipment at the current time point to obtain predicted dust accumulation parameters for the air conditioning equipment at the current time point, thereby determining the dust accumulation state.

[0097] Therefore, the first model can be used to process datasets from multiple time points to obtain predicted dust accumulation parameters for the air conditioning equipment at future time points, thereby determining the dust accumulation status, such as the dust accumulation status of the air conditioning equipment at future time points. And / or, the second model can be used to process environmental parameters at the current time point and the cumulative operating time of the air conditioning equipment at the current time point to obtain predicted dust accumulation parameters for the air conditioning equipment at the current time point, thereby determining the dust accumulation status, such as the dust accumulation status of the air conditioning equipment at the current time point.

[0098] Figure 3 This is a schematic flowchart illustrating a self-cleaning method for an air conditioning device according to another exemplary embodiment, such as... Figure 3As shown, the self-cleaning method for an air conditioning device according to an embodiment of the present disclosure includes the following steps.

[0099] S301, determining a self-cleaning time of the air conditioning device based on a dust accumulation state of the air conditioning device.

[0100] For the related content of step S301, reference can be made to the above-mentioned embodiments, which will not be repeated here.

[0101] S302, determining a predicted dust accumulation risk level of the air conditioning device based on the dust accumulation state.

[0102] Optionally, determining the predicted dust accumulation risk level of the air conditioning device based on the dust accumulation state includes obtaining a candidate dust accumulation risk level corresponding to the dust accumulation state as the predicted dust accumulation risk level. Accordingly, there is a corresponding relationship between the dust accumulation state and the candidate dust accumulation risk level, and the candidate dust accumulation risk level corresponding to the dust accumulation state can be obtained and used as the predicted dust accumulation risk level.

[0103] Optionally, the candidate dust accumulation risk levels of the air conditioning device include a first dust accumulation risk level, a second dust accumulation risk level and a third dust accumulation risk level, wherein the first dust accumulation risk level is greater than the third dust accumulation risk level, and the third dust accumulation risk level is greater than the second dust accumulation risk level.

[0104] Determining the predicted dust accumulation risk level of the air conditioning device based on the dust accumulation state includes: when a predicted dust accumulation parameter of the air conditioning device is less than a first threshold, determining the predicted dust accumulation risk level as the second dust accumulation risk level; when the predicted dust accumulation parameter is greater than or equal to the first threshold and less than a second threshold, determining the predicted dust accumulation risk level as the third dust accumulation risk level; when the predicted dust accumulation parameter is greater than or equal to the second threshold, determining the predicted dust accumulation risk level as the first dust accumulation risk level. Accordingly, the predicted dust accumulation risk level can be determined by considering the magnitude relationship between the predicted dust accumulation parameter and the first threshold and the second threshold.

[0105] For example, taking the predicted dust accumulation parameter as a predicted dust accumulation rate v 积尘 as an example, if v 积尘 < v1, the predicted dust accumulation risk level is determined to be the second dust accumulation risk level; if v1 ≤ v 积尘 < v2, the predicted dust accumulation risk level is determined to be the third dust accumulation risk level; if v 积尘 ≥ v2, the predicted dust accumulation risk level is determined to be the first dust accumulation risk level, where v1 is the first threshold and v2 is the second threshold.

[0106] Optionally, the method further includes collecting the actual dust accumulation parameters of the air conditioning equipment at the current moment, and updating the first threshold and / or the second threshold based on the actual dust accumulation parameters of the air conditioning equipment at the current moment. Thus, the first threshold and / or the second threshold can be optimized online by taking into account the actual dust accumulation parameters of the air conditioning equipment at the current moment, making the first threshold and / or the second threshold more closely reflect the actual environment.

[0107] For example, the update process for the first threshold and the second threshold is as follows:

[0108] v i 新 =v i 旧 +η(v 实际 -v i 旧 )

[0109] Where i is 1 or 2, v1 新 For the updated first threshold, v1 旧 The first threshold before the update, v2 新 For the updated second threshold, v2 旧 The second threshold before the update, v 实际 For the actual dust accumulation rate, η is v i The learning rate controls the amount of v each time. i The update step size, for example, η = 0.1.

[0110] S303, the predicted dust accumulation risk level is determined to be lower than the first dust accumulation risk level.

[0111] In this disclosure, the process of determining whether the self-cleaning time falls within a peak electricity consumption period and subsequent steps can only continue if the predicted dust accumulation risk level is lower than the first dust accumulation risk level. That is, the process of determining whether the self-cleaning time falls within a peak electricity consumption period and subsequent steps can only continue if the predicted dust accumulation risk level is low.

[0112] S304, determine whether the self-cleaning time falls during peak electricity usage periods.

[0113] S305 adjusts the self-cleaning time to the off-peak time if the self-cleaning time falls during peak electricity consumption periods.

[0114] S306 maintains the self-cleaning time even when the self-cleaning time falls during off-peak electricity usage periods.

[0115] The details of steps S304-S306 can be found in the above embodiments and will not be repeated here.

[0116] It should be noted that this disclosure does not limit the execution sequence of steps S301 to S306. For example, steps S301 to S305 can be implemented as independent embodiments, and steps S301 to S304 and S306 can be implemented as independent embodiments.

[0117] The self-cleaning method for air conditioning equipment provided in the embodiments of this disclosure further includes determining whether the self-cleaning time falls before the peak electricity consumption period, and determining a predicted dust accumulation risk level of the air conditioning equipment based on the dust accumulation status, wherein the predicted dust accumulation risk level is less than a first dust accumulation risk level. Therefore, only when the predicted dust accumulation risk level is low will the process of determining whether the self-cleaning time falls within the peak electricity consumption period and subsequent steps continue.

[0118] Based on any of the above embodiments, the method further includes maintaining the self-cleaning time when the predicted dust accumulation risk level is greater than or equal to the first dust accumulation risk level. Therefore, the self-cleaning time can be maintained when the predicted dust accumulation risk level is high; that is, even if the self-cleaning time falls during peak electricity consumption periods, the self-cleaning time is not adjusted, ensuring timely self-cleaning when the predicted dust accumulation risk level is high.

[0119] Based on any of the above embodiments, determining whether the self-cleaning time falls within a peak electricity consumption period further includes determining that the predicted dust accumulation risk level is greater than the second dust accumulation risk level. Therefore, the process of determining whether the self-cleaning time falls within a peak electricity consumption period and subsequent steps can continue only if the predicted dust accumulation risk level is greater than the second dust accumulation risk level but less than the first dust accumulation risk level.

[0120] Figure 4 This is a schematic flowchart illustrating a self-cleaning method for an air conditioning device according to another exemplary embodiment, such as... Figure 4 As shown in the figure, the self-cleaning method of the air conditioning equipment in this embodiment includes the following steps.

[0121] S401, based on the dust accumulation status, determine the predicted dust accumulation risk level of air conditioning equipment.

[0122] The details of step S401 can be found in the above embodiments and will not be repeated here.

[0123] S402, when the predicted dust accumulation risk level is less than or equal to the second dust accumulation risk level, obtain the target climate model of the environment where the air conditioning equipment is located.

[0124] S403, based on the target climate model, determines the target self-cleaning strategy for air conditioning equipment.

[0125] In this disclosure, when the predicted dust accumulation risk level is low, the target climate mode of the environment in which the air conditioning equipment is located can be taken into account to determine the target self-cleaning strategy of the air conditioning equipment, so that the target self-cleaning strategy matches the target climate mode. That is, different target self-cleaning strategies can be adopted under different target climate modes, which improves the diversity of target self-cleaning strategies and is applicable to multiple climate modes.

[0126] It should be noted that the climate model is not limited in many ways; for example, it can include the rainy season, dust storm season, hot season, cold season, smog season, and normal season.

[0127] For example, the plum rain season refers to a climate pattern in which the number of consecutive days when the ambient humidity is greater than or equal to a set humidity level reaches the first set number of days.

[0128] A dust storm season refers to a climate pattern in which the number of consecutive days with PM2.5 concentrations greater than or equal to a first set concentration reaches a second set concentration, where the first set concentration is k. 沙尘 ×C 基准 k 沙尘 is a coefficient.

[0129] The hot season refers to a climate pattern in which the average daily duration of the ambient temperature is greater than or equal to the first set temperature and the air conditioning equipment is in cooling mode is greater than or equal to 8 hours.

[0130] The cold season refers to a climate pattern in which the average daily duration of the ambient temperature is less than or equal to the second set temperature and the air conditioning equipment is in heating mode is greater than or equal to 8 hours.

[0131] A haze season refers to a climate pattern in which the number of consecutive days with PM2.5 concentrations greater than or equal to a second set concentration reaches a third set concentration, where the second set concentration is k. 雾霾 ×C 基准 k 雾霾 is a coefficient.

[0132] The normal season refers to a climate pattern without extreme weather characteristics, which is also the default climate pattern.

[0133] Optionally, there is a correspondence between candidate climate models and candidate self-cleaning strategies for air conditioning equipment.

[0134] Based on the target climate model, the target self-cleaning strategy for air conditioning equipment is determined, including obtaining candidate self-cleaning strategies that correspond to the target climate model, and using them as the target self-cleaning strategy.

[0135] For example, the correspondence between candidate climate models and candidate self-cleaning strategies is shown in Table 1.

[0136] Table 1. Correspondence between candidate climate models and candidate self-cleaning strategies

[0137] Plum rain season <![CDATA[Δt clean =T 基准 ×α 梅雨 ,t 清洁 =t 基准 ×β 梅雨 ]]> Dust season <![CDATA[Δt clean =T 基准 ×α 沙尘 ]]> hot season <![CDATA[t clean Restricted to off-peak electricity usage periods cold season <![CDATA[t clean Limited to the heating shutdown interval, t 清洁 =t 基准 ×β 寒冷 ]]> Haze season <![CDATA[Δt clean =T 基准 ×α 雾霾 ,v 风机 =v 基准 ×γ 雾霾 ]]> Normal Season <![CDATA[Based on v 积尘 and dynamically adjust Δt according to electricity price periods clean >

[0138] Among them, t 清洁 T represents the duration of a single self-cleaning cycle. 基准 t is the baseline value for the self-cleaning time interval. 基准 This is the baseline value for the duration of a single self-cleaning cycle.

[0139] α 梅雨 β 梅雨 All coefficients are greater than 1, meaning that during the rainy season, the self-cleaning interval and the duration of a single self-cleaning operation can be extended.

[0140] α 沙尘 The coefficient is greater than 0 and less than 1, meaning that the self-cleaning interval can be shortened during the sandstorm season.

[0141] During the hot season, t clean Limiting electricity consumption to off-peak hours helps reduce grid load. In addition, since off-peak hours mostly fall at night, it can prevent cooling interruptions during the day.

[0142] During the cold season, t clean Limiting heating to the intervals between heating shutdowns can prevent heating interruptions. β 寒冷 A coefficient greater than 0 and less than 1 means that the self-cleaning interval can be shortened in the cold season.

[0143] α 雾霾 γ is a coefficient greater than 0 and less than 1. 雾霾 A coefficient greater than 1 means that during smoggy seasons, the self-cleaning interval can be shortened and the fan speed can be increased.

[0144] During the normal season, it can be based on v 积尘 Electricity price period dynamic adjustment Δt clean That is, Δt clean With v 积尘 It is related to the electricity price period. For example, it can be based on v 积尘 Determine t clean Determine t clean Is it during peak electricity consumption hours? clean During peak electricity consumption periods, t clean Adjust to off-peak electricity usage times, at t clean During periods of low electricity demand, maintain t clean .

[0145] Optionally, based on the target climate model, a target self-cleaning strategy for the air conditioning equipment is determined. This includes, when there are multiple target climate models, identifying the highest-priority target climate model from among them, and determining the target self-cleaning strategy based on the highest-priority target climate model. Therefore, when there are multiple target climate models, the target self-cleaning strategy can be determined by considering the highest-priority target climate model.

[0146] Understandably, priorities can be set for each candidate climate model.

[0147] For example, the priorities of the candidate climate models are as follows:

[0148] Dust storm season > Haze season > Plum rain season > Cold season > Hot season > Normal season

[0149] That is, the priority is highest during the sandstorm season and lowest during the normal season.

[0150] For example, if the target climate model includes a dust season and a haze season, the target self-cleaning strategy can be determined based on the dust season.

[0151] If the target climate model includes a rainy season and a hot season, the target self-cleaning strategy can be determined based on the rainy season.

[0152] It should be noted that this disclosure does not impose any restrictions on the execution sequence of steps S401 to S403. Figure 4 The example only demonstrates the sequential execution of steps S401 to S403.

[0153] The self-cleaning method for air conditioning equipment provided in the embodiments of this disclosure, when the predicted dust accumulation risk level is less than or equal to a second dust accumulation risk level, obtains the target climate pattern of the environment in which the air conditioning equipment is located, and determines a target self-cleaning strategy for the air conditioning equipment based on the target climate pattern. Therefore, when the predicted dust accumulation risk level is low, the target climate pattern of the environment in which the air conditioning equipment is located can be taken into account to determine the target self-cleaning strategy for the air conditioning equipment, so that the target self-cleaning strategy matches the target climate pattern. That is, different target self-cleaning strategies can be adopted under different target climate patterns, improving the diversity of target self-cleaning strategies and making them applicable to multiple climate patterns.

[0154] Figure 5 This is a schematic flowchart illustrating a self-cleaning method for an air conditioning device according to another exemplary embodiment, such as... Figure 5 As shown in the figure, the self-cleaning method of the air conditioning equipment in this embodiment includes the following steps.

[0155] S501, determine the self-cleaning time of the air conditioning equipment based on the dust accumulation status of the air conditioning equipment.

[0156] S502, based on the dust accumulation status, determines the predicted dust accumulation risk level of air conditioning equipment.

[0157] S503, when the predicted dust accumulation risk level is less than or equal to the second dust accumulation risk level, obtains the target climate model of the environment where the air conditioning equipment is located.

[0158] S504, based on the target climate model, determines the target self-cleaning strategy for air conditioning equipment.

[0159] S505 determines whether the self-cleaning time falls during peak electricity consumption periods if the predicted dust accumulation risk level is greater than the second dust accumulation risk level but less than the first dust accumulation risk level.

[0160] S506 adjusts the self-cleaning time to the off-peak time if the self-cleaning time falls during peak electricity consumption periods.

[0161] S507 maintains its self-cleaning time even when the self-cleaning time falls during off-peak electricity usage periods.

[0162] S508 maintains self-cleaning time when the predicted dust accumulation risk level is greater than or equal to the first dust accumulation risk level.

[0163] The relevant content of steps S501-S508 can be found in the above embodiments, and will not be repeated here.

[0164] Based on any of the above embodiments, the self-cleaning process of the air conditioning equipment is as follows:

[0165] (1) Indoor unit self-cleaning process

[0166] a. Frosting stage

[0167] After the user activates the self-cleaning function, the indoor unit will be cleaned first by default, and the system will directly enter the frosting stage.

[0168] The air conditioner operates in cooling mode, adjusting the opening of the electronic expansion valve to maintain the evaporator pipe temperature T. evap Temperatures below the ambient dew point cause frost to form on the evaporator surface, with ice crystals encapsulating dirt.

[0169] Real-time monitoring of evaporator tube temperature; when the tube temperature drops to the set frosting temperature (e.g., around -5℃) and remains there for a certain period of time... 结霜 (Approximately 30 minutes) until frosting is complete.

[0170] b. Defrosting and sterilization stage

[0171] This stage will automatically begin after the frosting stage is completed.

[0172] Switch the air conditioner to heating mode to increase the evaporator temperature, and simultaneously turn on the electric heating element to raise the evaporator pipe temperature to the sterilization temperature T. 杀菌 (usually above 58℃).

[0173] Continuously monitor the evaporator tube temperature until it reaches T 杀菌 Then start timing and maintain that temperature T. 杀菌 To ensure effective sterilization, the air conditioner fan should remain off during this period to prevent heat loss.

[0174] c. Drying stage

[0175] After the sterilization stage is completed, this stage will begin automatically.

[0176] Turn off the electric heating device, maintain the heating mode, turn on the air conditioner fan, and run it at low speed to promote the evaporation of moisture on the evaporator surface.

[0177] The humidity H on the evaporator surface is monitored using a humidity sensor. evap When the humidity drops to the set safe value H 吹干 Once the drying process is complete, the indoor unit's self-cleaning and sterilization process is considered finished.

[0178] (2) Outdoor unit self-cleaning process

[0179] After the indoor unit completes its self-cleaning process, the outdoor unit's self-cleaning process will automatically begin.

[0180] During outdoor unit operation, the temperature T of the external pipe is monitored in real time. cond Ambient temperature and running time t cond Parameters such as these are used to determine whether the conditions for frosting are met.

[0181] When frost is detected on the outdoor unit's heat exchanger, briefly switch the air conditioner to cooling mode and turn off the outdoor unit's fan to raise the heat exchanger temperature to defrost.

[0182] Continuously monitor the heat exchanger temperature and defrost time. When the heat exchanger temperature rises to T... 杀菌 Once the defrosting time reaches the preset value, the defrosting is considered complete.

[0183] After defrosting is complete, the air conditioning unit automatically resumes heating mode and restarts the outdoor unit fan, operating according to normal heating logic, thus completing a full self-cleaning process for the outdoor unit.

[0184] Figure 6 This is a schematic diagram illustrating the structure of a self-cleaning device for an air conditioning unit according to an exemplary embodiment.

[0185] Reference Figure 6The self-cleaning device 600 of the air conditioning equipment in this embodiment includes: a first determining module 601, a second determining module 602, an adjusting module 603 and a processing module 604.

[0186] The first determining module 601 is configured to determine the self-cleaning time of the air conditioning equipment based on the dust accumulation status of the air conditioning equipment.

[0187] The second determining module 602 is configured to determine whether the self-cleaning time falls during a peak electricity consumption period.

[0188] The adjustment module 603 is configured to adjust the self-cleaning time to an off-peak time if the self-cleaning time falls during a peak electricity consumption period.

[0189] The processing module 604 is configured to maintain the self-cleaning time when the self-cleaning time falls during a period of low electricity consumption.

[0190] In some possible implementations, the adjustment module 603 is further configured to: obtain a low-electricity consumption period that is later than the self-cleaning time and has the shortest interval with the self-cleaning time, as a first low-electricity consumption period; postpone the self-cleaning time to the first low-electricity consumption period; or, obtain a low-electricity consumption period that is earlier than the self-cleaning time and has the shortest interval with the self-cleaning time, as a second low-electricity consumption period; and advance the self-cleaning time to the second low-electricity consumption period.

[0191] In some possible implementations, the adjustment module 603 is further configured to: determine that the delay time of the self-cleaning time is less than or equal to a set time, in order to postpone the self-cleaning time to before the first low electricity consumption period.

[0192] The adjustment module 603 is further configured to update the delay duration to the set duration if the delay duration is greater than the set duration.

[0193] In some possible implementations, the first determining module 601 is further configured to: acquire a dataset at multiple times, including historical times and the current time, the dataset including at least one of the following parameters: actual dust accumulation parameters of the air conditioning equipment, operating parameters of the air conditioning equipment, environmental parameters, and electricity price parameters; process the dataset at multiple times using a first model to obtain predicted dust accumulation parameters of the air conditioning equipment at future times, so as to determine the dust accumulation state; and / or process the environmental parameters at the current time and the cumulative running time of the air conditioning equipment at the current time using a second model to obtain predicted dust accumulation parameters of the air conditioning equipment at the current time, so as to determine the dust accumulation state.

[0194] In some possible implementations, the first determining module 601 is further configured to: collect the actual dust accumulation parameters of the air conditioning equipment at the current moment; and update the model parameters of the first model and / or the second model based on the actual dust accumulation parameters of the air conditioning equipment at the current moment.

[0195] In some possible implementations, when determining whether the self-cleaning time is before the peak electricity consumption period, the second determining module 602 is further configured to: determine the predicted dust accumulation risk level of the air conditioning equipment based on the dust accumulation state; and determine that the predicted dust accumulation risk level is less than the first dust accumulation risk level.

[0196] In some possible implementations, the processing module 604 is further configured to maintain the self-cleaning time if the predicted dust accumulation risk level is greater than or equal to the first dust accumulation risk level.

[0197] In some possible implementations, the second determining module 602 is further configured to: determine that the predicted dust accumulation risk level is greater than the second dust accumulation risk level when determining whether the self-cleaning time is before the peak electricity consumption period;

[0198] The second determining module 602 is further configured to: when the predicted dust accumulation risk level is less than or equal to the second dust accumulation risk level, obtain the target climate pattern of the environment where the air conditioning equipment is located; and determine the target self-cleaning strategy of the air conditioning equipment based on the target climate pattern.

[0199] In some possible implementations, there is a correspondence between candidate climate patterns and candidate self-cleaning strategies of the air conditioning equipment; the second determining module 602 is further configured to: acquire candidate self-cleaning strategies that correspond to the target climate pattern, and use them as the target self-cleaning strategy.

[0200] In some possible implementations, the second determining module 602 is further configured to: determine the highest priority target climate mode from the multiple target climate modes when there are multiple target climate modes; and determine the target self-cleaning strategy based on the highest priority target climate mode.

[0201] In some possible implementations, the candidate dust accumulation risk level of the air conditioning equipment includes a first dust accumulation risk level, a second dust accumulation risk level, and a third dust accumulation risk level, wherein the first dust accumulation risk level is greater than the third dust accumulation risk level, and the third dust accumulation risk level is greater than the second dust accumulation risk level.

[0202] The second determining module 602 is further configured to: determine the predicted dust accumulation risk level as the second dust accumulation risk level when the predicted dust accumulation parameter of the air conditioning equipment is less than the first threshold; determine the predicted dust accumulation risk level as the third dust accumulation risk level when the predicted dust accumulation parameter is greater than or equal to the first threshold and less than the second threshold; and determine the predicted dust accumulation risk level as the first dust accumulation risk level when the predicted dust accumulation parameter is greater than or equal to the second threshold.

[0203] In some possible implementations, the second determining module 602 is further configured to: collect the actual dust accumulation parameters of the air conditioning equipment at the current moment; and update the first threshold and / or the second threshold based on the actual dust accumulation parameters of the air conditioning equipment at the current moment.

[0204] In some possible implementations, the first determining module 601 is further configured to: determine the self-cleaning time based on the dust accumulation state and the energy consumption parameters and / or environmental parameters of the air conditioning equipment.

[0205] In some possible implementations, the first determining module 601 is further configured to: construct an objective function based on the dust accumulation state, the energy consumption parameters and / or the environmental parameters, wherein the independent variables of the objective function include the self-cleaning time; optimize the objective function to obtain the self-cleaning time when the objective function is minimized, and use it as the self-cleaning time.

[0206] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0207] The self-cleaning device for air conditioning equipment provided in the embodiments of this disclosure determines the self-cleaning time of the air conditioning equipment based on the dust accumulation status of the equipment. It also determines whether the self-cleaning time falls within a peak electricity consumption period. If the self-cleaning time falls within a peak electricity consumption period, it adjusts the self-cleaning time to a low electricity consumption period; if the self-cleaning time falls within a low electricity consumption period, it maintains the self-cleaning time. Therefore, the self-cleaning time of the air conditioning equipment can be determined considering the dust accumulation status, meaning different self-cleaning times can be adopted under different dust accumulation statuses. This improves the flexibility of the self-cleaning time, avoids over-cleaning or under-cleaning, and helps extend the service life of the air conditioning equipment.

[0208] In addition, it can be determined whether the self-cleaning time falls during peak electricity consumption periods. If the self-cleaning time falls during peak electricity consumption periods, adjusting the self-cleaning time to off-peak electricity consumption periods can avoid the air conditioning equipment from performing self-cleaning during peak electricity consumption periods, which helps to reduce the grid load and improve the stability and reliability of grid operation.

[0209] Maintain the self-cleaning time when it coincides with a period of low power consumption.

[0210] To implement the above embodiments, this disclosure also proposes an air conditioning device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the self-cleaning method for the air conditioning device provided in this disclosure.

[0211] To implement the above embodiments, this disclosure also proposes a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the steps of the self-cleaning method for air conditioning equipment provided in this disclosure.

[0212] Alternatively, the computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0213] To implement the above embodiments, this disclosure also proposes a chip including an interface circuit and a processing circuit coupled to each other. The interface circuit is used to input or output signals, and the processing circuit is configured to implement the steps of the self-cleaning method for the air conditioning equipment provided in this disclosure.

[0214] Figure 7 This is a schematic diagram illustrating the structure of a chip according to an exemplary embodiment. See also... Figure 7 The diagram shown is a schematic representation of the structure of chip 700, but is not limited thereto.

[0215] Chip 700 includes processing circuit 701, which is configured to perform the self-cleaning method of any of the above air conditioning devices.

[0216] In some embodiments, chip 700 further includes one or more interface circuits 702. Optionally, interface circuit 702 is connected to memory 703, and interface circuit 702 can be used to receive signals from memory 703 or other devices, and interface circuit 702 can be used to send signals to memory 703 or other devices. For example, interface circuit 702 can read instructions stored in memory 703 and send the instructions to processing circuit 701.

[0217] In some embodiments, the interface circuit 702 performs at least one of the communication steps such as sending and / or receiving in the above method, and the processing circuit 701 performs other steps.

[0218] In some embodiments, the terms interface circuit, interface, transceiver pin, transceiver, etc., can be used interchangeably.

[0219] In some embodiments, chip 700 further includes one or more memories 703 for storing instructions. Optionally, all or part of the memories 703 may be located outside of chip 700.

[0220] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program that, when executed by a processor, implements the steps of the self-cleaning method for air conditioning equipment provided in this disclosure.

[0221] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0222] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A self-cleaning method for air conditioning equipment, characterized in that, include: The self-cleaning time of the air conditioning equipment is determined based on the dust accumulation status of the air conditioning equipment; Based on the dust accumulation status, the predicted dust accumulation risk level of the air conditioning equipment is determined; If the predicted dust accumulation risk level is less than the first dust accumulation risk level but greater than the second dust accumulation risk level, determine whether the self-cleaning time is during a peak electricity consumption period; wherein, the second dust accumulation risk level is less than the first dust accumulation risk level. If the self-cleaning time falls during peak electricity consumption periods, the self-cleaning time will be adjusted to during off-peak electricity consumption periods. The self-cleaning time is maintained even when it falls during a period of low electricity consumption. If the predicted dust accumulation risk level is greater than or equal to the first dust accumulation risk level, the self-cleaning time is maintained; If the predicted dust risk level is less than or equal to the second dust risk level, obtain the target climate pattern of the environment where the air conditioning equipment is located. Based on the target climate pattern, the target self-cleaning strategy for the air conditioning equipment is determined.

2. The method according to claim 1, characterized in that, Adjusting the self-cleaning time to off-peak electricity usage includes: The electricity off-peak time period that is later than the self-cleaning time and has the shortest interval with the self-cleaning time is obtained as the first electricity off-peak time period. The self-cleaning time is postponed to the first off-peak electricity consumption period; or... The electricity off-peak time period that is earlier than the self-cleaning time and has the shortest interval with the self-cleaning time is obtained as the second electricity off-peak time period. The self-cleaning time is moved forward to the second off-peak electricity consumption period.

3. The method according to claim 2, characterized in that, The step of postponing the self-cleaning time to before the first off-peak electricity consumption period also includes: The delay time for the self-cleaning moment is determined to be less than or equal to the set time. The method further includes: If the delay duration is greater than the set duration, the delay duration will be updated to the set duration.

4. The method according to claim 1, characterized in that, The method further includes: Acquire datasets at multiple time points, including historical time points and current time points. The datasets include at least one of the following parameters: actual dust accumulation parameters of the air conditioning equipment, operating parameters of the air conditioning equipment, environmental parameters, and electricity price parameters. By processing the datasets from the multiple time points using the first model, predicted dust accumulation parameters for the air conditioning equipment at future time points are obtained to determine the dust accumulation state; and / or, The second model processes the environmental parameters at the current moment and the cumulative operating time of the air conditioning equipment at the current moment to obtain the predicted dust accumulation parameters of the air conditioning equipment at the current moment, so as to determine the dust accumulation state.

5. The method according to claim 4, characterized in that, The method further includes: Collect the actual dust accumulation parameters of the air conditioning equipment at the current moment; Based on the actual dust accumulation parameters of the air conditioning equipment at the current moment, the model parameters of the first model and / or the second model are updated.

6. The method according to claim 1, characterized in that, There is a correspondence between candidate climate patterns and candidate self-cleaning strategies of the air conditioning equipment; The step of determining the target self-cleaning strategy for the air conditioning equipment based on the target climate model includes: Candidate self-cleaning strategies that correspond to the target climate model are obtained and used as the target self-cleaning strategy.

7. The method according to claim 1, characterized in that, The step of determining the target self-cleaning strategy for the air conditioning equipment based on the target climate model includes: When there are multiple target climate models, the target climate model with the highest priority is determined from among the multiple target climate models; The target self-cleaning strategy is determined based on the highest priority target climate model.

8. The method according to any one of claims 1-7, characterized in that, The candidate dust accumulation risk levels of the air conditioning equipment include a first dust accumulation risk level, a second dust accumulation risk level, and a third dust accumulation risk level, wherein the first dust accumulation risk level is greater than the third dust accumulation risk level, and the third dust accumulation risk level is greater than the second dust accumulation risk level. The step of determining the predicted dust accumulation risk level of the air conditioning equipment based on the dust accumulation status includes: If the predicted dust accumulation parameter of the air conditioning equipment is less than the first threshold, the predicted dust accumulation risk level is determined to be the second dust accumulation risk level. If the predicted dust accumulation parameter is greater than or equal to the first threshold and less than the second threshold, the predicted dust accumulation risk level is determined to be the third dust accumulation risk level. If the predicted dust accumulation parameter is greater than or equal to the second threshold, the predicted dust accumulation risk level is determined to be the first dust accumulation risk level.

9. The method according to claim 8, characterized in that, The method further includes: Collect the actual dust accumulation parameters of the air conditioning equipment at the current moment; The first threshold and / or the second threshold are updated based on the actual dust accumulation parameters of the air conditioning equipment at the current moment.

10. The method according to any one of claims 1-7, characterized in that, Determining the self-cleaning time of the air conditioning unit based on its dust accumulation status includes: The self-cleaning time is determined based on the dust accumulation status, as well as the energy consumption parameters and / or environmental parameters of the air conditioning equipment.

11. The method according to claim 10, characterized in that, Determining the self-cleaning time based on the dust accumulation state, and the energy consumption parameters and / or environmental parameters of the air conditioning equipment, includes: Based on the dust accumulation state, the energy consumption parameters and / or the environmental parameters, an objective function is constructed, wherein the independent variables of the objective function include the self-cleaning time; The objective function is optimized to obtain the self-cleaning time when the objective function is minimized, which is taken as the self-cleaning time.

12. A self-cleaning device for an air conditioning unit, characterized in that, include: The first determining module is configured to determine the self-cleaning time of the air conditioning equipment based on the dust accumulation status of the air conditioning equipment; The second determining module is configured to determine the predicted dust accumulation risk level of the air conditioning equipment based on the dust accumulation status. The second determining module is further configured to determine whether the self-cleaning time is during a peak electricity consumption period when the predicted dust accumulation risk level is less than the first dust accumulation risk level and greater than the second dust accumulation risk level; wherein the second dust accumulation risk level is less than the first dust accumulation risk level. The adjustment module is configured to adjust the self-cleaning time to an off-peak time if the self-cleaning time falls during a peak electricity consumption period. The processing module is configured to maintain the self-cleaning time when the self-cleaning time falls during a period of low electricity consumption. The processing module is further configured to maintain the self-cleaning time when the predicted dust accumulation risk level is greater than or equal to the first dust accumulation risk level. The second determining module is further configured to, when the predicted dust accumulation risk level is less than or equal to the second dust accumulation risk level, obtain the target climate pattern of the environment in which the air conditioning equipment is located; and determine the target self-cleaning strategy of the air conditioning equipment based on the target climate pattern.

13. The apparatus according to claim 12, characterized in that, The adjustment module is also configured to: The electricity off-peak time period that is later than the self-cleaning time and has the shortest interval with the self-cleaning time is obtained as the first electricity off-peak time period. The self-cleaning time is postponed to the first off-peak electricity consumption period; or... The electricity off-peak time period that is earlier than the self-cleaning time and has the shortest interval with the self-cleaning time is obtained as the second electricity off-peak time period. The self-cleaning time is moved forward to the second off-peak electricity consumption period.

14. The apparatus according to claim 13, characterized in that, The adjustment module, which postpones the self-cleaning time to before the first off-peak electricity consumption period, is further configured to: The delay time for the self-cleaning moment is determined to be less than or equal to the set time. The adjustment module is also configured to: If the delay duration is greater than the set duration, the delay duration will be updated to the set duration.

15. An air conditioning device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the steps of the method according to any one of claims 1-11.

16. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When executed by a processor, the program instructions implement the steps of the method described in any one of claims 1-11.

17. A chip, characterized in that, The chip includes an interface circuit and a processing circuit coupled to each other. The interface circuit is used to input or output signals, and the processing circuit is configured to implement the steps of the method according to any one of claims 1-11.

18. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-11.

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