Frequency adjustment method and device, air conditioner and storage medium

Abstract

This disclosure relates to a frequency regulation method, apparatus, air conditioner, and storage medium, and pertains to the field of air conditioning technology. It includes: predicting the indoor ambient temperature to obtain a predicted temperature, which is the desired indoor ambient temperature; determining the temperature difference between the predicted temperature and a set temperature, which is the target temperature that the air conditioner needs to reach after adjusting the indoor temperature; and, based on the temperature difference, downwardly adjusting a first proportional coefficient of a PID model to a second proportional coefficient; wherein, the larger the temperature difference, the greater the magnitude of the change between the first and second proportional coefficients; the PID model is used to adjust the operating frequency of the air conditioner's compressor according to the set temperature and the actual indoor temperature, so that the actual temperature output by the air conditioner approximates the set temperature. Using the frequency regulation method proposed in this disclosure, the user experience of the air conditioner can be guaranteed while improving the energy efficiency of the air conditioner without using hardware energy-saving measures.

Description

Technical Field

[0001] This disclosure relates to the field of air conditioning technology, and in particular to a frequency regulation method, device, air conditioner and storage medium. Background Technology

[0002] Currently, air conditioners include compressors, and the higher the operating frequency of the compressor, the faster the air conditioner cools down.

[0003] In related technologies, the air conditioning cooling process includes an open-loop stage and a closed-loop stage. The open-loop stage refers to the air conditioner quickly reaching the set temperature during cooling, while the closed-loop stage refers to maintaining the indoor temperature near the set temperature after it has reached that temperature. However, even when the air conditioner is in the closed-loop stage, it still consumes some electricity during cooling, resulting in a relatively low energy efficiency. Summary of the Invention

[0004] To overcome the problems existing in related technologies, this disclosure provides a frequency regulation method, apparatus, air conditioner, and storage medium.

[0005] According to a first aspect of the present disclosure, a frequency adjustment method is provided, comprising:

[0006] The indoor temperature is predicted to obtain a predicted temperature, which is the desired indoor temperature.

[0007] Determine the temperature difference between the predicted temperature and the set temperature, where the set temperature is the target temperature that the air conditioner needs to reach after adjusting the indoor temperature;

[0008] Based on the temperature difference, the first proportional coefficient of the PID model is adjusted downward to the second proportional coefficient; wherein, the larger the temperature difference, the greater the change amplitude of the first proportional coefficient to the second proportional coefficient; the PID model is used to adjust the operating frequency of the air conditioner compressor according to the set temperature and the actual indoor temperature, so that the actual temperature output by the air conditioner is close to the set temperature.

[0009] Optionally, the step of adjusting the proportional coefficient of the PID model from the first proportional coefficient to the second proportional coefficient based on the temperature difference includes:

[0010] The amplitude of the change is obtained based on the temperature difference and the preset constant;

[0011] The first proportional coefficient is adjusted to the second proportional coefficient based on the change amplitude.

[0012] Optionally, obtaining the change amplitude based on the temperature difference and a preset constant includes:

[0013] Determine the deviation range between the predicted temperature and the actual temperature, and the temperature difference range.

[0014] Determine the preset constants corresponding to the deviation range and the temperature difference range;

[0015] The change amplitude is obtained based on the temperature difference and the preset constant.

[0016] Optionally, the deviation interval includes a first deviation interval and a second deviation interval, wherein the minimum value of the first deviation interval is greater than the maximum value of the second deviation interval;

[0017] Wherein, the preset constant corresponding to the first deviation interval is less than the preset constant corresponding to the second deviation interval.

[0018] Optionally, the method further includes:

[0019] If the deviation is within the second deviation interval, the predicted temperature is corrected upward to the target predicted temperature; wherein the difference between the target predicted temperature and the predicted temperature is greater than or equal to the absolute value of the minimum value of the second deviation interval.

[0020] Optionally, within the same deviation range and the same temperature difference range, the preset constant corresponding to the heating stage is smaller than the preset constant corresponding to the cooling stage.

[0021] Optionally, the temperature difference range corresponding to the heating stage includes a first temperature difference range, a second temperature difference range, and a third temperature difference range, wherein the minimum value of the first temperature difference range is greater than the maximum value of the second temperature difference range, and the minimum value of the second temperature difference range is greater than the maximum value of the third temperature difference range.

[0022] Within the same deviation interval, the preset constants corresponding to the first temperature difference interval, the second temperature difference interval, and the third temperature difference interval decrease sequentially.

[0023] Optionally, the temperature difference range corresponding to the cooling stage includes a first temperature difference range, a second temperature difference range, a third temperature difference range, and a fourth temperature difference range. The minimum value of the first temperature difference range is greater than the maximum value of the second temperature difference range, the minimum value of the second temperature difference range is greater than the maximum value of the third temperature difference range, and the minimum value of the third temperature difference range is greater than the maximum value of the fourth temperature difference range.

[0024] Within the same deviation interval, the preset constants corresponding to the first temperature difference interval, the second temperature difference interval, the third temperature difference interval, and the fourth temperature difference interval decrease sequentially.

[0025] Optionally, the method further includes:

[0026] If the deviation is outside the range between the first deviation interval and the second deviation interval, the first proportional coefficient is not corrected.

[0027] Optionally, predicting the indoor temperature to obtain the predicted temperature includes:

[0028] The predicted temperature for this prediction is obtained by taking multiple indoor temperatures and / or the predicted temperatures obtained from the previous prediction by the temperature prediction model within a preset time period before the air conditioner is turned on.

[0029] Optionally, the step of using multiple indoor temperatures within a preset time period before the air conditioner is turned on, along with the previously predicted temperature obtained from the temperature prediction model, as input parameters for the temperature prediction model to obtain the current predicted temperature includes:

[0030] Remove the first input parameter from the plurality of input parameters, and add the predicted temperature obtained by the temperature prediction model in the previous prediction to the end of the plurality of input parameters, and use it as the input parameter for the temperature prediction model in this prediction, to obtain the predicted temperature for this prediction.

[0031] According to a second aspect of the present disclosure, a frequency adjustment device is provided, comprising:

[0032] The prediction module is configured to predict the temperature of the indoor environment and obtain a predicted temperature, which is the desired temperature of the indoor environment.

[0033] The temperature difference module is configured to determine the temperature difference between the predicted temperature and the set temperature, wherein the set temperature is the target temperature that the air conditioner needs to reach after adjusting the indoor temperature.

[0034] The correction module is configured to adjust the first proportional coefficient of the PID model downward to a second proportional coefficient based on the temperature difference; wherein, the larger the temperature difference, the greater the change amplitude of the first proportional coefficient to the second proportional coefficient; the PID model is used to adjust the operating frequency of the air conditioner compressor based on the set temperature and the actual indoor temperature, so that the actual temperature output by the air conditioner is close to the set temperature.

[0035] According to a third aspect of the present disclosure, an air conditioner is provided, comprising:

[0036] processor;

[0037] Memory used to store processor-executable instructions;

[0038] The processor is configured as follows:

[0039] The steps of performing the frequency adjustment method provided in the first aspect of the embodiments of this disclosure.

[0040] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the steps of the frequency adjustment method provided in the first aspect of the present disclosure.

[0041] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0042] By using the above technical solution, when the temperature difference between the predicted temperature and the set temperature is large, it can be determined that the temperature difference between the predicted temperature and the actual temperature is large. In this way, the proportional coefficient of the PID model can be controlled to decrease from the first proportional coefficient to the second proportional coefficient. The larger the change amplitude, the slower the actual operating frequency of the compressor and the faster the cooling speed of the air conditioner, so that the actual indoor temperature can approach the predicted temperature.

[0043] In this process, firstly, since the predicted temperature is a temperature prediction model that predicts the temperature in line with user habits and the air conditioner installation environment, the indoor actual temperature will approach the predicted temperature, making the indoor actual temperature closer to the predicted temperature that users are accustomed to, thus improving the user experience; secondly, since controlling the actual temperature to rise to the predicted temperature will control the compressor's operating frequency to decrease, and the compressor's rotation speed to decrease, the energy-saving level of the air conditioner will be further improved; thirdly, this disclosure improves the energy-saving level of the air conditioner from the software aspect in the closed-loop stage of the air conditioner, without the need for additional hardware, thereby improving the energy-saving level of the air conditioner from the hardware aspect, thus reducing the production cost of the air conditioner.

[0044] 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

[0045] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0046] Figure 1 This is a flowchart illustrating a frequency adjustment method according to an exemplary embodiment.

[0047] Figure 2 This is a schematic diagram illustrating a comparison between a predicted temperature and the actual temperature according to an exemplary embodiment.

[0048] Figure 3 This is a schematic diagram illustrating a PID model according to an exemplary embodiment.

[0049] Figure 4 This is a schematic diagram illustrating a PID model according to an exemplary embodiment.

[0050] Figure 5 This is a schematic diagram illustrating a PID model according to an exemplary embodiment.

[0051] Figure 6 This is a schematic diagram illustrating a first deviation range according to an exemplary embodiment.

[0052] Figure 7 This is a schematic diagram illustrating a second deviation range according to an exemplary embodiment.

[0053] Figure 8 This is a schematic diagram of a frequency adjustment device according to an exemplary embodiment.

[0054] Figure 9 This is a schematic diagram of a frequency adjustment device according to an exemplary embodiment. Detailed Implementation

[0055] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. 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.

[0056] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0057] Inverter air conditioners are classified into several energy efficiency levels based on their Air Purification Factor (ADF). The energy efficiency of an air conditioner is typically determined by both its hardware and software. The APF value reflects the energy efficiency of the hardware, while the energy efficiency of the software is reflected by the air conditioner's control algorithm.

[0058] Currently, most air conditioning companies improve the energy efficiency of air conditioners by focusing on the hardware. However, improving the energy efficiency of the hardware inevitably leads to higher production costs. Therefore, this publication aims to improve the energy efficiency of air conditioners by focusing on the software aspect.

[0059] For related technologies, please refer to Figure 2 As shown, Figure 2The black curve at the bottom represents the actual temperature change curve of the room during air conditioning cooling. When the user sets the set temperature, in the open-loop phase, the air conditioner controls the compressor to operate at a higher actual operating frequency, allowing the room temperature to quickly reach the set temperature. Due to the high actual operating frequency of the compressor, the rotor inside the compressor rotates faster, resulting in overshoot, causing the room temperature to drop below the set temperature. As the compressor's actual operating frequency increases, the room temperature will rise back to near the set temperature. For example, if the set temperature is 16℃, during air conditioning cooling, the actual room temperature will quickly reach 16℃, then drop below 16℃, and finally rise back to around 16℃, stabilizing at approximately 16℃. In the closed-loop phase, the air conditioner controls the compressor to operate at a lower actual operating frequency compared to the open-loop phase, maintaining the room temperature near the set temperature. Even when air conditioning is in the closed-loop phase, it still consumes some electricity, resulting in a lower energy efficiency. In short, the open-loop phase refers to the stage where the air conditioner's cooling speed drops rapidly, while the closed-loop phase refers to the stage where the actual room temperature is maintained near the set temperature.

[0060] Based on this, this disclosure proposes a frequency adjustment method. Figure 1 This is a flowchart illustrating a frequency adjustment method according to an exemplary embodiment, such as... Figure 1 As shown, the frequency adjustment method used in the controller includes the following steps.

[0061] In step S11, the temperature of the indoor environment is predicted to obtain a predicted temperature, which is the desired temperature of the indoor environment.

[0062] Indoor temperature can be predicted using temperature prediction models. These models provide an ideal indoor temperature that aligns with user habits and the environment in which the air conditioning is configured. The temperature prediction model uses the indoor temperature within a preset timeframe before the air conditioner starts to predict the indoor temperature for a future period after the air conditioner starts. For example, please refer to... Figure 2 As shown, Figure 2 The black dotted curve shown is the curve of the ideal indoor temperature predicted by the temperature prediction model, which meets the user's expectations. The predicted temperature in this curve slowly decreases to the set temperature, unlike the actual temperature curve, which rapidly decreases below the set temperature and then rises back to near the set temperature. Therefore, there will be no overshoot phenomenon where the indoor predicted temperature is lower than the set temperature.

[0063] In step S12, the temperature difference between the predicted temperature and the set temperature is determined, where the set temperature is the target temperature that the air conditioner needs to reach after adjusting the indoor temperature.

[0064] The set temperature can be a temperature set by the user, or a set temperature triggered by the air conditioner at a certain time based on the user's lifestyle.

[0065] In the closed-loop stage of air conditioning, the greater the temperature difference between the predicted temperature and the set temperature, the greater the adjustment range from the actual operating frequency of the compressor to the desired operating frequency. The actual operating frequency corresponds to the real temperature, while the desired operating frequency corresponds to the desired predicted temperature.

[0066] In step S13, the first proportional coefficient of the PID model is adjusted downward to the second proportional coefficient based on the temperature difference.

[0067] The first proportional coefficient is the initial proportional coefficient before the PID model is corrected, and the second proportional coefficient is the target proportional coefficient after correction by the change amplitude.

[0068] In air conditioning control, the higher the actual operating frequency of the compressor, the faster the air conditioner cools down and the lower the actual temperature reached in the indoor environment.

[0069] The PID model is used to adjust the operating frequency of the air conditioner compressor according to the set temperature and the actual indoor temperature, so that the actual temperature output by the air conditioner is close to the set temperature.

[0070] Please see Figure 3 As shown, in the closed-loop stage of the air conditioner, the user's set temperature and the actual indoor temperature can be input into the PID model. The PID model adjusts the actual operating frequency of the compressor and outputs the calculated actual temperature. Then, the error between the calculated actual temperature and the set temperature is determined. If the error is greater than a preset value, the calculated actual temperature is fed back to the input of the PID model. The PID model then adjusts the actual operating frequency of the compressor again to determine the error between the calculated actual temperature and the set temperature. This process is repeated until the error is less than or equal to the preset value. During this process, the PID model, through this adjustment mechanism, adjusts the actual operating frequency of the compressor when it detects a large error between the actual temperature and the set temperature, so that the error between the actual temperature and the set temperature gradually decreases.

[0071] In the PID model, the actual operating frequency is adjusted using a proportional gain (KP), an integral gain (KI), and a derivative gain (KD). The proportional gain controls the speed at which the temperature rises from the actual temperature to the set temperature; a higher proportional gain results in a faster rise. The integral gain accumulates the temperature difference between the actual and set temperatures at each calculation and reduces this difference more quickly. The derivative gain stabilizes the actual temperature near the set temperature, preventing overshoot caused by the actual temperature dropping below the set temperature.

[0072] The following example will illustrate the proportional coefficient, integral coefficient, and differential coefficient.

[0073] For the scaling factor, please refer to Figure 4 As shown, assuming the goal is to hover the drone at a height of 10m (h = 10), and the drone's current height is 2m (h0 = 2), then 10m is the set height, and 2m is the drone's current actual height. The adjustment height for each adjustment is equal to the height difference multiplied by a scaling factor. See also... Figure 4 During the initial adjustment, there was an 8-meter height difference between the actual and set height. Assuming a scaling factor of 0.5, if the adjusted height is 4 meters, then the drone's actual height will increase from 2 meters to 6 meters. (See also...) Figure 5 In the second adjustment, there was a 4-meter height difference between the actual and set heights. This time, the adjustment was 2 meters, increasing the drone's actual height from 6 meters to 8 meters. This demonstrates that by using the same scaling factor, the drone's actual height can continuously approach the set height. Furthermore, as the height difference between the actual and set heights decreases, the adjustment height of the drone decreases with each adjustment. This example also shows that a larger scaling factor results in a larger adjustment height, allowing the drone to approach the set height from its current actual height more quickly.

[0074] Regarding integral coefficients, various interferences can occur during the drone's ascent. Even with adjustments to the proportional coefficient, the drone may still fail to reach the set altitude. To ensure the drone reaches the set altitude, integral coefficients can be used for control. Integral control accumulates the difference between the calculated actual altitude and the set altitude. For example, if the first calculated difference is 8 and the second is 4, the accumulated error is 12. Assuming an integral coefficient of 0.1, the resulting integral adjustment coefficient is 1.2. This allows the drone to ascend to 9.2m from its previous actual altitude of 8m, thus approaching the set altitude of 10m. Therefore, integral coefficients enable the drone to accurately reach the set altitude.

[0075] Regarding the differential coefficient, if the drone continues to operate at its previous frequency after ascending to 9.2m, it might exceed 10m. For example, the drone might fly to an altitude of 12m and then slowly descend back to 10m. This overshoot phenomenon would undoubtedly lead to a poor user experience. Therefore, the differential coefficient can be determined based on the altitude difference between the next and previous moments. If the altitude difference in the next moment is greater than the altitude difference in the previous moment, it is considered that the altitude difference is gradually increasing, and the control intensity needs to be increased to reduce the altitude difference. Conversely, if the altitude difference in the next moment is less than the altitude difference in the previous moment, it is considered that the altitude difference is gradually decreasing, and the control intensity needs to be decreased to increase the altitude difference. Ultimately, this allows the drone to smoothly and gradually reach the set altitude.

[0076] In the closed-loop stage of air conditioning, a large temperature difference between the predicted temperature and the set temperature indicates a significant error between the set temperature and the predicted temperature that aligns with user habits. This disclosure aims to control the actual indoor temperature to move closer to the predicted temperature. This can be achieved by reducing the proportional gain of the PID model, decreasing the actual operating frequency of the compressor, and reducing the cooling speed of the air conditioner, thereby bringing the actual indoor temperature closer to the predicted temperature. Please refer to [link / reference]. Figure 3 As shown, this can be viewed as the proportional coefficient of the control PID model decreasing, thereby allowing Figure 2 The lower black curve gradually approaches the upper black dotted curve. The black curve is the curve showing the actual change in indoor temperature based on the compressor's actual operating frequency, while the black dotted curve is the curve showing the predicted temperature change based on user habits and the air conditioning installation environment, as predicted by the temperature prediction model.

[0077] Understandably, the larger the temperature difference, the greater the magnitude of the change in the proportional gain of the PID model from the first proportional gain to the second proportional gain. For situations where the temperature difference between the predicted temperature and the set temperature is large, please refer to [further details]. Figure 2 As shown, in the closed-loop stage of air conditioning, since the predicted temperature is above the set temperature and the actual temperature is below the set temperature, the greater the temperature difference between the predicted temperature and the set temperature, the greater the temperature difference between the predicted temperature and the actual temperature. At this time, the downward adjustment of the proportional coefficient can be controlled to make the actual operating frequency of the compressor smaller and the cooling speed slower, so that the actual temperature can be closer to the predicted temperature.

[0078] Conversely, the smaller the temperature difference between the predicted temperature and the set temperature, the smaller the temperature difference between the predicted temperature and the actual temperature. At this time, the downward adjustment of the proportional coefficient can be reduced, thereby making the actual operating frequency of the compressor relatively larger and the cooling speed relatively faster. It can also make the actual temperature closer to the predicted temperature, without rising to exceed the predicted temperature.

[0079] By using the above technical solution, when the temperature difference between the predicted temperature and the set temperature is large, it can be determined that the temperature difference between the predicted temperature and the actual temperature is large. In this way, the proportional coefficient of the PID model can be controlled to decrease from the first proportional coefficient to the second proportional coefficient. The larger the change amplitude, the slower the actual operating frequency of the compressor and the faster the cooling speed of the air conditioner, so that the actual indoor temperature can approach the predicted temperature.

[0080] In this process, firstly, since the predicted temperature is a temperature prediction model that predicts the temperature in line with user habits and the air conditioner installation environment, the indoor actual temperature will approach the predicted temperature, making the indoor actual temperature closer to the predicted temperature that users are accustomed to, thus improving the user experience; secondly, since controlling the actual temperature to rise to the predicted temperature will control the compressor's operating frequency to decrease, and the compressor's rotation speed to decrease, the energy-saving level of the air conditioner will be further improved; thirdly, this disclosure improves the energy-saving level of the air conditioner from the software aspect in the closed-loop stage of the air conditioner, without the need for additional hardware, thereby improving the energy-saving level of the air conditioner from the hardware aspect, thus reducing the production cost of the air conditioner.

[0081] The following describes a specific embodiment of step S13 above, which explains how to obtain the change amplitude, and specifically includes the following steps.

[0082] Step A1: Obtain the change amplitude based on the temperature difference and the preset constant.

[0083] The product of temperature difference, preset constant, and first proportional coefficient can be used as the change amplitude. Step A1 includes: determining the deviation range where the deviation between the predicted temperature and the actual temperature is located, and the temperature difference range where the temperature difference is located; determining the preset constant corresponding to the deviation range and the temperature difference range; and using the product of temperature difference, preset constant, and first proportional coefficient as the change amplitude.

[0084] Please see Figure 6 and Figure 7 As shown, the deviation range includes a first deviation range and a second deviation range, where the minimum value of the first deviation range is greater than the maximum value of the second deviation range. For example, the first deviation range is [0, 0.5), and the second deviation range is [-0.5, 0).

[0085] The temperature difference range has two phases: during the heating phase of the air conditioner, there are a first temperature difference range, a second temperature difference range, and a third temperature difference range; during the cooling phase of the air conditioner, there are a first temperature difference range, a second temperature difference range, a third temperature difference range, and a fourth temperature difference range. For example, the first temperature difference range is (2, +∞), the second temperature difference range is (0.5, 2], the third temperature difference range is (-0.5, 0.5], and the fourth temperature difference range is (-∞, -0.5).

[0086] Please see Figure 6 As shown, when the deviation range is the first deviation range, it can be divided into a heating stage and a cooling stage to distinguish different change amplitudes.

[0087] Please see Figure 6 As shown, the heating phase within the first deviation range includes the following three scenarios.

[0088] In the first scenario, where the deviation range is the first deviation range [0, 0.5) and the temperature range is the first temperature range (2, +∞), the corresponding preset constant can be 0.1, and the change amplitude is KP*m*△T. 温差 KP is the first proportionality coefficient, m is a preset constant, and ΔT 温差 If it's the temperature difference, then the amplitude of the change obtained after substituting the preset constant and the temperature difference into the correction function is KP*0.1*△T. 温差 The second proportionality coefficient is KP - KP * 0.1 * ΔT 温差 .

[0089] In the second scenario, where the deviation range is the first deviation range [0, 0.5) and the temperature difference range is the second temperature difference range (0.5, 2), the corresponding preset constant can be 0.08, and the change amplitude is KP*m*△T. 温差 KP is the first proportionality coefficient, m is a preset constant, and ΔT 温差 If it's the temperature difference, then the amplitude of the change obtained after substituting the preset constant and the temperature difference into the correction function is KP*0.08*△T. 温差 The second proportionality coefficient is KP - KP * 0.08 * ΔT 温 Difference.

[0090] In the third scenario, if the deviation range is the first deviation range [0, 0.5) and the temperature range is the third temperature range (-0.5, 0.5), then the first proportional coefficient is not corrected.

[0091] Please see Figure 6 As shown, the cooling phase within the first deviation interval includes the following four scenarios.

[0092] In the first scenario, where the deviation range is the first deviation range [0, 0.5) and the temperature range is the first temperature range (2, +∞), the corresponding preset constant can be 0.12, and the change amplitude is KP*m*△T. 温差 KP is the first proportionality coefficient, m is a preset constant, and ΔT 温差 If it's the temperature difference, then the amplitude of the change obtained after substituting the preset constant and the temperature difference into the correction function is KP*0.12*△T.温差 The second proportionality coefficient is KP - KP * 0.12 * ΔT 温差 .

[0093] In the second scenario, where the deviation range is the first deviation range [0, 0.5) and the temperature difference range is the second temperature difference range (0.5, 2), the corresponding preset constant can be 0.1, and the change amplitude is KP*m*△T. 温差 KP is the first proportionality coefficient, m is a preset constant, and ΔT 温差 If it's the temperature difference, then the amplitude of the change obtained after substituting the preset constant and the temperature difference into the correction function is KP*0.1*△T. 温差 The second proportionality coefficient is KP - KP * 0.1 * ΔT 温差 .

[0094] In the third scenario, where the deviation range is the first deviation range [0, 0.5) and the temperature difference range is the third temperature difference range (-0.5, 0.5), the corresponding preset constant can be 0.08, and the change amplitude is KP*m*△T. 温差 KP is the first proportionality coefficient, m is a preset constant, and ΔT 温差 If it's the temperature difference, then the amplitude of the change obtained after substituting the preset constant and the temperature difference into the correction function is KP*0.08*△T. 温差 The second proportionality coefficient is KP - KP * 0.08 * ΔT 温 Difference.

[0095] In the fourth scenario, when the deviation range is the first deviation range [0, 0.5) and the temperature range is the fourth temperature range (-∞, -0.5), the first proportional coefficient is not corrected.

[0096] Please see Figure 7 As shown, when the deviation is located in the second deviation range, it can also be divided into a heating stage and a cooling stage to distinguish different change amplitudes.

[0097] Please see Figure 7 As shown, the heating phase within the second deviation range includes the following three scenarios.

[0098] In the first scenario, where the deviation falls within the second deviation interval [-0.5, 0) and the temperature difference falls within the first temperature difference interval (2, +∞), the corresponding preset constant can be 0.12, and the change amplitude is KP*m*△T. 温差 KP is the first proportionality coefficient, m is a preset constant, and ΔT 温差 If it's the temperature difference, then the amplitude of the change obtained after substituting the preset constant and the temperature difference into the correction function is KP*0.12*△T.温差 The second proportionality coefficient is KP - KP * 0.12 * ΔT 温 Difference.

[0099] In the second scenario, where the deviation range is the second deviation range [-0.5, 0) and the temperature difference range is the second temperature difference range (0.5, 2), the corresponding preset constant can be 0.1, and the change amplitude is KP*m*△T. 温差 KP is the first proportionality coefficient, m is a preset constant, and ΔT 温差 If it's the temperature difference, then the amplitude of the change obtained after substituting the preset constant and the temperature difference into the correction function is KP*0.1*△T. 温差 The second proportionality coefficient is KP - KP * 0.1 * ΔT 温差 .

[0100] In the third scenario, if the deviation range is the second deviation range [-0.5, 0) and the temperature range is the third temperature range (-0.5, 0.5), then the first proportional coefficient is not corrected.

[0101] Please see Figure 7 As shown, the cooling phase within the second deviation interval includes the following four scenarios.

[0102] In the first scenario, where the deviation falls within the second deviation interval [-0.5, 0) and the temperature difference falls within the first temperature difference interval (2, +∞), the corresponding preset constant can be 0.15, and the change amplitude is KP*m*△T. 温差 KP is the first proportionality coefficient, m is a preset constant, and ΔT 温差 If it's the temperature difference, then the amplitude of the change obtained after substituting the preset constant and the temperature difference into the correction function is KP*0.15*△T. 温差 The second proportionality coefficient is KP - KP * 0.15 * ΔT 温 Difference.

[0103] In the second scenario, where the deviation range is the second deviation range [-0.5, 0) and the temperature difference range is the second temperature difference range (0.5, 2), the corresponding preset constant can be 0.12, and the change amplitude is KP*m*△T. 温差 KP is the first proportionality coefficient, m is a preset constant, and ΔT 温差 If it's the temperature difference, then the amplitude of the change obtained after substituting the preset constant and the temperature difference into the correction function is KP*0.12*△T. 温差 The second proportionality coefficient is KP - KP * 0.12 * ΔT 温差 .

[0104] In the third scenario, where the deviation falls within the second deviation range [-0.5, 0) and the temperature difference falls within the third temperature difference range (-0.5, 0.5), the corresponding preset constant can be 0.1, and the change amplitude is KP*m*△T. 温差 KP is the first proportionality coefficient, m is a preset constant, and ΔT 温差 If it's the temperature difference, then the amplitude of the change obtained after substituting the preset constant and the temperature difference into the correction function is KP*0.1*△T. 温差 The second proportionality coefficient is KP - KP * 0.1 * ΔT 温差 .

[0105] In the fourth scenario, when the deviation range is the second deviation range [-0.5, 0) and the temperature range is the fourth temperature range (-∞, -0.5), the first proportional coefficient is not corrected.

[0106] In the aforementioned scenarios, the preset constant is directly proportional to the amplitude of change. The larger the preset constant, the larger the amplitude of change, and the greater the downward correction value of the compressor's actual operating frequency. This results in the compressor's actual operating frequency decreasing to be closer to the desired operating frequency. The correction function is KP*m*△T. 温差 This is just an example; it can also be designed as other functions. This disclosure does not restrict this. The preset constant is also an example, but the preset constant needs to follow the following design principles in order to further improve the energy-saving level of the air conditioner without affecting the user experience.

[0107] Principle 1: When the deviation is within the second deviation interval, the predicted temperature is corrected upward to the target predicted temperature; wherein the difference between the target predicted temperature and the predicted temperature is greater than or equal to the absolute value of the minimum value of the second deviation interval.

[0108] Please see Figure 2 As shown, when the deviation is within the second deviation interval, the predicted temperature is lower than the actual temperature. This disclosure aims to correct the actual temperature to be closer to the predicted temperature when the predicted temperature is higher than the actual temperature. When the predicted temperature is lower than the actual temperature, if the actual temperature is corrected upwards, the deviation between the predicted and actual temperatures will gradually increase, causing the actual temperature to gradually deviate from the user's desired predicted temperature. Therefore, this disclosure adds a target value to the actual temperature to obtain a target predicted temperature, ensuring that the target predicted temperature is higher than the actual temperature. This allows the disclosure to continue correcting the actual temperature upwards to approach the target predicted temperature. Furthermore, when the deviation is within the second deviation interval, the maximum difference between the predicted temperature and the set temperature is 0.5, meaning the absolute value of the minimum value in the second deviation interval is 0.5. Therefore, 0.5 can be used as the target value, and then 0.5 can be added to the actual temperature to obtain the target predicted temperature.

[0109] For example, T can be 预测温度 Revised to T 目标预测温度 T 目标预测温度 =T 真实温度 +0.5.

[0110] In this process, on the one hand, after the predicted temperature is corrected upward to the target predicted temperature, the actual temperature can continue to be corrected towards the target predicted temperature, thereby reducing the actual operating frequency of the compressor and improving the energy-saving level of the air conditioner; on the other hand, the target value of 0.5 between the target predicted temperature and the predicted temperature is small, so even if the predicted temperature is corrected upward to 0.5, the user will not perceive a temperature change, so it will not affect the user experience.

[0111] Principle 2: The preset constant corresponding to the first deviation interval is less than the preset constant corresponding to the second deviation interval.

[0112] Please see Figure 6 and Figure 7 As shown, when the temperature difference ranges are the same, the preset constant corresponding to the first deviation range is smaller than the preset constant corresponding to the second deviation range. For example, taking the temperature difference range (2, +∞) as the first temperature difference range, the preset constant 0.1 corresponding to the first deviation range is smaller than the preset constant 0.12 corresponding to the second deviation range.

[0113] In Principle 1, after correcting the predicted temperature to the target predicted temperature, since the target value is 0.5, the deviation between the target predicted temperature and the actual temperature is at least 0.5. Therefore, the second deviation interval changes from [0, 0.5) to [0.5, +∞). The minimum deviation of the second deviation interval changes from 0 to 0.5, that is, the minimum deviation between the target predicted temperature and the actual temperature in the second deviation interval is 0.5; while the maximum deviation between the predicted temperature and the actual temperature in the first deviation interval [0, 0.5) is 0.5.

[0114] It is evident that the deviation between the predicted temperature and the actual temperature within the first deviation interval is smaller than the deviation between the target predicted temperature and the actual temperature within the second deviation interval. In this case, to bring the actual temperature closer to the predicted temperature or the target predicted temperature, a larger change amplitude is needed to control the actual temperature towards the target predicted temperature, and a smaller change amplitude is needed. Therefore, the preset constant corresponding to the first deviation interval can be configured to be smaller than the preset constant for the second deviation interval. This results in the change amplitude corresponding to the first deviation interval being smaller than that corresponding to the second deviation interval, allowing the proportional coefficient corresponding to the second deviation interval to decrease more significantly than that corresponding to the first deviation interval. Consequently, the actual operating frequency of the compressor can decrease more, bringing it closer to the target predicted temperature. Similarly, when the deviation is within the first deviation interval, the corresponding decrease in the proportional coefficient is relatively smaller, resulting in a relatively smaller decrease in the actual operating frequency of the compressor, bringing it closer to the predicted temperature.

[0115] Principle 3: For the same deviation and within the same temperature difference range, the preset constant corresponding to the heating stage is less than the preset constant corresponding to the cooling stage.

[0116] For example, please see Figure 6 As shown, taking the deviation interval where the deviation is located as the first deviation interval [0, 0.5) and the temperature difference interval where the temperature difference is located as the first temperature difference interval (2, +∞) as an example, the preset constant corresponding to the first deviation interval and the first temperature difference interval in the heating stage is 0.1, and the preset constant corresponding to the first deviation interval and the first temperature difference interval in the cooling stage is 0.12.

[0117] Please see Figure 2 As shown, when the actual indoor temperature is close to the set temperature, the air conditioner compressor will work at a lower actual operating frequency. At this time, the actual indoor temperature will fluctuate around the set temperature, and during the fluctuation, there will be a heating phase and a cooling phase.

[0118] During the heating phase, the compressor is actually operating at a lower actual operating frequency. Since the actual operating frequency of the compressor is reduced, the preset constant for the heating phase can be set smaller than that for the cooling phase. In this case, the change amplitude of the actual operating frequency during the heating phase is smaller than that during the cooling phase. The downward correction value of the compressor's actual operating frequency during the heating phase will also be smaller, thus avoiding the compressor stopping and causing the indoor temperature to continue to rise.

[0119] During the cooling phase, the compressor operates at a relatively high actual operating frequency. Since the compressor's actual operating frequency is relatively high, the preset constant for the cooling phase can be set larger than that for the heating phase. As a result, the amplitude of the temperature change during the cooling phase will be larger than that during the heating phase, and the downward correction value of the compressor's actual operating frequency during the cooling phase will also be larger. This will further reduce the compressor's actual operating frequency and improve the energy efficiency of the air conditioner.

[0120] Principle 4: Within the same deviation range, the smaller the temperature difference, the smaller the corresponding preset constant.

[0121] For example, for the three temperature difference intervals during the heating phase, the preset constants corresponding to the first temperature difference interval, the second temperature difference interval, and the third temperature difference interval decrease sequentially. For instance, within the first deviation interval [0, 0.5), the preset constants corresponding to the first temperature difference interval, the second temperature difference interval, and the third temperature difference interval are 0.1, 0.08, and 0, respectively.

[0122] For example, for the four temperature difference intervals during the cooling phase, the preset constants corresponding to the first temperature difference interval, the second temperature difference interval, the third temperature difference interval, and the fourth temperature difference interval decrease sequentially. For instance, within the first deviation interval [0, 0.5), the preset constants corresponding to the first temperature difference interval, the second temperature difference interval, the third temperature difference interval, and the fourth temperature difference interval are 0.12, 0.1, 0.08, and 0, respectively.

[0123] The reason for this design is that, within the same deviation range, as the temperature difference between the predicted temperature and the set temperature gradually decreases, it indicates that the temperature difference between the predicted temperature and the actual temperature is also gradually decreasing. This means the available space for frequency adjustment is also gradually decreasing. Therefore, as the temperature difference between the predicted temperature and the set temperature gradually decreases, the preset constant can be controlled to gradually decrease, thereby gradually reducing the amplitude of the change in the first proportional coefficient.

[0124] As the temperature difference between the predicted and actual temperatures gradually decreases, and both temperatures approach the set temperature, the compressor gradually reduces its operating frequency to maintain the actual temperature near the set temperature. Consequently, the corresponding first proportional coefficient also gradually decreases to reduce the compressor's operating speed and the air conditioner's cooling speed. Based on this, this disclosure allows control over the magnitude of the first proportional coefficient's change to gradually decrease as the temperature difference decreases, preventing the first proportional coefficient from dropping to zero and thus avoiding a complete compressor shutdown.

[0125] For example, if the temperature difference between the predicted temperature and the actual temperature is 4°C at the first moment, the first proportionality coefficient is 0.12. If the temperature difference between the predicted temperature and the actual temperature is 1°C at the second moment, the first proportionality coefficient is 0.05. In this scenario, even if a large change is subtracted from the first proportionality coefficient at the first moment, the compressor will not stop working. Similarly, if a large change is subtracted from the first proportionality coefficient at the second moment, the compressor's first proportionality coefficient will approach 0, causing the compressor to stop working. Therefore, a smaller change needs to be subtracted from the first proportionality coefficient at the second moment to avoid the compressor stopping.

[0126] Step A2: Adjust the first proportional coefficient to the second proportional coefficient based on the change amplitude.

[0127] The formula for calculating the amplitude of change is KP*m*△T 温差 The formula for calculating the second proportionality coefficient is KP - KP*m*△T 温 Difference.

[0128] As can be seen, after obtaining the change amplitude, the second proportional coefficient can be obtained by subtracting the change amplitude from the first proportional coefficient.

[0129] It is understandable that the above are the solutions implemented when the deviation is within the first deviation range or the second deviation range. When the deviation is outside the first deviation range and the second deviation range, the predicted temperature obtained by the temperature prediction model is considered unreliable. If the first proportional coefficient is still corrected according to the predicted temperature obtained by the temperature prediction model, it will obviously lead to a significant decrease in the cooling speed of the air conditioner, resulting in a reduced user experience.

[0130] Therefore, when the deviation is outside the first deviation range and the second deviation range, the first proportional coefficient does not need to be corrected, and the air conditioner compressor can maintain the original first proportional coefficient operation.

[0131] Through the above technical solution, firstly, the preset constant corresponding to the first deviation interval with smaller deviation can be smaller than the preset constant corresponding to the second deviation interval with larger deviation after correction. This allows the downward correction amplitude of the first proportional coefficient to be larger in the first deviation interval and the downward correction amplitude of the first proportional coefficient to be smaller in the second deviation interval. Consequently, the actual temperature can be corrected upward to be close to the predicted temperature in different deviation stages.

[0132] Secondly, the preset constant corresponding to the heating stage can be set to be smaller than the preset constant for the cooling stage. This allows for a smaller change amplitude to control the first proportional coefficient to decrease during the heating stage when the compressor operates at low frequency, and a larger change amplitude to control the first proportional coefficient to decrease during the cooling stage when the compressor operates at relatively high frequency. This improves the energy efficiency of the air conditioner in different heating and cooling stages.

[0133] Thirdly, the smaller the temperature difference, the smaller the preset constant can be, thereby reducing the magnitude of the change in the first proportional coefficient and preventing the compressor from stopping.

[0134] The following describes a specific embodiment of step S11 above, which illustrates how the temperature prediction model predicts the predicted temperature.

[0135] First, we will introduce the training process of the temperature prediction model. This training process includes: using historical temperatures within a preset time before the air conditioner is turned on as training samples, and using the actual temperatures detected by the temperature sensor after the air conditioner is turned on as labels to train the temperature prediction model; if the error between the predicted temperature and the actual temperature does not meet the convergence condition, we will update the network parameters of the temperature prediction model until the error between the predicted temperature and the actual temperature output by the temperature prediction model meets the convergence condition.

[0136] The temperature prediction model is the Autoregressive Moving Average (ARIMA) model, which can predict the actual temperature over a future period based on historical temperatures.

[0137] The modeling process for temperature prediction models includes:

[0138] (1) Air conditioning data preprocessing, which includes missing value filling and data normalization.

[0139] For missing value imputation, we can first obtain the historical temperature for a preset period of time before the air conditioner is turned on. There may be missing data in the historical temperature of this time series, such as the missing historical temperature at a certain time point. Therefore, we can average the obtained historical temperatures to obtain the average value and replace the missing historical temperature with the average value.

[0140] For data normalization, since the dimensions and units of each data point are different, the historical temperatures of the time series can be normalized before model training. The historical temperatures can be normalized to the range of [0, 1], which can ensure that the subsequent temperature prediction model can have a faster convergence speed.

[0141] (2) Data partitioning.

[0142] A portion of the acquired historical temperatures can be used as training samples to train the temperature prediction model; another portion of the historical temperatures can be used as test samples to test the prediction performance of the temperature prediction model.

[0143] For example, 70% of the historical temperatures in the time series can be used as training samples, and 30% of the historical temperatures in the time series can be used as test samples.

[0144] (3) Stationarity verification: Since the temperature prediction model is an ARIMA model, unit root verification can be used. The purpose of unit root verification is to determine whether the historical temperatures of the time series need to be differencing to achieve stationarity. Unit root verification methods include the following two methods.

[0145] Method A, Augmented Dickey-Fuller (ADF) test, determines the stationarity of a time series by calculating the presence or absence of a unit root. If the test results show the presence of a unit root, it indicates that the historical temperature of the time series is non-stationary.

[0146] Method B, the Phillips-Perron (PP) test, is similar to the ADF test. It determines the existence of a unit root by regressing the residual sequence.

[0147] These two unit root tests can help determine the parameters in a temperature prediction model and whether the historical temperatures of the time series need to be differencing. If the unit root test results indicate that the historical temperatures of the time series are non-stationary, they are typically differencing to eliminate non-stationarity before being applied to the temperature prediction model for modeling and prediction.

[0148] (4) Stationarity processing. If the historical temperature of the time series is not stationary, it is necessary to perform stationarity processing on the historical temperature. Usually, the difference method is used for stationarity processing.

[0149] The difference method is suitable for time series data with obvious trends, such as historical temperatures in a time series. Non-stationarity can be eliminated by performing first-order or higher-order differences on the historical temperatures of the time series.

[0150] First-order difference refers to calculating the difference between adjacent historical temperatures, that is, the difference between the next historical temperature and the previous historical temperature. If the difference sequence after first-order difference no longer shows an obvious trend, the historical temperature of the time series can be considered to be a stationary sequence.

[0151] For higher-order differencing, a similar operation can be performed, repeatedly differencing the historical temperatures of the differencing time series until a stationary series is obtained.

[0152] (5) Determining the order of the temperature prediction model.

[0153] The model order can be determined based on the autocorrelation function (ACF) and partial autocorrelation coefficient (PACF). In this scheme, the order of the temperature prediction model can be determined based on the Bayesian Information Criterion (BIC). The order that minimizes the BIC is the optimal order of the temperature prediction model. The expression for the BIC criterion is as follows:

[0154]

[0155] In the above formula, It is the maximum likelihood estimation function of the residual variance, M is the total number of unknown parameters in the model, and N is the number of sample sequences.

[0156] (6) Parameter estimation: The initial network parameters of the temperature prediction model can be estimated by using the maximum likelihood estimation method. These network parameters include weight coefficients, etc.

[0157] (7) Adaptability verification of temperature prediction model.

[0158] The model's fitness test mainly examines whether the temperature prediction model has extracted useful information from the historical temperature data of the time series, that is, whether the residual sequence is a white noise process.

[0159] In the modeling process of this scheme, the Ljung-Box test method is used to test the residual autocorrelation coefficients in the form of groups. If the p-value of the Ljung-Box test statistic is greater than the significance level of 0.05, the null hypothesis that the residual sequence is white noise is accepted.

[0160] (8) The expression for the temperature prediction model is as follows:

[0161]

[0162] In the above formula, L(X) t ) = X t -X t-1 L d (X t )=L(L d-1 (X t )).

[0163] Among them, X t This is the predicted temperature from the temperature prediction model; μ is the mean constant of the historical temperatures in the time series, and ε...t It's an error based on historical temperatures. It is the autoregressive coefficient, θ j It is the moving average coefficient, L d It is a d-order difference operator.

[0164] After designing the temperature prediction model, training samples, and test samples, rolling temperature predictions can be performed. This allows the temperature prediction model to learn the correlation between multiple historical temperatures within a preset time period before the air conditioner starts and the predicted temperature. First, multiple historical temperatures within the preset time period before the air conditioner starts are input into the temperature prediction model to predict the temperature one second later. Then, this predicted temperature is added to the training samples, and the first historical temperature in the training samples is removed. This means that for every new predicted temperature added to the training samples, the first historical temperature is removed from the training samples, thus keeping the amount of data in the training samples constant, avoiding overfitting, and improving the generalization ability of the temperature prediction model.

[0165] For example, in the first prediction, the training samples are Tem1 = {X1, X2, ..., X...} k Using these training samples, the temperature value for the next second can be predicted. Bundle Add it to the training sample, then remove the first historical temperature from the training sample; for the second prediction, the training sample is... Following this pattern, the predicted temperature for the next hour is: Where n is the total predicted time, and in this scheme, n can be 3600s.

[0166] (9) Evaluation of temperature prediction models.

[0167] After the temperature prediction model has been trained, the mean squared error (MSE) method can be used to evaluate the performance and accuracy of the temperature prediction model. The expression for the mean squared error is as follows:

[0168]

[0169] Where MSE is the mean squared error. It is the actual temperature; is the predicted temperature; n is the number of predicted temperatures.

[0170] As can be seen from the above formula, the smaller the mean square error, the closer the predicted temperature obtained by the temperature prediction model is to the actual temperature, the better the accuracy and precision of the temperature prediction model, and the better the performance of the temperature prediction model.

[0171] Secondly, the application process of the temperature prediction model will be introduced. This application process includes: taking multiple real temperatures within a preset time before the air conditioner is turned on and / or the predicted temperature obtained by the temperature prediction model in the previous prediction as input parameters of the temperature prediction model to obtain the predicted temperature for this prediction.

[0172] For example, the first input parameter among the multiple input parameters is removed, and the predicted temperature obtained by the temperature prediction model in the previous prediction is added to the end of the multiple input parameters as the input parameter for the current prediction of the temperature prediction model, so as to obtain the current predicted temperature.

[0173] Taking a preset duration of 1 hour as an example, the actual temperatures before the air conditioner is turned on are A1, A2, A3, A4, A5, and A6. After inputting these six actual temperatures as input parameters into the temperature prediction model, the temperature prediction model can predict the predicted temperature B1 in the first second after the air conditioner is turned on. Then, A1 is removed and B1 is added to obtain the input parameters A2, A3, A4, A5, A6, and B1. After inputting these six input parameters into the temperature prediction model, the temperature prediction model can predict the predicted temperature B2 in the second second after the air conditioner is turned on, and so on, until the temperature prediction model predicts the predicted temperature within 1 hour after the air conditioner is turned on.

[0174] Since different room spaces will result in different actual temperatures after air conditioning is turned on, the historical temperature within a preset time before the user turns on the air conditioner and the actual temperature after the air conditioner is turned on can be used as training samples to train the temperature prediction model, so that the temperature prediction model can learn the temperature change trend in different rooms before and after the air conditioner is turned on.

[0175] When the temperature prediction model receives the actual temperature within the preset time before the air conditioner is turned on, it can predict the temperature for a future period that is more consistent with the current room size. This predicted temperature will change as the room size changes.

[0176] Understandably, after air conditioners are purchased and installed in homes of varying sizes, and different users have different usage habits, the temperature prediction model built into the air conditioner learns the correlation between the historical temperature before the air conditioner is turned on and the actual temperature after it is turned on. This correlation will also vary depending on the user's usage habits and the size of the indoor space, resulting in different temperature prediction models being trained. In practical applications, the temperature prediction model will use the learned correlation to predict a temperature that is more consistent with the current room size and the user's usage habits.

[0177] Through the above technical solution, the temperature prediction model will learn the temperature change trend before and after the air conditioner is turned on in different spaces, thereby predicting personalized predicted temperatures for different houses. This makes the predicted temperatures more in line with user habits and living environment, thus providing support for adjusting the compressor's operating frequency.

[0178] Figure 8 This is a frequency adjustment device 800 according to an exemplary embodiment, which includes a prediction module 810, a temperature difference module 820 and a correction module 830.

[0179] The prediction module 810 is configured to predict the temperature of the indoor environment and obtain a predicted temperature, which is the desired temperature of the indoor environment.

[0180] The temperature difference module 820 is configured to determine the temperature difference between the predicted temperature and the set temperature, wherein the set temperature is the target temperature that the air conditioner needs to reach after adjusting the indoor temperature.

[0181] The correction module 830 is configured to correct the first proportional coefficient of the PID model downward to the second proportional coefficient based on the temperature difference; wherein, the larger the temperature difference, the greater the change amplitude of the first proportional coefficient to the second proportional coefficient; the PID model is used to adjust the operating frequency of the air conditioner compressor based on the set temperature and the actual indoor temperature, so that the actual temperature output by the air conditioner is close to the set temperature.

[0182] Optionally, the correction module 830 includes:

[0183] The first calculation submodule is configured to obtain the change amplitude based on the temperature difference and a preset constant;

[0184] The first correction submodule is configured to adjust the first proportional coefficient to the second proportional coefficient by the change amplitude.

[0185] Optionally, the first computation submodule includes:

[0186] The interval determination submodule is configured to determine the deviation interval between the predicted temperature and the actual temperature, and the temperature difference interval.

[0187] The preset constant determination submodule is configured to determine preset constants corresponding to the deviation interval and the temperature difference interval;

[0188] The second calculation submodule is configured to obtain the change amplitude based on the temperature difference and the preset constant.

[0189] Optionally, the deviation interval includes a first deviation interval and a second deviation interval, wherein the minimum value of the first deviation interval is greater than the maximum value of the second deviation interval;

[0190] Wherein, the preset constant corresponding to the first deviation interval is less than the preset constant corresponding to the second deviation interval.

[0191] Optionally, the frequency adjustment device 800 includes:

[0192] The predicted temperature correction module is configured to correct the predicted temperature upward to a target predicted temperature when the deviation is within the second deviation interval; wherein the difference between the target predicted temperature and the predicted temperature is greater than or equal to the absolute value of the minimum value of the second deviation interval.

[0193] Optionally, within the same deviation range and the same temperature difference range, the preset constant corresponding to the heating stage is smaller than the preset constant corresponding to the cooling stage.

[0194] Optionally, the temperature difference range corresponding to the heating stage includes a first temperature difference range, a second temperature difference range, and a third temperature difference range, wherein the minimum value of the first temperature difference range is greater than the maximum value of the second temperature difference range, and the minimum value of the second temperature difference range is greater than the maximum value of the third temperature difference range.

[0195] Within the same deviation interval, the preset constants corresponding to the first temperature difference interval, the second temperature difference interval, and the third temperature difference interval decrease sequentially.

[0196] Optionally, the temperature difference range corresponding to the cooling stage includes a first temperature difference range, a second temperature difference range, a third temperature difference range, and a fourth temperature difference range. The minimum value of the first temperature difference range is greater than the maximum value of the second temperature difference range, the minimum value of the second temperature difference range is greater than the maximum value of the third temperature difference range, and the minimum value of the third temperature difference range is greater than the maximum value of the fourth temperature difference range.

[0197] Within the same deviation interval, the preset constants corresponding to the first temperature difference interval, the second temperature difference interval, the third temperature difference interval, and the fourth temperature difference interval decrease sequentially.

[0198] Optionally, the frequency adjustment device 800 includes:

[0199] The adjustment module is configured not to adjust the first proportional coefficient when the deviation is outside the range between the first deviation interval and the second deviation interval.

[0200] Optionally, the prediction module 810 includes:

[0201] The prediction submodule is configured to use multiple indoor temperatures and / or the predicted temperatures obtained from the previous prediction by the temperature prediction model within a preset time period before the air conditioner is turned on as input parameters of the temperature prediction model to obtain the predicted temperature for this prediction.

[0202] Optionally, the prediction submodule includes:

[0203] The filtering submodule is configured to remove the first input parameter from the plurality of input parameters, and to add the predicted temperature obtained by the temperature prediction model in the previous prediction to the end of the plurality of input parameters, as the input parameter for the temperature prediction model in this prediction, to obtain the predicted temperature for this prediction.

[0204] 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.

[0205] This disclosure also provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the steps of the frequency adjustment method provided in this disclosure.

[0206] Figure 9 This is a block diagram illustrating a device 900 for frequency regulation according to an exemplary embodiment. For example, device 900 may be an air conditioner, etc.

[0207] Reference Figure 9 The device 900 may include one or more of the following components: a processing component 902, a memory 904, a power supply component 906, a multimedia component 908, an audio component 910, an input / output interface 912, a sensor component 914, and a communication component 916.

[0208] Processing component 902 typically controls the overall operation of device 900, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 902 may include one or more processors 920 to execute instructions to complete all or part of the steps of the frequency adjustment method described above. Furthermore, processing component 902 may include one or more modules to facilitate interaction between processing component 902 and other components. For example, processing component 902 may include a multimedia module to facilitate interaction between multimedia component 908 and processing component 902.

[0209] Memory 904 is configured to store various types of data to support the operation of device 900. Examples of this data include instructions for any application or method operating on device 900, contact data, phonebook data, messages, pictures, videos, etc. Memory 904 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0210] Power supply component 906 provides power to various components of device 900. Power supply component 906 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to device 900.

[0211] Multimedia component 908 includes a screen that provides an output interface between the device 900 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 908 includes a front-facing camera and / or a rear-facing camera. When the device 900 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0212] Audio component 910 is configured to output and / or input audio signals. For example, audio component 910 includes a microphone (MIC) configured to receive external audio signals when device 900 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 904 or transmitted via communication component 916. In some embodiments, audio component 910 also includes a speaker for outputting audio signals.

[0213] Input / output interface 912 provides an interface between processing component 902 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, start buttons, and lock buttons.

[0214] Sensor assembly 914 includes one or more sensors for providing status assessments of various aspects of device 900. For example, sensor assembly 914 may detect the on / off state of device 900, the relative positioning of components such as the display and keypad of device 900, changes in position of device 900 or a component of device 900, the presence or absence of user contact with device 900, orientation or acceleration / deceleration of device 900, and temperature changes of device 900. Sensor assembly 914 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 914 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 914 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0215] Communication component 916 is configured to facilitate wired or wireless communication between device 900 and other devices. Device 900 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 916 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 916 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0216] In an exemplary embodiment, the device 900 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the frequency adjustment method described above.

[0217] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 904 including instructions, which can be executed by a processor 920 of the device 900 to complete the frequency adjustment method described above. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0218] The aforementioned device can be a standalone electronic device or a part of a standalone electronic device. For example, in one embodiment, the device can be an integrated circuit (IC) or a chip, wherein the integrated circuit can be a single IC or a collection of multiple ICs. The chip can include, but is not limited to, the following types: GPU (Graphics Processing Unit), CPU (Central Processing Unit), FPGA (Field Programmable Gate Array), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), and SoC (System on Chip). The aforementioned integrated circuit or chip can be used to execute executable instructions (or code) to implement the aforementioned frequency adjustment method. The executable instructions can be stored in the integrated circuit or chip or obtained from other devices or equipment. For example, the integrated circuit or chip includes a processor, memory, and an interface for communicating with other devices. The executable instruction can be stored in the memory, and when the executable instruction is executed by the processor, it implements the frequency adjustment method described above; or, the integrated circuit or chip can receive the executable instruction through the interface and transmit it to the processor for execution to implement the frequency adjustment method described above.

[0219] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the frequency adjustment method described above when executed by the programmable device.

[0220] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of this disclosure. 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.

[0221] 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 frequency adjustment method, characterized by, include: The indoor temperature is predicted to obtain a predicted temperature, which is the desired indoor temperature. Determine the temperature difference between the predicted temperature and the set temperature, where the set temperature is the target temperature that the air conditioner needs to reach after adjusting the indoor temperature. Based on the temperature difference, the first proportional coefficient of the PID model is adjusted downward to the second proportional coefficient; wherein, the larger the temperature difference, the greater the change amplitude of the first proportional coefficient to the second proportional coefficient; the PID model is used to adjust the operating frequency of the air conditioner compressor according to the set temperature and the actual indoor temperature, so that the actual temperature output by the air conditioner is close to the set temperature.

2. The method of claim 1, wherein, The step of adjusting the proportional coefficient of the PID model from the first proportional coefficient to the second proportional coefficient based on the temperature difference includes: The amplitude of the change is obtained based on the temperature difference and the preset constant; The first proportional coefficient is adjusted to the second proportional coefficient based on the change amplitude.

3. The method of claim 2, wherein, The step of obtaining the change amplitude based on the temperature difference and a preset constant includes: Determine the deviation range between the predicted temperature and the actual temperature, and the temperature difference range. Determine the preset constants corresponding to the deviation range and the temperature difference range; The change amplitude is obtained based on the temperature difference and the preset constant.

4. The method of claim 3, wherein, The deviation range includes a first deviation range and a second deviation range, wherein the minimum value of the first deviation range is greater than the maximum value of the second deviation range; Wherein, the preset constant corresponding to the first deviation interval is less than the preset constant corresponding to the second deviation interval.

5. The method of claim 4, wherein, The method further includes: If the deviation is within the second deviation interval, the predicted temperature is corrected upward to the target predicted temperature; wherein the difference between the target predicted temperature and the predicted temperature is greater than or equal to the absolute value of the minimum value of the second deviation interval.

6. The method of claim 3, wherein, Within the same deviation range and the same temperature difference range, the preset constant corresponding to the heating stage is less than the preset constant corresponding to the cooling stage.

7. The method of claim 3, wherein, The temperature difference range corresponding to the heating stage includes a first temperature difference range, a second temperature difference range, and a third temperature difference range. The minimum value of the first temperature difference range is greater than the maximum value of the second temperature difference range, and the minimum value of the second temperature difference range is greater than the maximum value of the third temperature difference range. Within the same deviation interval, the preset constants corresponding to the first temperature difference interval, the second temperature difference interval, and the third temperature difference interval decrease sequentially.

8. The method of claim 3, wherein, The temperature difference range corresponding to the cooling stage includes a first temperature difference range, a second temperature difference range, a third temperature difference range, and a fourth temperature difference range. The minimum value of the first temperature difference range is greater than the maximum value of the second temperature difference range, the minimum value of the second temperature difference range is greater than the maximum value of the third temperature difference range, and the minimum value of the third temperature difference range is greater than the maximum value of the fourth temperature difference range. Within the same deviation interval, the preset constants corresponding to the first temperature difference interval, the second temperature difference interval, the third temperature difference interval, and the fourth temperature difference interval decrease sequentially.

9. The method of claim 4, wherein, The method further includes: If the deviation is outside the range between the first deviation interval and the second deviation interval, the first proportional coefficient is not corrected.

10. The method according to claim 1, characterized in that, The process of predicting the indoor temperature to obtain the predicted temperature includes: The predicted temperature for this prediction is obtained by taking multiple indoor temperatures and / or the predicted temperatures obtained from the previous prediction by the temperature prediction model within a preset time period before the air conditioner is turned on.

11. The method of claim 10, wherein, The step of using multiple indoor temperatures within a preset time period before the air conditioner is turned on, along with the previously predicted temperature obtained from the temperature prediction model, as input parameters for the temperature prediction model to obtain the current predicted temperature includes: Remove the first input parameter from the plurality of input parameters, and add the predicted temperature obtained by the temperature prediction model in the previous prediction to the end of the plurality of input parameters, and use it as the input parameter for the temperature prediction model in this prediction, to obtain the predicted temperature for this prediction.

12. A frequency adjustment device, characterized by include: The prediction module is configured to predict the temperature of the indoor environment and obtain a predicted temperature, which is the desired temperature of the indoor environment. The temperature difference module is configured to determine the temperature difference between the predicted temperature and the set temperature, wherein the set temperature is the target temperature that the air conditioner needs to reach after adjusting the indoor temperature. The correction module is configured to adjust the first proportional coefficient of the PID model downward to a second proportional coefficient based on the temperature difference; wherein, the larger the temperature difference, the greater the change amplitude of the first proportional coefficient to the second proportional coefficient; the PID model is used to adjust the operating frequency of the air conditioner compressor based on the set temperature and the actual indoor temperature, so that the actual temperature output by the air conditioner is close to the set temperature.

13. An air conditioner characterized by comprising: include: processor; Memory used to store processor-executable instructions; The processor is configured as follows: Perform the steps of the method according to any one of claims 1 to 11.

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

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