Compressor frequency modulation method and device, medium and air conditioner

Abstract

The invention discloses a compressor frequency modulation method and device, a medium and an air conditioner, and the method comprises the steps: obtaining the current and target indoor temperatures, precisely predicting the total adjustment time and the total load, obtaining the equivalent compressor frequency meeting the load demand, and providing a reference frame for frequency adjustment; constructing a target function taking a frequency sequence as a variable on the basis, calculating a target frequency sequence through iterative optimization in combination with a limiting condition set on the basis of equivalent frequency, fully considering system inertia in the process, and estimating the target frequency sequence before the temperature is close to a target value; and then fast dynamic frequency modulation is performed according to at least one compressor frequency in the target frequency sequence. Therefore, according to the scheme, through advanced planning and dynamic adjustment, the temperature deviation can be quickly responded, the initial adjustment lag is reduced, and then the temperature overshoot phenomenon is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of air-conditioning compressors, and in particular to a compressor frequency modulation method, device, medium and air conditioner. Background Art

[0002] Currently, air conditioner compressor frequency regulation is typically based on the difference between the real-time return air temperature and the target indoor temperature. However, this regulation method is affected by system inertia, and the adjustment action often lags behind temperature changes. This lag can cause the system to continue to operate due to the accumulated cooling (or heating) capacity in the system when the temperature approaches the set point, even if the controller has adjusted the compressor frequency. This can eventually cause the temperature to exceed the set point, resulting in overshoot. Summary of the Invention

[0003] Based on this, it is necessary to provide a compressor frequency modulation method, device, medium and air conditioner to solve the problem that the traditional method of adjusting the compressor frequency is prone to overshoot.

[0004] In a first aspect, an embodiment of the present application provides a compressor frequency modulation method, the method comprising:

[0005] Obtaining a current indoor temperature and a target indoor temperature, and predicting a total adjustment time and a total load from the current indoor temperature to the target indoor temperature;

[0006] Determine an equivalent compressor frequency according to the total load; wherein the equivalent compressor frequency is the equivalent total frequency required for the compressor to meet the total load within the total adjustment time;

[0007] Obtaining an objective function constructed based on a frequency sequence, and, on the premise that the frequency sequence satisfies a frequency restriction condition, iteratively optimizing the objective function until the optimization condition is satisfied, so as to calculate a current target frequency sequence; wherein the frequency restriction condition is constructed based on the equivalent compressor frequency;

[0008] The frequency of the compressor is adjusted in a corresponding adjustment period based on at least one compressor frequency in the target frequency sequence.

[0009] In some embodiments of the present application, the method of predicting the total load includes:

[0010] Determine a first load forecasting model within the current regulation period; wherein the first load forecasting model is expressed as:

[0011]

[0012] In the above formula, Φ z is the total load; is the total adjustment time t zThe average load within the period; A, B, C are the definition parameters within the current regulation period; T out is the current outdoor temperature; is the total adjustment time t z The average temperature inside; T in is the current indoor temperature; T set is the target indoor temperature;

[0013] The current outdoor temperature, the current indoor temperature, the target indoor temperature, and the total adjustment time are substituted into the first load prediction model to calculate the total load.

[0014] In some embodiments of the present application, determining the first load forecasting model within the current regulation period includes:

[0015] Obtain a second load forecasting model; wherein the second load forecasting model is expressed as:

[0016]

[0017] In the above formula, Φ is the instantaneous load; K is the comprehensive heat transfer coefficient of the building; F is the effective heat exchange area of ​​the building; M is the air quality of the space; C p is the space specific heat; S is the load heat source;

[0018] The second load forecasting model is converted into a third load forecasting model; wherein the third load forecasting model is expressed as:

[0019]

[0020] In the above formula, is the average load within the time interval Δt; parameter A is defined by the first constant parameter K·F equivalent; parameter B is defined by the second constant parameter M·C p Equivalently obtained; the definition parameter C is obtained by equivalently obtaining the third constant parameter S; is the average temperature within the time interval Δt; T start is the indoor temperature at the beginning of the time interval Δt; T end the indoor temperature at the end of the time interval Δt;

[0021] Acquire multiple sets of historical data associated with the current regulation cycle, and solve the defined parameters in the third load prediction model based on the historical data to obtain the first load prediction model within the current regulation cycle; wherein each set of historical data includes the outdoor temperature, average temperature, indoor temperature at the beginning, indoor temperature at the end, and average load within a historical regulation cycle.

[0022] In some embodiments of the present application, determining the equivalent compressor frequency according to the total load includes:

[0023] The historical compressor frequency and the historical load of the last adjustment cycle are obtained, and a proportional calculation is performed according to the historical compressor frequency, the historical load and the total load to obtain the equivalent compressor frequency.

[0024] In some embodiments of the present application, the frequency limitation condition includes: the sum of the compressor frequencies corresponding to the at least one adjustment cycle is equal to the equivalent compressor frequency, and the compressor frequencies corresponding to the at least one adjustment cycle are all within a preset frequency range, and the absolute value of the difference between the compressor frequencies corresponding to two adjacent adjustment cycles is less than a preset difference threshold.

[0025] In some embodiments of the present application, obtaining the current indoor temperature and the target indoor temperature includes:

[0026] Determine whether the current continuous operation time of the compressor reaches the continuous operation time limit;

[0027] On the premise that the continuous operation time reaches the continuous operation time limit, the current indoor temperature and the target indoor temperature are obtained.

[0028] In some embodiments of the present application, the method of predicting the total adjustment time includes:

[0029] Calculating the difference between the current indoor temperature and the target indoor temperature as the real-time target temperature difference;

[0030] Determine the input fuzzy set corresponding to the real-time target temperature difference through a preset membership function;

[0031] Performing fuzzy reasoning on the input fuzzy set according to a predefined fuzzy rule table to obtain an output fuzzy set for expressing the total adjustment time;

[0032] The output fuzzy set is converted into the corresponding total adjustment time through defuzzification processing.

[0033] In a second aspect, an embodiment of the present application further provides a compressor frequency modulation device, the compressor frequency modulation device comprising:

[0034] a parameter acquisition module, configured to acquire a current indoor temperature and a target indoor temperature, and predict a total adjustment time and a total load from the current indoor temperature to the target indoor temperature;

[0035] An equivalent compressor frequency calculation module is configured to determine an equivalent compressor frequency according to the total load; wherein the equivalent compressor frequency is an equivalent total frequency required for the compressor to meet the total load within the total adjustment time;

[0036] A frequency modulation module is used to obtain an objective function constructed based on a frequency sequence, and on the premise that the frequency sequence satisfies a frequency restriction condition, iteratively optimize the objective function until the optimization condition is satisfied, so as to calculate the current target frequency sequence; wherein the frequency restriction condition is constructed based on the equivalent compressor frequency; and based on at least one compressor frequency in the target frequency sequence, the compressor is frequency modulated in a corresponding adjustment period.

[0037] In a third aspect, an embodiment of the present application further provides an air conditioner, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps in the above-mentioned compressor frequency modulation method are implemented.

[0038] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned compressor frequency modulation method are implemented.

[0039] In a fifth aspect, embodiments of the present application further provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described in the embodiments of the present application.

[0040] The present invention provides a compressor frequency modulation method, device, medium, and air conditioner. By obtaining the current and target indoor temperatures, the total modulation time and total load are accurately predicted, and the equivalent compressor frequency that meets the load demand is derived, providing a benchmark framework for frequency modulation. Based on this, an objective function with a frequency sequence as a variable is constructed, and combined with constraints set based on the equivalent frequency, the target frequency sequence is calculated through iterative optimization. This process fully considers system inertia, estimates the target frequency sequence before the temperature approaches the target value, and then performs rapid dynamic frequency modulation based on at least one compressor frequency in the target frequency sequence. As can be seen, through advance planning and dynamic adjustment, this solution can quickly respond to temperature deviations, reduce initial modulation lag, and thus reduce the occurrence of temperature overshoot. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] in:

[0043] Figure 1 A flow chart of a compressor frequency modulation method provided in the first embodiment of the present application;

[0044] Figure 2 A flow chart of a compressor frequency modulation method provided in the second embodiment of the present application;

[0045] Figure 3 It is a structural diagram of the compressor frequency modulation device;

[0046] Figure 4 This is the structural block diagram of the air conditioner. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0048] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0049] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0050] See also Figure 1 , Figure 1 This is a flow chart of the compressor frequency modulation method provided in the first embodiment of the present application. Although the flow chart shows a logical order, in some cases, the steps shown or described may be performed in a different order than that shown in the figures. Specifically, the specific process of the compressor frequency modulation method provided in the first embodiment of the present application is as follows:

[0051] S101, obtaining the current indoor temperature and the target indoor temperature, and predicting the total adjustment time and total load from the current indoor temperature to the target indoor temperature.

[0052] The current indoor temperature refers to the indoor air temperature measured in real time. The target indoor temperature refers to the desired indoor temperature. The total adjustment time refers to the total time required to reach the target indoor temperature from the current indoor temperature. In this embodiment, the total adjustment time includes at least one adjustment cycle. The total load refers to the total cooling capacity or heating capacity required by the air conditioning system to reach the target indoor temperature from the current indoor temperature during the total adjustment time.

[0053] Optionally, the current indoor temperature can be acquired in real time using a thermistor temperature sensor installed at the indoor unit's return air vent. Alternatively, the target indoor temperature can be acquired by directly reading the setpoint entered by the user on the air conditioner remote control. Other acquisition methods are also possible and are not specifically limited here.

[0054] Optionally, based on the current indoor temperature, target indoor temperature, and outdoor temperature (measured by the outdoor unit sensor), a pre-built temperature difference-time mapping table is queried to obtain the total adjustment time. This mapping table is generated through experimental tests and historical operating data, and records the typical time required to reach the target indoor temperature from the current indoor temperature under different outdoor temperatures and different temperature differences. Alternatively, the parameters based on the current indoor temperature, target indoor temperature, and outdoor temperature are input into a trained neural network model, and the total adjustment time is predicted by the neural network model. Accordingly, if the total adjustment time is t z , each adjustment period is Δt, then there are N adjustment periods in the total adjustment time, where

[0055] Alternatively, by collecting the operating parameters of the compressor (such as frequency, current, and voltage), combined with the ten-coefficient model, the cooling or heating capacity of the compressor per unit time is calculated and accumulated until the total load is obtained. Alternatively, by measuring the supply air temperature, return air temperature, and air volume, combined with the specific heat capacity and density of the air, the cooling or heating capacity carried by the air per unit time is calculated and accumulated until the total load is obtained. The number of accumulations here is the ratio of the total adjustment time to the unit time.

[0056] S102: Determine the equivalent compressor frequency according to the total load.

[0057] The equivalent compressor frequency is the equivalent total frequency required by the compressor to meet the total load within the total regulation time.

[0058] Optionally, a database is established in advance through experimental tests, which contains the frequency values ​​required for the compressor to operate within a fixed adjustment cycle under different load demands. The total load and total adjustment time are entered as input conditions into the database for query to obtain the closest frequency value. Furthermore, if there is no exact matching value in the database, the equivalent compressor frequency can be calculated by linear interpolation to ensure that the prediction result is applicable to the actual operating conditions. Alternatively, the total load, current indoor temperature, target indoor temperature, outdoor temperature and other parameters are used as inputs to a trained neural network model to directly output the equivalent compressor frequency. Of course, other prediction methods are also possible and are not specifically limited here.

[0059] S103, obtaining an objective function constructed based on the frequency sequence, and on the premise that the frequency sequence meets the frequency restriction condition, iteratively optimizing the objective function until the optimization condition is met, so as to calculate the current target frequency sequence.

[0060] The frequency sequence includes at least one compressor frequency corresponding to each adjustment cycle. The frequency constraint is a constraint set based on the equivalent compressor frequency to ensure that the frequency sequence meets the total load demand while also complying with the compressor's operating capacity and system stability requirements. The optimization condition is the criterion for stopping iteration when the objective function reaches a specific state. The target frequency sequence is the frequency sequence corresponding to the objective function meeting the optimization condition.

[0061] In some embodiments of the present application, the frequency restriction condition includes: the sum of the compressor frequencies corresponding to at least one adjustment cycle is equal to the equivalent compressor frequency, the compressor frequencies corresponding to at least one adjustment cycle are all within a preset frequency range, and the absolute value of the difference between the compressor frequencies corresponding to two adjacent adjustment cycles is less than a preset difference threshold. The above frequency restriction condition can be expressed as:

[0062]

[0063] In the above formula, f j is the compressor frequency corresponding to the jth regulation cycle, 1≤j≤N; Ω is the equivalent compressor frequency; f B is the lower limit of the frequency range; f T is the upper limit of the frequency interval; Δf is the difference threshold.

[0064] The above embodiment achieves precise matching of compressor output capacity with total load demand within the total regulation time. It also implements safety constraints on the compressor's operating state, preventing problems such as overheating and increased mechanical wear caused by excessively high frequencies, or inability to meet cooling / heating requirements due to excessively low frequencies, thereby improving system stability. It also achieves smooth transitions in compressor frequency, avoiding current surges and noise caused by sudden frequency changes, while also reducing wear on the compressor's mechanical components caused by frequent acceleration and deceleration.

[0065] Optionally, construct an energy consumption function P with frequency sequence as variable j =F(T ev ,T cd ,f j ) as the objective function, where T ev is the current suction side temperature parameter; T cd is the current exhaust side temperature parameter; F(.) is the function identifier. The particle swarm optimization algorithm is used for optimization: a set of random frequency sequences is initialized as the particle swarm, each particle represents a set of frequency sequences, and all must meet the above frequency restriction conditions. The position and velocity of the particles are iteratively updated, and the function value of the energy consumption function is calculated after each update until the rate of change of the function value of the energy consumption function is lower than the preset threshold or the maximum number of iterations is reached. It is determined that the optimization conditions are currently met and the target frequency sequence f1, f2...f is obtained. N This minimizes the energy consumption of the compressor, significantly reducing the operating cost of the air-conditioning system and is suitable for scenarios where energy conservation is a priority.

[0066] Alternatively, we can first construct a comfort function with frequency sequence as the variable as the objective function:

[0067]

[0068] In the above formula, C is the comfort score. The smaller the value, the closer the indoor temperature is to the target temperature and the higher the comfort. N is the total number of adjustment cycles. i is the weight of the i-th adjustment period, which is set according to the importance of the period. For example, the period close to the current moment has a higher weight. i is the estimated indoor temperature of the ith adjustment cycle; T target is the target indoor temperature; T i-1 is the estimated indoor temperature of the i-1th adjustment cycle; kh is the refrigeration coefficient, which reflects the relationship between the compressor frequency and the cooling capacity. The larger the value, the stronger the cooling effect per unit frequency. f is the compressor frequency of the i-th adjustment cycle; t i is the duration of the i-th adjustment cycle; m cThe room heat capacity represents the room's ability to store heat. The larger the value, the slower the temperature changes. The least recursive squares algorithm is then used for optimization: the frequency sequence is initialized with the equivalent compressor frequency as the starting point, the frequency value is recursively updated, and the model parameters are adjusted based on the measured temperature deviation to minimize the comfort function. When the temperature deviation converges to the preset threshold or reaches the maximum number of iterations, it is determined that the optimization conditions are currently met, and the target frequency sequence f1, f2...f is obtained. N This dynamic parameter adjustment based on the measured temperature deviation ensures that the model can quickly adapt to environmental changes, significantly improving user comfort and is particularly suitable for complex or dynamically changing indoor environments.

[0069] Of course, the objective function and optimization method of this step can also be in other forms, which are not specifically limited here.

[0070] S104 , regulating the frequency of the compressor in a corresponding regulation period based on at least one compressor frequency in the target frequency sequence.

[0071] Optionally, the first frequency value from the target frequency sequence can be selected as the compressor operating frequency for the first regulation cycle. During this regulation cycle, the compressor operates at this frequency constantly until the end of the cycle. This allows for rapid response to temperature deviations and reduces initial regulation lag. Single-frequency control also simplifies the controller logic, making it suitable for systems with limited computing resources.

[0072] Alternatively, the compressor can be adjusted sequentially based on the top-ranked frequency values ​​within the corresponding adjustment cycle. For example, the top three frequency values ​​correspond to the compressor operating frequencies for the first three adjustment cycles. Within each adjustment cycle, the compressor operates at that frequency until the end of the cycle. This gradual frequency adjustment adapts to dynamic changes in indoor temperature, optimizing temperature control accuracy and system stability, meeting operational requirements in complex environments.

[0073] Furthermore, after at least one frequency modulation cycle is completed, it is determined whether the current indoor temperature has reached the target indoor temperature. If the current indoor temperature has not reached the target indoor temperature after the frequency modulation, the process returns to S101. If the current indoor temperature has reached the target indoor temperature after the frequency modulation, the process ends.

[0074] Optionally, in the step of "determining whether the current indoor temperature has reached the target indoor temperature after the frequency modulation," if the indoor temperature is equal to the target indoor temperature, or if the absolute value of the difference between the indoor temperature and the target indoor temperature is less than a preset threshold, then it is determined that the current indoor temperature has reached the target indoor temperature after the frequency modulation. Of course, other methods are also possible and are not specifically limited here. Otherwise, it is determined that the current indoor temperature has not reached the target indoor temperature after the frequency modulation.

[0075] It is understood that after each frequency modulation (i.e., after each modulation cycle or multiple cycles), if the indoor temperature does not reach the target indoor temperature, it means that the frequency modulation based on the previous frequency sequence has not fully offset the load and further frequency correction is required. Therefore, by returning to S101 and subsequent steps, the total modulation time, equivalent frequency, and optimized frequency sequence are recalculated and the adjustment is performed again. Conversely, if the indoor temperature reaches the target indoor temperature, it means that the frequency modulation based on the previous frequency sequence can fully offset the load and the system ends the process. The compressor may stop running or enter a low-frequency maintenance mode to maintain temperature stability and save energy.

[0076] The above embodiment obtains the current and target indoor temperatures to accurately predict the total adjustment time and total load, thereby deriving the equivalent compressor frequency that meets the load requirements, providing a benchmark framework for frequency regulation. Based on this, an objective function is constructed with the frequency sequence as a variable. Combined with constraints set based on the equivalent frequency, the target frequency sequence is calculated through iterative optimization. This process fully considers system inertia, estimating the target frequency sequence before the temperature approaches the target value, and then rapidly and dynamically adjusting the frequency based on at least one compressor frequency in the target frequency sequence. This solution, through advance planning and dynamic adjustment, can quickly respond to temperature deviations, reduce initial adjustment lag, and thus minimize the occurrence of temperature overshoot.

[0077] See also Figure 2 , Figure 2 This is a flow chart of a compressor frequency modulation method provided in the second embodiment of the present application. Although the flow chart shows a logical order, in some cases, the steps shown or described may be performed in a different order than that shown in the accompanying drawings. Specifically, the specific process of the compressor frequency modulation method provided in the second embodiment of the present application is as follows:

[0078] S201: Determine whether the current continuous operation time of the compressor reaches the continuous operation time limit. If the continuous operation time reaches the continuous operation time limit, execute S202.

[0079] The continuous run time is the time the compressor runs uninterrupted from startup to the current moment. The continuous run time limit is the maximum continuous run time of the compressor, set to ensure system stability. This value is based on experimental data and equipment performance.

[0080] Optionally, a timer built into the controller records the continuous operation time of the compressor from startup and compares it with a preset continuous operation time limit (e.g., 10 minutes). If the continuous operation time reaches or exceeds the limit, the system is determined to have entered a stable operation state, triggering step S202. Conversely, if the continuous operation time does not reach the limit, the system continues to operate.

[0081] S202, obtaining the current indoor temperature and the target indoor temperature.

[0082] In some embodiments of the present application, the above S202 is basically consistent with the principle of "current indoor temperature and target indoor temperature" in S101 in the compressor frequency modulation method provided in the first embodiment, so it is not repeated here.

[0083] Understandably, because the system experiences significant parameter fluctuations (such as unstable temperature, pressure, or current) during initial startup, setting a time limit ensures the system enters a stable state, providing a reliable foundation for subsequent temperature data collection and parameter calculations. Furthermore, because subsequent steps require sufficient historical data to calculate coefficients A, B, and C, the compressor is allowed to accumulate sufficient historical data before executing subsequent steps in S201.

[0084] S203, predicting the total adjustment time and total load from the current indoor temperature to the target indoor temperature.

[0085] In some embodiments of the present application, the total adjustment time is predicted by:

[0086] S203A, calculating the difference between the current indoor temperature and the target indoor temperature as the real-time target temperature difference.

[0087] S203B, determining the input fuzzy set corresponding to the real-time target temperature difference through a preset membership function.

[0088] The membership function is a mathematical function that maps the real-time target temperature difference to a fuzzy set, quantifying the degree of fuzziness to which the temperature difference belongs. The input fuzzy set is the set of fuzzy linguistic variables, such as "low temperature difference," "medium temperature difference," or "high temperature difference," that are converted by the membership function into the real-time target temperature difference.

[0089] S203C: Perform fuzzy reasoning on the input fuzzy set according to a predefined fuzzy rule table to obtain an output fuzzy set for expressing the total adjustment time.

[0090] The fuzzy rule table is a predefined set of rules that, based on fuzzy control theory, maps input fuzzy sets to output fuzzy sets to describe the total adjustment time. The output fuzzy set is a set of fuzzy linguistic variables representing the total adjustment time, such as "short time," "medium time," or "long time," derived through fuzzy inference.

[0091] S203D, converting the output fuzzy set into the corresponding total adjustment time through defuzzification processing.

[0092] Among them, defuzzification processing refers to the process of converting the output fuzzy set into a specific value (total adjustment time), usually using the center of gravity method or the maximum membership method.

[0093] Optionally, the real-time target temperature difference is first calculated (e.g., 25°C - 22°C = 3°C). A triangular membership function is then used to map the real-time target temperature difference to an input fuzzy set. For example, if the preset temperature difference range is [-10°C, 10°C], this is divided into three fuzzy sets: "negative temperature difference" (-10°C to -2°C), "zero temperature difference" (-2°C to 2°C), and "positive temperature difference" (2°C to 10°C). For example, the membership of 3°C is 0.8 for "positive temperature difference" and 0.2 for "zero temperature difference." Fuzzy reasoning is then performed on the input fuzzy set based on a predefined fuzzy rule table (e.g., "If the temperature difference is positive, the total adjustment time is medium") to generate an output fuzzy set, which is divided into "short time," "medium time," and "long time." Finally, the centroid method is used for defuzzification, converting the output fuzzy set into a specific total adjustment time. For example, the centroid value corresponding to the output fuzzy set "medium time" is 90 seconds, which serves as the predicted total adjustment time.

[0094] The above S203A-S203D achieves accurate quantification of temperature adjustment requirements by calculating the real-time target temperature difference and using fuzzy control to predict the total adjustment time. This conversion method adapts to nonlinear environmental changes and improves the robustness and accuracy of the prediction.

[0095] In some embodiments of the present application, the total load prediction method includes:

[0096] S203a: Obtain a second load forecasting model.

[0097] Among them, the second load forecasting model is expressed as:

[0098]

[0099] In the above formula, Φ is the instantaneous load. K is the comprehensive heat transfer coefficient of the building. F is the effective heat exchange area of ​​the building. M is the air quality of the space. C p is the space specific heat. S is the load heat source. T out is the current outdoor temperature. in It is understandable that the second load prediction model refers to an instantaneous load calculation model constructed based on physical thermal principles.

[0100] S203b: Convert the second load forecasting model into a third load forecasting model.

[0101] Among them, the third load forecasting model is expressed as:

[0102]

[0103] In the above formula, is the average load in the time interval Δt. The definition parameter A is obtained by the equivalent of the first constant parameter K·F. The definition parameter B is obtained by the equivalent of the second constant parameter M·Cp The definition parameter C is obtained by equivalently modifying the third constant parameter S. is the average temperature within the time interval Δt. start is the indoor temperature at the beginning of the time interval Δt. end The room temperature at the end of the time interval Δt.

[0104] It can be understood that within a short period of time, such as multiple time intervals Δt, the first constant parameter K·F and the second constant parameter M·C can be approximately considered to be p The third constant parameter S is constant, and the definition coefficients are: A = K·F; B = M·C p ; C = S. And Φ is the average cooling capacity per unit time Instead, T in each time interval out Approximately unchanged, T in Use the average temperature within the period Therefore, within a relatively short period of time, the second load forecasting model can be integrally transformed to obtain a third load forecasting model representing the average load within the time interval.

[0105] S203c: Acquire multiple groups of historical data associated with the current regulation period, and solve the defined parameters in the third load forecasting model based on the historical data to obtain the first load forecasting model in the current regulation period.

[0106] Each set of historical data includes the outdoor temperature, average temperature, starting indoor temperature, ending indoor temperature and average load within a historical adjustment period, that is, obtaining variables in the third load model within different time intervals Δt.

[0107] Optionally, obtaining multiple sets of historical data associated with the current adjustment cycle may involve obtaining the most recent n sets (e.g., five sets) of historical data, based on the current moment. It will be appreciated that the historical data obtained is different during each iteration, thereby ensuring that the calculated definition parameters A, B, and C are consistent with the current operating state.

[0108] Optionally, for the left side of the equation in the third load prediction model, the real-time cooling capacity can be calculated by monitoring parameters such as the compressor suction side pressure, exhaust side pressure, compressor frequency, or the indoor return air temperature and wind speed, combined with the compressor performance curve or air heat formula, and then the average load can be derived. The remaining variables on the right side of the equation can be directly obtained through sensors or databases. Then, an equation is generated based on each set of historical data. Multiple sets of data form an overdetermined set of equations, and the prediction error is minimized by the least squares method to solve A, B, and C. Alternatively, the recursive least squares method is used to update the parameters A, B, and C group by group to adapt to dynamically changing operating conditions.

[0109] S203d: Determine a first load forecasting model in the current regulation period.

[0110] Among them, the first load forecasting model is expressed as:

[0111]

[0112] In the above formula, Φ z is the total load. is the total adjustment time t z The average load in the current period. A, B, C are the definition parameters in the current regulation period. out is the current outdoor temperature. is the total adjustment time t z The average temperature inside. in is the current indoor temperature. set It is understandable that the first load forecasting model refers to a load forecasting model that is calibrated based on historical data and adapted to the current regulation cycle.

[0113] Optionally, after calculating and obtaining the definition parameters A, B, and C of the current regulation cycle in S203c, some parameters in the third load forecasting model are replaced with the parameters of the current regulation cycle to obtain the first load forecasting model, wherein Can be used Determined by equal weighting method. start Use T in Replacement, T end Use T set replace.

[0114] S203e: Substitute the current outdoor temperature, the current indoor temperature, the target indoor temperature, and the total adjustment time into the first load prediction model to calculate the total load.

[0115] In steps S203a-S203e, a second load forecasting model is constructed based on physical thermal principles and then converted into a third load forecasting model. This simplifies the calculation from instantaneous load to average load, improves forecasting efficiency, and is suitable for the real-time computing requirements of embedded systems. Finally, by replacing parameters and using a weighted method to estimate the average temperature, the first load forecasting model is precisely matched to the current regulation cycle, enhancing the targeted load forecast within the current regulation cycle. Finally, parameters such as the real-time temperature and total regulation time are substituted to calculate the total load, achieving precise quantification of the total load.

[0116] S204: Determine the equivalent compressor frequency according to the total load.

[0117] In some embodiments of the present application, S204 determines the equivalent compressor frequency based on the total load, which specifically includes the following steps: obtaining the historical compressor frequency and historical load of the previous adjustment cycle, and performing proportional calculation based on the historical compressor frequency, historical load and total load to obtain the equivalent compressor frequency.

[0118] Optionally, the calculation formula for the equivalent compressor frequency Ω is:

[0119]

[0120] In the above formula, f i-1 is the historical compressor frequency; Φ i-1 is the historical load. It's understandable that the above formula uses the proportional relationship between historical load and frequency to derive the equivalent frequency required for total load, theoretically ensuring precise matching of the equivalent compressor frequency. Furthermore, because historical data from the previous regulation cycle is highly correlated with current operating conditions, this calculation directly uses data from the most recent cycle.

[0121] S205 , obtaining an objective function constructed based on the frequency sequence, and on the premise that the frequency sequence satisfies a frequency restriction condition, iteratively optimizing the objective function until the optimization condition is satisfied, so as to calculate a current target frequency sequence.

[0122] S206 , frequency-modulating the compressor in a corresponding adjustment period based on at least one compressor frequency in the target frequency sequence.

[0123] In some embodiments of the present application, the principles of the above S205-S206 are basically consistent with those of S103-S104 in the compressor frequency modulation method provided in the first embodiment, and therefore are not described in detail.

[0124] In general, the above embodiment ensures system stability by judging the continuous operation time of the compressor, providing a reliable basis for subsequent data collection; and by using fuzzy control to predict the total adjustment time, adapts to nonlinear environmental changes and improves prediction robustness; and by constructing and optimizing the load prediction model, combining historical data and real-time parameters, accurately quantifies the total load; and by determining the equivalent compressor frequency based on the proportional relationship of historical data, provides a basis for frequency optimization; and by optimizing the frequency sequence and dynamically adjusting the frequency, effectively reduces the temperature overshoot caused by system inertia, thereby improving user comfort and system operation stability.

[0125] To facilitate better implementation of the compressor frequency modulation method of the present application, the present application also provides a compressor frequency modulation device based on the above compressor frequency modulation method. The meanings of the terms herein are the same as those in the above compressor frequency modulation method. For specific implementation details, please refer to the description in the method embodiment.

[0126] See also Figure 3 , Figure 3 : is a structural diagram of a compressor frequency modulation device provided in an embodiment of the present application, which may specifically include:

[0127] The parameter acquisition module 301 is used to obtain the current indoor temperature and the target indoor temperature, and predict the total adjustment time and total load from the current indoor temperature to the target indoor temperature; wherein the total adjustment time includes at least one adjustment cycle;

[0128] The equivalent compressor frequency calculation module 302 is used to determine the equivalent compressor frequency according to the total load; wherein the equivalent compressor frequency is the equivalent total frequency required by the compressor to meet the total load within the total regulation time;

[0129] The frequency modulation module 303 is used to obtain an objective function constructed based on a frequency sequence. On the premise that the frequency sequence satisfies a frequency restriction condition, the objective function is iteratively optimized until the optimization condition is satisfied, so as to calculate the current target frequency sequence; wherein the frequency sequence includes at least one compressor frequency corresponding to each adjustment period, and the frequency restriction condition is constructed based on the equivalent compressor frequency; based on at least one compressor frequency in the target frequency sequence, the compressor is frequency modulated in the corresponding adjustment period. If the current indoor temperature does not reach the target indoor temperature after the frequency modulation, the parameter acquisition module is called until the current indoor temperature reaches the target indoor temperature after the frequency modulation.

[0130] In the above embodiment, the parameter acquisition module 301 is used to accurately predict the total adjustment time and total load by obtaining the current and target indoor temperatures. The equivalent compressor frequency calculation module 302 is used to further derive the equivalent compressor frequency that meets the load requirements, providing a reference framework for frequency regulation. The frequency modulation module 303 is used to construct an objective function based on this with the frequency sequence as a variable, and in combination with the constraints set based on the equivalent frequency, calculate the target frequency sequence through iterative optimization. This process fully considers system inertia, estimates the target frequency sequence before the temperature approaches the target value, and then performs rapid dynamic frequency modulation based on at least one compressor frequency in the target frequency sequence. It can be seen that this solution can quickly respond to temperature deviations through advance planning and dynamic adjustment, reduce initial adjustment lag, and thus reduce the occurrence of temperature overshoot.

[0131] In some embodiments of the present application, the method of predicting the total load includes:

[0132] Determine a first load forecasting model within the current regulation period; wherein the first load forecasting model is expressed as:

[0133]

[0134] In the above formula, Φ z is the total load; is the total adjustment time t zThe average load within the period; A, B, C are the definition parameters within the current regulation period; T out is the current outdoor temperature; is the total adjustment time t z The average temperature inside; T in is the current indoor temperature; T set is the target indoor temperature;

[0135] The current outdoor temperature, the current indoor temperature, the target indoor temperature and the total adjustment time are substituted into the first load prediction model to calculate the total load.

[0136] In some embodiments of the present application, determining a first load forecasting model within a current regulation period includes:

[0137] Obtain a second load forecasting model; wherein the second load forecasting model is expressed as:

[0138]

[0139] In the above formula, Φ is the instantaneous load; K is the comprehensive heat transfer coefficient of the building; F is the effective heat exchange area of ​​the building; M is the air quality of the space; C p is the space specific heat; S is the load heat source;

[0140] The second load forecasting model is converted into a third load forecasting model; wherein the third load forecasting model is expressed as:

[0141]

[0142] In the above formula, is the average load within the time interval Δt; parameter A is defined by the first constant parameter K·F equivalent; parameter B is defined by the second constant parameter M·C p Equivalently obtained; the definition parameter C is obtained by equivalently obtaining the third constant parameter S; is the average temperature within the time interval Δt; T start is the indoor temperature at the beginning of the time interval Δt; T end the indoor temperature at the end of the time interval Δt;

[0143] Acquire multiple sets of historical data associated with the current regulation cycle, and solve the defined parameters in the third load prediction model based on the historical data to obtain the first load prediction model within the current regulation cycle; wherein each set of historical data includes the outdoor temperature, average temperature, indoor temperature at the beginning, indoor temperature at the end, and average load within a historical regulation cycle.

[0144] In some embodiments of the present application, determining the equivalent compressor frequency according to the total load includes:

[0145] The historical compressor frequency and historical load of the previous regulation cycle are obtained, and a proportional calculation is performed based on the historical compressor frequency, historical load and total load to obtain the equivalent compressor frequency.

[0146] In some embodiments of the present application, the frequency limitation conditions include: the sum of the compressor frequencies corresponding to at least one adjustment cycle is equal to the equivalent compressor frequency, and the compressor frequencies corresponding to at least one adjustment cycle are all within a preset frequency range, and the absolute value of the difference between the compressor frequencies corresponding to two adjacent adjustment cycles is less than a preset difference threshold.

[0147] In some embodiments of the present application, obtaining the current indoor temperature and the target indoor temperature includes:

[0148] Determine whether the current continuous operation time of the compressor reaches the continuous operation time limit;

[0149] On the premise that the continuous operation time reaches the continuous operation time limit, the current indoor temperature and the target indoor temperature are obtained.

[0150] In some embodiments of the present application, the method of predicting the total adjustment time includes:

[0151] Calculate the difference between the current indoor temperature and the target indoor temperature as the real-time target temperature difference;

[0152] Determine the input fuzzy set corresponding to the real-time target temperature difference through a preset membership function;

[0153] Perform fuzzy reasoning on the input fuzzy set according to a predefined fuzzy rule table to obtain an output fuzzy set for expressing the total adjustment time;

[0154] The output fuzzy set is converted into the corresponding total adjustment time through defuzzification processing.

[0155] In addition, the present application also provides an air conditioner, such as Figure 4 As shown, it shows a structural diagram of the air conditioner involved in this application, specifically:

[0156] The air conditioner may include one or more processors 401, one or more computer-readable storage media memories 402, a power supply 403, an input unit 404, and other components. Figure 4 The air conditioner structure shown in the figure does not constitute a limitation on the air conditioner, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0157] Processor 401 is the control center of the air conditioner. It utilizes various interfaces and circuits to connect the various components of the air conditioner. By running or executing software programs and / or modules stored in memory 402 and accessing data stored in memory 402, it performs various functions of the air conditioner and processes data, thereby providing overall monitoring of the air conditioner. Optionally, processor 401 may include one or more processing cores; preferably, processor 401 may integrate an application processor and a modem processor, wherein the application processor primarily handles the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 401.

[0158] Memory 402 can be used to store software programs and modules. Processor 401 executes various functional applications and data processing by running the software programs and modules stored in memory 402. Memory 402 may primarily include a program storage area and a data storage area. The program storage area may store an operating system, at least one application required for a function, and the data storage area may store data generated based on the use of the air conditioner. Furthermore, memory 402 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, memory 402 may also include a memory controller to provide processor 401 with access to memory 402.

[0159] The air conditioner also includes a power supply 403 for supplying power to various components. Preferably, the power supply 403 can be logically connected to the processor 401 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The power supply 403 can also include one or more DC or AC power supplies, a recharging system, a power supply device debugging circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0160] The air conditioner may further include an input unit 404, which may be configured to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0161] Although not shown, the air conditioner may also include a display unit, etc., which will not be described in detail here. Specifically in this embodiment, the processor 401 in the air conditioner will load the executable files corresponding to the processes of one or more applications into the memory 402 according to the following instructions, and the processor 401 will run the application stored in the memory 402, thereby implementing the steps in any of the compressor frequency modulation methods provided in the embodiments of the present application: obtaining the current indoor temperature and the target indoor temperature, and predicting the total adjustment time and total load from the current indoor temperature to the target indoor temperature; wherein the total adjustment time includes at least one adjustment cycle; determining the equivalent compressor frequency according to the total load; wherein the equivalent compressor frequency is the total load required for the compressor to meet the total load within the total adjustment time. The required equivalent total frequency; obtaining an objective function constructed based on the frequency sequence, and on the premise that the frequency sequence satisfies the frequency restriction condition, iteratively optimizing the objective function until the optimization condition is satisfied, so as to calculate the current target frequency sequence; wherein, the frequency sequence includes at least one compressor frequency corresponding to each adjustment period, and the frequency restriction condition is constructed based on the equivalent compressor frequency; frequency modulation is performed on the compressor in the corresponding adjustment period based on at least one compressor frequency in the target frequency sequence, and if the current indoor temperature does not reach the target indoor temperature after the frequency modulation, returning to the step of obtaining the current indoor temperature and the target indoor temperature, until the current indoor temperature reaches the target indoor temperature after the frequency modulation.

[0162] The above embodiment obtains the current and target indoor temperatures to accurately predict the total adjustment time and total load, thereby deriving the equivalent compressor frequency that meets the load requirements, providing a benchmark framework for frequency regulation. Based on this, an objective function is constructed with the frequency sequence as a variable. Combined with constraints set based on the equivalent frequency, the target frequency sequence is calculated through iterative optimization. This process fully considers system inertia, estimating the target frequency sequence before the temperature approaches the target value, and then rapidly and dynamically adjusting the frequency based on at least one compressor frequency in the target frequency sequence. This solution, through advance planning and dynamic adjustment, can quickly respond to temperature deviations, reduce initial adjustment lag, and thus minimize the occurrence of temperature overshoot.

[0163] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0164] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0165] To this end, the present application provides a computer-readable storage medium, on which a computer program is stored. The computer program can be loaded by a processor to execute the steps in any compressor frequency modulation method provided in the present application.

[0166] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0167] The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0168] Since the instructions stored in the computer-readable storage medium can execute the steps in any compressor frequency modulation method provided in the present application, the beneficial effects that can be achieved by any compressor frequency modulation method provided in the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0169] The above is a detailed introduction to a compressor frequency modulation method, device, air conditioner and computer-readable storage medium provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A compressor frequency modulation method, characterized in that: The method comprises: Obtaining a current indoor temperature and a target indoor temperature, and predicting a total adjustment time and a total load from the current indoor temperature to the target indoor temperature; Determine an equivalent compressor frequency according to the total load; wherein the equivalent compressor frequency is the equivalent total frequency required for the compressor to meet the total load within the total adjustment time; Obtaining an objective function constructed based on a frequency sequence, and, on the premise that the frequency sequence satisfies a frequency restriction condition, iteratively optimizing the objective function until the optimization condition is satisfied, so as to calculate a current target frequency sequence; wherein the frequency restriction condition is constructed based on the equivalent compressor frequency; The frequency of the compressor is adjusted in a corresponding adjustment period based on at least one compressor frequency in the target frequency sequence.

2. The compressor frequency modulation method according to claim 1, characterized in that: Ways to predict the total load include: Determine a first load forecasting model within the current regulation period; wherein the first load forecasting model is expressed as: In the above formula, Φ z is the total load; is the total adjustment time t z The average load within the period; A, B, C are the definition parameters within the current regulation period; T out is the current outdoor temperature; is the total adjustment time t z The average temperature inside; T in is the current indoor temperature; T set is the target indoor temperature; The current outdoor temperature, the current indoor temperature, the target indoor temperature, and the total adjustment time are substituted into the first load prediction model to calculate the total load.

3. The compressor frequency modulation method according to claim 2, characterized in that: The determining of the first load forecasting model within the current regulation period includes: Obtain a second load forecasting model; wherein the second load forecasting model is expressed as: In the above formula, Φ is the instantaneous load; K is the comprehensive heat transfer coefficient of the building; F is the effective heat exchange area of ​​the building; M is the air quality of the space; C p is the space specific heat; S is the load heat source; The second load forecasting model is converted into a third load forecasting model; wherein the third load forecasting model is expressed as: In the above formula, is the average load within the time interval Δt; parameter A is defined by the first constant parameter K·F equivalent; parameter B is defined by the second constant parameter M·C p Equivalently obtained; the definition parameter C is obtained by equivalently obtaining the third constant parameter S; is the average temperature within the time interval Δt; T start is the indoor temperature at the beginning of the time interval Δt; T end the indoor temperature at the end of the time interval Δt; Acquire multiple sets of historical data associated with the current regulation cycle, and solve the defined parameters in the third load prediction model based on the historical data to obtain the first load prediction model within the current regulation cycle; wherein each set of historical data includes the outdoor temperature, average temperature, indoor temperature at the beginning, indoor temperature at the end, and average load within a historical regulation cycle.

4. The compressor frequency modulation method according to claim 1, characterized in that: The determining of the equivalent compressor frequency according to the total load includes: The historical compressor frequency and the historical load of the last adjustment cycle are obtained, and a proportional calculation is performed according to the historical compressor frequency, the historical load and the total load to obtain the equivalent compressor frequency.

5. The compressor frequency modulation method according to claim 1, characterized in that: The frequency restriction condition includes: the sum of the compressor frequencies corresponding to at least one adjustment cycle is equal to the equivalent compressor frequency, and the compressor frequencies corresponding to at least one adjustment cycle are all within a preset frequency range, and the absolute value of the difference between the compressor frequencies corresponding to two adjacent adjustment cycles is less than a preset difference threshold.

6. The compressor frequency modulation method according to claim 1, characterized in that: The obtaining of the current indoor temperature and the target indoor temperature includes: Determine whether the current continuous operation time of the compressor reaches the continuous operation time limit; On the premise that the continuous operation time reaches the continuous operation time limit, the current indoor temperature and the target indoor temperature are obtained.

7. The compressor frequency modulation method according to claim 1, characterized in that: Methods for predicting the total adjustment time include: Calculating the difference between the current indoor temperature and the target indoor temperature as the real-time target temperature difference; Determine the input fuzzy set corresponding to the real-time target temperature difference through a preset membership function; Performing fuzzy reasoning on the input fuzzy set according to a predefined fuzzy rule table to obtain an output fuzzy set for expressing the total adjustment time; The output fuzzy set is converted into the corresponding total adjustment time through defuzzification processing.

8. A compressor frequency modulation device, characterized in that: The compressor frequency modulation device comprises: a parameter acquisition module, configured to acquire a current indoor temperature and a target indoor temperature, and predict a total adjustment time and a total load from the current indoor temperature to the target indoor temperature; An equivalent compressor frequency calculation module is configured to determine an equivalent compressor frequency according to the total load; wherein the equivalent compressor frequency is an equivalent total frequency required for the compressor to meet the total load within the total adjustment time; A frequency modulation module is used to obtain an objective function constructed based on a frequency sequence, and on the premise that the frequency sequence satisfies a frequency restriction condition, iteratively optimize the objective function until the optimization condition is satisfied, so as to calculate the current target frequency sequence; wherein the frequency restriction condition is constructed based on the equivalent compressor frequency; and based on at least one compressor frequency in the target frequency sequence, the compressor is frequency modulated in a corresponding adjustment period.

9. A computer-readable storage medium, characterized in that A computer program is stored, and when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

10. An air conditioner, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

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