Heat pump fan speed regulation method and system based on capability and energy efficiency optimization

Abstract

The invention discloses a heat pump fan speed regulation method and system based on capability and energy efficiency optimization, and the method comprises the steps: judging whether heat pump equipment enters a steady-state operation state or not when the heat pump equipment works, and obtaining a steady-state judgment result; when the steady state judgment result is yes, repeated change optimization of the rotating speed of a fan of the heat pump equipment is started; when the rotating speed of the draught fan is changed every time, corresponding capacity parameters and energy efficiency parameters are calculated according to the real-time working condition data of the heat pump equipment; and according to the capability parameters, the energy efficiency parameters and a preset numerical value judgment rule, optimizing is repeated until the optimal fan rotating speed is determined. It can be seen that precise fan rotating speed self-adaptive optimization based on capacity and energy efficiency real-time optimization can be achieved, the overall energy efficiency and heat supply capacity of heat pump equipment are improved, and the risk that the energy efficiency is low or the capacity is insufficient due to the fact that the fan rotating speed is fixed is reduced.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for speed regulation of a heat pump fan based on optimization of capacity and energy efficiency. Background Technology

[0002] With the rapid application and widespread adoption of heat pump technology in various equipment sectors, users and manufacturers are increasingly focusing on achieving a dynamic balance between overall energy efficiency and heating capacity through fan speed optimization. Existing technologies typically employ fixed fan speeds or simple, step-by-step adjustments based on a single ambient temperature in heat pump equipment, relying on factory settings or empirical curves to control fan operation and maintain basic heating needs. However, existing solutions lack iterative calculations of real-time capacity and energy efficiency parameters under steady-state conditions, as well as numerically driven optimization mechanisms. This makes it difficult to achieve precise adaptive optimization of fan speed under complex external conditions. Commonly used fixed or coarse adjustment strategies cannot track actual heat load and environmental changes, leading to low overall heat pump energy efficiency or insufficient heating capacity. This can easily result in energy waste or decreased comfort, limiting the energy-saving performance and actual operating effect of heat pump equipment. Therefore, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a heat pump fan speed regulation method and system based on capacity and energy efficiency optimization, which can realize precise adaptive optimization of fan speed based on real-time capacity and energy efficiency optimization, improve the overall energy efficiency and heating capacity of heat pump equipment, and reduce the risk of low energy efficiency or insufficient capacity caused by fixed fan speed.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a method for speed regulation of a heat pump fan based on capacity and energy efficiency optimization, the method comprising: When the heat pump equipment is working, determine whether the heat pump equipment has entered a steady-state operating state, and obtain the steady-state determination result; When the steady-state judgment result is yes, the repeated optimization of the fan speed of the heat pump equipment is initiated. Each time the fan speed is changed, the corresponding capacity parameters and energy efficiency parameters are calculated based on the real-time operating data of the heat pump equipment. Based on the capacity parameters, the energy efficiency parameters, and the preset numerical judgment rules, the optimization process is repeated until the optimal fan speed is determined.

[0005] As an optional implementation, in the first aspect of the present invention, the step of calculating the corresponding capacity parameters and energy efficiency parameters based on the real-time operating data of the heat pump equipment includes: Calculate the corresponding equipment capacity based on the water delivery parameters of the heat pump equipment; Calculate the ratio of the capacity parameter to the total input power of the heat pump equipment to obtain the corresponding equipment energy efficiency; Calculate the capability difference between the current equipment capability and the equipment capability of the previous optimization cycle, and calculate the ratio between the capability difference and the equipment capability of the previous optimization cycle to obtain the corresponding capability change rate; Calculate the energy efficiency difference between the current equipment energy efficiency and the equipment energy efficiency in the previous optimization cycle, and calculate the ratio between the energy efficiency difference and the equipment energy efficiency in the previous optimization cycle to obtain the corresponding energy efficiency change rate.

[0006] As an optional implementation, in the first aspect of the invention, the device capability is calculated through the following steps: The specific heat capacity, density, flow rate, outlet temperature, and inlet temperature of the heat pump equipment are acquired in real time. Calculate the temperature difference between the outlet water temperature and the inlet water temperature; The equipment capacity is obtained by calculating the product of the water specific heat capacity parameter, the water density parameter, the water flow rate parameter, and the temperature difference.

[0007] As an optional implementation, in the first aspect of the present invention, the numerical judgment rule includes: When the rate of change of capacity is greater than or equal to the first threshold and the rate of change of energy efficiency is greater than or equal to the second threshold, it is determined that the current optimization belongs to the performance improvement, the wind turbine speed of the current optimization is accepted, and the next optimization is entered. When the rate of change of capacity is less than the first threshold or the rate of change of energy efficiency is less than the second threshold, it is determined that the current optimization belongs to performance degradation, and it is determined whether the two most recent previous optimizations both belong to performance degradation, and a first judgment result is obtained. If the first judgment result is yes, stop the optimization and output the latest accepted fan speed as the optimal fan speed; If the first judgment result is negative, the current fan speed is not accepted, and the next optimization is initiated.

[0008] As an optional implementation, in the first aspect of the present invention, when the first determination result is negative, the adjustment direction of the fan speed is changed to a direction different from the current optimization direction, so as to enter the next optimization; the adjustment direction is to increase the speed or decrease the speed.

[0009] As an optional implementation, in the first aspect of the invention, in each optimization process, the most recently accepted wind turbine speed is changed by the current speed adjustment step size based on the current adjustment direction; the numerical judgment rule includes: If two consecutive optimizations in the same adjustment direction both result in improved performance, the speed adjustment step size is increased by a preset ratio. When the rate of change of capability or the rate of change of energy efficiency decreases after a preset number of consecutive optimizations, the current speed adjustment step size is reduced to a preset initial step size. When the difference between the most recently accepted fan speed and the preset maximum or minimum speed is less than a preset difference threshold, the speed adjustment step size is reduced.

[0010] As an optional implementation, in the first aspect of the invention, the steady-state operating state includes at least one of the following conditions: The change in the inlet water temperature of the heat pump device is less than the first temperature threshold during a continuous first time period. The ambient temperature change value of the heat pump equipment is less than the second temperature threshold during a continuous second time period. The compressor frequency change value of the heat pump equipment is less than the preset frequency threshold during the third consecutive time period. The time difference between the current time point and the end time of the most recent optimization is less than the preset time threshold.

[0011] As an optional implementation, in the first aspect of the invention, the current fan speed is determined in each optimization process through the following steps: Obtain the current operating status data of the heat pump equipment; The current operating condition data is input into the trained optimal speed prediction model to obtain the current optimized wind turbine speed output; the optimal speed prediction model is trained using a training dataset that includes multiple historical operating condition data and corresponding optimal speed labels.

[0012] A second aspect of this invention discloses a heat pump fan speed control system based on capacity and energy efficiency optimization, the system comprising: The judgment module is used to determine whether the heat pump equipment has entered a steady-state operating state when the heat pump equipment is working, and to obtain a steady-state judgment result; The startup module is used to initiate repeated optimization of the fan speed of the heat pump equipment when the steady-state judgment result is yes. The calculation module is used to calculate the corresponding capacity parameters and energy efficiency parameters based on the real-time operating data of the heat pump equipment each time the fan speed is changed. The optimization module is used to repeatedly optimize according to the capacity parameters, the energy efficiency parameters and preset numerical judgment rules until the optimal wind turbine speed is determined.

[0013] As an optional implementation, in the second aspect of the present invention, the specific method by which the calculation module calculates the corresponding capacity parameters and energy efficiency parameters based on the real-time operating data of the heat pump equipment includes: Calculate the corresponding equipment capacity based on the water delivery parameters of the heat pump equipment; Calculate the ratio of the capacity parameter to the total input power of the heat pump equipment to obtain the corresponding equipment energy efficiency; Calculate the capability difference between the current equipment capability and the equipment capability of the previous optimization cycle, and calculate the ratio between the capability difference and the equipment capability of the previous optimization cycle to obtain the corresponding capability change rate; Calculate the energy efficiency difference between the current equipment energy efficiency and the equipment energy efficiency in the previous optimization cycle, and calculate the ratio between the energy efficiency difference and the equipment energy efficiency in the previous optimization cycle to obtain the corresponding energy efficiency change rate.

[0014] As an optional implementation, in a second aspect of the invention, the device capability is calculated through the following steps: The specific heat capacity, density, flow rate, outlet temperature, and inlet temperature of the heat pump equipment are acquired in real time. Calculate the temperature difference between the outlet water temperature and the inlet water temperature; The equipment capacity is obtained by calculating the product of the water specific heat capacity parameter, the water density parameter, the water flow rate parameter, and the temperature difference.

[0015] As an optional implementation, in a second aspect of the present invention, the numerical judgment rule includes: When the rate of change of capacity is greater than or equal to the first threshold and the rate of change of energy efficiency is greater than or equal to the second threshold, it is determined that the current optimization belongs to the performance improvement, the wind turbine speed of the current optimization is accepted, and the next optimization is entered. When the rate of change of capacity is less than the first threshold or the rate of change of energy efficiency is less than the second threshold, it is determined that the current optimization belongs to performance degradation, and it is determined whether the two most recent previous optimizations both belong to performance degradation, and a first judgment result is obtained. If the first judgment result is yes, stop the optimization and output the latest accepted fan speed as the optimal fan speed; If the first judgment result is negative, the current fan speed is not accepted, and the next optimization is initiated.

[0016] As an optional implementation, in a second aspect of the present invention, when the first determination result is negative, the adjustment direction of the fan speed is changed to a direction different from the current optimization direction, so as to enter the next optimization; the adjustment direction is to increase the speed or decrease the speed.

[0017] As an optional implementation, in the second aspect of the invention, in each optimization process, the most recently accepted wind turbine speed is changed by the current speed adjustment step size based on the current adjustment direction; the numerical judgment rule includes: If two consecutive optimizations in the same adjustment direction both result in improved performance, the speed adjustment step size is increased by a preset ratio. When the rate of change of capability or the rate of change of energy efficiency decreases after a preset number of consecutive optimizations, the current speed adjustment step size is reduced to a preset initial step size. When the difference between the most recently accepted fan speed and the preset maximum or minimum speed is less than a preset difference threshold, the speed adjustment step size is reduced.

[0018] As an optional implementation, in the second aspect of the invention, the steady-state operating state includes at least one of the following conditions: The change in the inlet water temperature of the heat pump device is less than the first temperature threshold during a continuous first time period. The ambient temperature change value of the heat pump equipment is less than the second temperature threshold during a continuous second time period. The compressor frequency change value of the heat pump equipment is less than the preset frequency threshold during the third consecutive time period. The time difference between the current time point and the end time of the most recent optimization is less than the preset time threshold.

[0019] As an optional implementation, in the second aspect of the invention, the current fan speed is determined in each optimization process through the following steps: Obtain the current operating status data of the heat pump equipment; The current operating condition data is input into the trained optimal speed prediction model to obtain the current optimized wind turbine speed output; the optimal speed prediction model is trained using a training dataset that includes multiple historical operating condition data and corresponding optimal speed labels.

[0020] A third aspect of the present invention discloses another heat pump fan speed control system based on capacity and energy efficiency optimization, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the heat pump fan speed regulation method based on capability and energy efficiency optimization disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the heat pump fan speed regulation method based on capacity and energy efficiency optimization disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention optimizes the fan speed by repeatedly changing it during the steady-state operation of the heat pump equipment. After each change, the capacity and energy efficiency parameters are calculated based on real-time operating data and iterated according to numerical judgment rules until the optimal fan speed is determined. This enables precise adaptive optimization of the fan speed based on real-time optimization of capacity and energy efficiency, improving the overall energy efficiency and heating capacity of the heat pump equipment and reducing the risk of low energy efficiency or insufficient capacity caused by fixed fan speed. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of a heat pump fan speed regulation method based on capacity and energy efficiency optimization disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of a heat pump fan speed control system based on capacity and energy efficiency optimization disclosed in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of another heat pump fan speed control system based on capacity and energy efficiency optimization disclosed in an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] This invention discloses a heat pump fan speed regulation method and system based on capacity and energy efficiency optimization. By repeatedly changing the fan speed during steady-state operation of the heat pump equipment, the system calculates capacity and energy efficiency parameters based on real-time operating data after each change and iterates according to numerical judgment rules until the optimal fan speed is determined. This enables precise adaptive optimization of the fan speed based on real-time capacity and energy efficiency optimization, improving the overall energy efficiency and heating capacity of the heat pump equipment and reducing the risk of low energy efficiency or insufficient capacity due to fixed fan speed. Detailed explanations follow.

[0031] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for speed regulation of a heat pump fan based on capacity and energy efficiency optimization, as disclosed in an embodiment of the present invention. Figure 1 The described method for speed regulation of heat pump fans based on capability and energy efficiency optimization can be applied to data processing systems / data processing equipment / data processing servers (including local processing servers or cloud processing servers). For example... Figure 1 As shown, the heat pump fan speed regulation method based on capacity and energy efficiency optimization may include the following operations: 101. Determine whether the heat pump equipment has entered a steady-state operating state when it is working, and obtain the steady-state judgment result.

[0032] 102. When the steady-state judgment result is yes, start the repeated change optimization of the fan speed of the heat pump equipment.

[0033] 103. Each time the fan speed is changed, calculate the corresponding capacity parameters and energy efficiency parameters based on the real-time operating data of the heat pump equipment.

[0034] 104. Based on the capacity parameters, energy efficiency parameters, and preset numerical judgment rules, repeatedly seek optimization until the optimal fan speed is determined.

[0035] As can be seen, the above-mentioned embodiments of the invention achieve precise adaptive optimization of fan speed based on real-time optimization of capacity and energy efficiency during the steady-state operation of the heat pump equipment. After each change, the capacity and energy efficiency parameters are calculated based on real-time operating data and iterated according to numerical judgment rules until the optimal fan speed is determined. This improves the overall energy efficiency and heating capacity of the heat pump equipment and reduces the risk of low energy efficiency or insufficient capacity caused by fixed fan speed.

[0036] As an optional embodiment, the step above, calculating the corresponding capacity parameters and energy efficiency parameters based on the real-time operating data of the heat pump equipment, includes: Calculate the corresponding equipment capacity based on the water delivery parameters of the heat pump equipment; The energy efficiency of the equipment is obtained by calculating the ratio of the calculated capacity parameter to the total input power of the heat pump equipment. Calculate the capability difference between the current equipment capability and the equipment capability of the previous optimization cycle, and calculate the ratio between the capability difference and the equipment capability of the previous optimization cycle to obtain the corresponding capability change rate; Calculate the energy efficiency difference between the current equipment energy efficiency and the equipment energy efficiency in the previous optimization cycle, and calculate the ratio between the energy efficiency difference and the equipment energy efficiency in the previous optimization cycle to obtain the corresponding energy efficiency change rate.

[0037] As can be seen, through the above optional embodiments, the energy efficiency of the equipment is obtained by calculating the equipment capacity and the ratio of computing capacity to total input power in real time. Then, the rate of change of capacity and the rate of change of energy efficiency between the current and previous calculations are calculated to achieve accurate quantitative tracking of the performance changes of the heat pump. This provides reliable data support for subsequent optimization direction and step size decisions, and reduces the risk of misjudgment in optimization caused by deviations in performance index calculations.

[0038] As an optional embodiment, the device capability in the above steps is calculated through the following steps: Real-time acquisition of water specific heat capacity parameters, water density parameters, water flow rate parameters, outlet water temperature and inlet water temperature of heat pump equipment; Calculate the temperature difference between the outlet water temperature and the inlet water temperature; The equipment capacity is obtained by multiplying the specific heat capacity of water, the density of water, the flow rate of water, and the temperature difference.

[0039] As can be seen, the above optional embodiments define the approach of calculating the specific heat capacity of water, water density, water flow rate and the product of the inlet and outlet water temperature difference to obtain the equipment capacity, so as to accurately calculate the equipment capacity, facilitate the subsequent accurate quantitative tracking of heat pump performance changes, provide reliable data support for subsequent optimization direction and step size decisions, and reduce the risk of optimization misjudgment caused by performance index calculation deviations.

[0040] As an optional embodiment, the numerical judgment rules in the above steps include: When the rate of change of capacity is greater than or equal to the first threshold and the rate of change of energy efficiency is greater than or equal to the second threshold, it is determined that the current optimization belongs to the performance improvement, the wind turbine speed of the current optimization is accepted, and the next optimization is entered. When the rate of change in capacity is less than the first threshold or the rate of change in energy efficiency is less than the second threshold, it is determined that the current optimization belongs to performance degradation, and it is determined whether the two most recent previous optimizations both belong to performance degradation, thus obtaining the first judgment result. If the first judgment result is yes, stop the optimization and output the latest accepted fan speed as the optimal fan speed; If the first judgment result is negative, the current optimal fan speed is not accepted, and the next optimization is initiated.

[0041] As can be seen, by setting the rules for judging performance increase and decrease, and stopping the optimization and outputting the optimal speed when the performance decreases three times in a row, the efficiency of wind turbine speed optimization and the accuracy of the optimal solution are improved, and the risk of suboptimal speed caused by incorrect direction or premature termination is reduced.

[0042] As an optional embodiment, in the above steps, when the first judgment result is negative, the adjustment direction of the fan speed is changed to a direction different from the current optimization adjustment direction, so as to enter the next optimization; the adjustment direction is to increase the speed or decrease the speed.

[0043] As can be seen, the above optional embodiments realize an intelligent optimization strategy that adjusts the direction of the fan speed in the opposite direction during a single descent, thereby achieving rapid convergence and improving the efficiency of fan speed optimization and the accuracy of the optimal solution.

[0044] As an optional embodiment, in the above steps, the most recently accepted wind turbine speed is changed by the current speed adjustment step size based on the current adjustment direction in each optimization process; the numerical judgment rules include: When two consecutive optimizations in the same direction both result in improved performance, the speed adjustment step size is increased by a preset ratio. When the rate of change of capacity or the rate of change of energy efficiency decreases after a preset number of consecutive optimization attempts, the current speed adjustment step size is reduced to the preset initial step size. When the speed difference between the most recently accepted fan speed and the preset maximum or minimum speed is less than the preset difference threshold, the speed adjustment step size is reduced.

[0045] As can be seen, through the above optional embodiments, by proportionally increasing the speed step size when the performance increases in the same direction twice in a row, restoring the initial step size when the rate of change decreases, and decreasing the step size when approaching the boundary, adaptive dynamic step size adjustment is achieved, which improves the exploration efficiency and precision of the optimization process and reduces the risk of slow optimization or oscillation caused by fixed step size.

[0046] As an optional embodiment, the steady-state operating state in the above steps includes at least one of the following conditions: The change in the inlet water temperature of the heat pump equipment is less than the first temperature threshold within a continuous first time period; The ambient temperature change of the heat pump equipment is less than the second temperature threshold during the continuous second time period; The compressor frequency change value of the heat pump equipment is less than the preset frequency threshold in the third consecutive time period; The time difference between the current time point and the end time of the most recent optimization is less than the preset time threshold.

[0047] As can be seen, through the above optional embodiments, by taking the comprehensive inlet water temperature, ambient temperature, long-term stability of compressor frequency, and sufficient time since the last optimization as steady-state judgment conditions, the accurate identification of the truly stable operating state of the heat pump can be achieved, avoiding erroneous start-up optimization in the non-steady-state stage, improving the effectiveness and reliability of optimization results, and reducing the risk of incorrect optimal speed caused by non-steady-state optimization.

[0048] As an optional embodiment, in the above steps, the current fan speed is determined in each optimization process through the following steps: Obtain current operating data of the heat pump equipment; The current operating condition data is input into the trained optimal speed prediction model to obtain the current optimal wind turbine speed output. The optimal speed prediction model is trained using a training dataset that includes multiple historical operating condition data and corresponding optimal speed labels.

[0049] As can be seen, through the above optional embodiments, by inputting the current operating condition data into the trained optimal speed prediction model and directly outputting the wind turbine speed for this optimization, data-driven intelligent speed recommendation is achieved, improving the accuracy and convergence speed of each optimization starting point, and reducing the risk of excessive optimization time or getting trapped in local optima due to random or fixed step size starting.

[0050] Example 2 Please see Figure 2 , Figure 2This is a schematic diagram of a heat pump fan speed control system based on capacity and energy efficiency optimization, as disclosed in an embodiment of the present invention. Figure 2 The described heat pump fan speed control system based on capability and energy efficiency optimization can be applied to data processing systems / data processing equipment / data processing servers (including local processing servers or cloud processing servers). For example... Figure 2 As shown, the heat pump fan speed control system based on capacity and energy efficiency optimization may include: The judgment module 201 is used to determine whether the heat pump equipment has entered a steady-state operating state when the heat pump equipment is working, and to obtain a steady-state judgment result.

[0051] The startup module 202 is used to start repeated changes to optimize the fan speed of the heat pump equipment when the steady-state judgment result is yes.

[0052] The calculation module 203 is used to calculate the corresponding capacity parameters and energy efficiency parameters based on the real-time operating data of the heat pump equipment each time the fan speed is changed.

[0053] The optimization module 204 is used to repeatedly optimize according to the capacity parameters, energy efficiency parameters and preset numerical judgment rules until the optimal fan speed is determined.

[0054] As can be seen, the above-mentioned embodiments of the invention achieve precise adaptive optimization of fan speed based on real-time optimization of capacity and energy efficiency during the steady-state operation of the heat pump equipment. After each change, the capacity and energy efficiency parameters are calculated based on real-time operating data and iterated according to numerical judgment rules until the optimal fan speed is determined. This improves the overall energy efficiency and heating capacity of the heat pump equipment and reduces the risk of low energy efficiency or insufficient capacity caused by fixed fan speed.

[0055] As an optional embodiment, the calculation module calculates the corresponding capacity parameters and energy efficiency parameters based on the real-time operating data of the heat pump equipment in the following specific ways: Calculate the corresponding equipment capacity based on the water delivery parameters of the heat pump equipment; The energy efficiency of the equipment is obtained by calculating the ratio of the calculated capacity parameter to the total input power of the heat pump equipment. Calculate the capability difference between the current equipment capability and the equipment capability of the previous optimization cycle, and calculate the ratio between the capability difference and the equipment capability of the previous optimization cycle to obtain the corresponding capability change rate; Calculate the energy efficiency difference between the current equipment energy efficiency and the equipment energy efficiency in the previous optimization cycle, and calculate the ratio between the energy efficiency difference and the equipment energy efficiency in the previous optimization cycle to obtain the corresponding energy efficiency change rate.

[0056] As can be seen, through the above optional embodiments, the energy efficiency of the equipment is obtained by calculating the equipment capacity and the ratio of computing capacity to total input power in real time. Then, the rate of change of capacity and the rate of change of energy efficiency between the current and previous calculations are calculated to achieve accurate quantitative tracking of the performance changes of the heat pump. This provides reliable data support for subsequent optimization direction and step size decisions, and reduces the risk of misjudgment in optimization caused by deviations in performance index calculations.

[0057] As an optional embodiment, the device capability is calculated through the following steps: Real-time acquisition of water specific heat capacity parameters, water density parameters, water flow rate parameters, outlet water temperature and inlet water temperature of heat pump equipment; Calculate the temperature difference between the outlet water temperature and the inlet water temperature; The equipment capacity is obtained by multiplying the specific heat capacity of water, the density of water, the flow rate of water, and the temperature difference.

[0058] As can be seen, the above optional embodiments define the approach of calculating the specific heat capacity of water, water density, water flow rate and the product of the inlet and outlet water temperature difference to obtain the equipment capacity, so as to accurately calculate the equipment capacity, facilitate the subsequent accurate quantitative tracking of heat pump performance changes, provide reliable data support for subsequent optimization direction and step size decisions, and reduce the risk of optimization misjudgment caused by performance index calculation deviations.

[0059] As an optional embodiment, the numerical judgment rules include: When the rate of change of capacity is greater than or equal to the first threshold and the rate of change of energy efficiency is greater than or equal to the second threshold, it is determined that the current optimization belongs to the performance improvement, the wind turbine speed of the current optimization is accepted, and the next optimization is entered. When the rate of change in capacity is less than the first threshold or the rate of change in energy efficiency is less than the second threshold, it is determined that the current optimization belongs to performance degradation, and it is determined whether the two most recent previous optimizations both belong to performance degradation, thus obtaining the first judgment result. If the first judgment result is yes, stop the optimization and output the latest accepted fan speed as the optimal fan speed; If the first judgment result is negative, the current optimal fan speed is not accepted, and the next optimization is initiated.

[0060] As can be seen, by setting the rules for judging performance increase and decrease, and stopping the optimization and outputting the optimal speed when the performance decreases three times in a row, the efficiency of wind turbine speed optimization and the accuracy of the optimal solution are improved, and the risk of suboptimal speed caused by incorrect direction or premature termination is reduced.

[0061] As an optional embodiment, when the first determination result is negative, the adjustment direction of the fan speed is changed to a direction different from the current optimization direction, so as to enter the next optimization; the adjustment direction is to increase the speed or decrease the speed.

[0062] As can be seen, the above optional embodiments realize an intelligent optimization strategy that adjusts the direction of the fan speed in the opposite direction during a single descent, thereby achieving rapid convergence and improving the efficiency of fan speed optimization and the accuracy of the optimal solution.

[0063] As an optional embodiment, in each optimization process, the most recently accepted wind turbine speed is changed by the current speed adjustment step size based on the current adjustment direction; the numerical judgment rules include: When two consecutive optimizations in the same direction both result in improved performance, the speed adjustment step size is increased by a preset ratio. When the rate of change of capacity or the rate of change of energy efficiency decreases after a preset number of consecutive optimization attempts, the current speed adjustment step size is reduced to the preset initial step size. When the speed difference between the most recently accepted fan speed and the preset maximum or minimum speed is less than the preset difference threshold, the speed adjustment step size is reduced.

[0064] As can be seen, through the above optional embodiments, by proportionally increasing the speed step size when the performance increases in the same direction twice in a row, restoring the initial step size when the rate of change decreases, and decreasing the step size when approaching the boundary, adaptive dynamic step size adjustment is achieved, which improves the exploration efficiency and precision of the optimization process and reduces the risk of slow optimization or oscillation caused by fixed step size.

[0065] As an optional embodiment, the steady-state operating state includes at least one of the following conditions: The change in the inlet water temperature of the heat pump equipment is less than the first temperature threshold within a continuous first time period; The ambient temperature change of the heat pump equipment is less than the second temperature threshold during the continuous second time period; The compressor frequency change value of the heat pump equipment is less than the preset frequency threshold in the third consecutive time period; The time difference between the current time point and the end time of the most recent optimization is less than the preset time threshold.

[0066] As can be seen, through the above optional embodiments, by taking the comprehensive inlet water temperature, ambient temperature, long-term stability of compressor frequency, and sufficient time since the last optimization as steady-state judgment conditions, the accurate identification of the truly stable operating state of the heat pump can be achieved, avoiding erroneous start-up optimization in the non-steady-state stage, improving the effectiveness and reliability of optimization results, and reducing the risk of incorrect optimal speed caused by non-steady-state optimization.

[0067] As an optional embodiment, the current fan speed for optimization is determined in each optimization process through the following steps: Obtain current operating data of the heat pump equipment; The current operating condition data is input into the trained optimal speed prediction model to obtain the current optimal wind turbine speed output. The optimal speed prediction model is trained using a training dataset that includes multiple historical operating condition data and corresponding optimal speed labels.

[0068] As can be seen, through the above optional embodiments, by inputting the current operating condition data into the trained optimal speed prediction model and directly outputting the wind turbine speed for this optimization, data-driven intelligent speed recommendation is achieved, improving the accuracy and convergence speed of each optimization starting point, and reducing the risk of excessive optimization time or getting trapped in local optima due to random or fixed step size starting.

[0069] Specifically, in one specific implementation scheme, a speed regulation method for a heat pump fan is implemented based on the technical solution in the embodiments of the present invention, the steps of which include: System initialization and parameter settings: a: Set the allowable range of fan speed: [N_min, N_max] (e.g., [500, 1200] RPM).

[0070] b: Set the optimization period T (e.g., 10 minutes) and the speed adjustment step size ΔN (e.g., 50 RPM).

[0071] c: Set steady-state judgment conditions: within 3 consecutive sampling periods, the change in influent temperature is <0.5℃ and the change in ambient temperature is <0.5℃; d: Pre-store the initial rotational speed lookup table N_init=f(T_env,T_in) based on ambient temperature (T_env) and inlet water temperature (T_in) in the controller memory; Initial speed setting: After the system starts up, it monitors the ambient temperature T_env and the inlet water temperature T_in in real time, obtains the initial fan speed N_current by looking up a table, and adjusts the fan to run at that speed.

[0072] Steady-state operation determination: The system continuously monitors changes in ambient temperature and inlet water temperature. When the following conditions are met simultaneously, the system is determined to have entered a steady-state operating state and the optimization process is initiated: a: For 3 consecutive minutes, |ΔT_env| < 0.5℃.

[0073] b: For 3 consecutive minutes, |ΔT_in| < 0.5℃.

[0074] c: Compressor frequency variation < 5Hz (if it is an inverter compressor) Reference parameter acquisition: At the start of the optimization cycle, the baseline parameters under the current operating conditions are collected and calculated: a: System real-time capability: Q_current=c*ρ*V*(T_out-T_in); Where c is the specific heat capacity of water, ρ is the density of water, V is the water flow rate, T_out is the outlet water temperature, and T_in is the inlet water temperature; b: Real-time system energy efficiency: COP_current = Q_current / P_total Where P_total is the total system input power, including heat pump equipment modules such as compressors, fans, and water pumps; c: Records the current fan speed N_current; Exploring directions for optimization: Try changing the fan speed according to the preset adjustment direction: a: If this is the first optimization or the performance improved after the last adjustment, then maintain the original adjustment direction; b: Otherwise, reverse the direction; Adjust the fan speed to N_try = N_current + ΔN (or N_try = N_current - ΔN); Ensure that N_min ≤ N_try ≤ N_max; if the boundary is exceeded, the direction is automatically reversed. Performance evaluation and decision-making: After the system has been running stably for 1-2 sampling cycles, data is collected and calculated at the new rotational speed N_try: a: New system capability Q_try; b: New system energy efficiency COP_try; c: Rate of change of capacity: ΔQ = (Q_try - Q_current) / Q_current; d: Energy efficiency change rate: ΔCOP=(COP_try-COP_current) / COP_current; Performance evaluation rules: a: If ΔQ>0 and ΔCOP>0: Performance improvement, accept this adjustment, update N_current=N_try; b: If ΔQ>0 and ΔCOP≈0: Capacity is improved and energy efficiency is not reduced, accept this adjustment; c: If ΔCOP>0 and ΔQ≈0: Energy efficiency is improved and capacity is not reduced, accept this adjustment; d: If ΔQ<0 or ΔCOP<0: Capacity or energy efficiency has decreased, reject this adjustment and reverse the adjustment direction; e: If three consecutive adjustments result in a performance decrease, it is determined that the optimal speed point has been found, and the optimization process is paused; Step size optimization: a: When two consecutive adjustments in the same direction result in performance improvement, increase the adjustment step size ΔN by 50% (e.g., 50→75RPM) to speed up the optimization process; b: When the performance improvement rate is detected to be gradually decreasing, the adjustment step size ΔN will be reduced to the initial value for fine adjustment; c: When approaching the speed limit, the step size is automatically reduced to prevent overshoot; Continuous monitoring and re-optimization: a: During non-optimal periods, continuously monitor the steady-state conditions of the system; b: When the ambient temperature or inlet water temperature is detected to change beyond the set threshold (e.g., ±2℃), it is determined that the working condition has changed and execution is restarted from (2); c: Force restart the optimization process at fixed intervals (e.g., every 4 hours) to adapt to possible slow changes in operating conditions.

[0075] The embodiment of this invention is illustrated using the specific process of fan speed regulation in an air source heat pump unit as an example. Specifically, the unit system includes a variable frequency compressor, a variable frequency outdoor fan, a water pump, a water flow sensor, an inlet water temperature sensor, an outlet water temperature sensor, an ambient temperature sensor, a system total power detection module, and a main controller. The main controller uses a microprocessor and is responsible for executing the intelligent speed regulation algorithm.

[0076] Specific algorithm parameter settings: a: Fan speed range: [500, 1200] RPM b: Optimization cycle: 10 minutes c: Initial step size: 50 RPM d: Steady-state determination threshold: Temperature change < 0.5℃ for 3 consecutive minutes. The specific algorithm working process includes: a: The system starts up, detects the current ambient temperature as 25℃ and the inlet water temperature as 15℃, and finds the initial speed as 750RPM from the table.

[0077] b: After the system runs for 12 minutes, it is determined that it has entered a steady state (temperature fluctuation < 0.5℃).

[0078] c: Baseline parameters acquired: Q_current=8.5kW, COP_current=3.6, N_current=750RPM.

[0079] d: Initial optimization attempt, try increasing the speed to 800 RPM.

[0080] e: After 2 minutes of stable operation, new parameters were collected: Q_try=8.7kW, COP_try=3.7.

[0081] f: Calculated ΔQ = +2.4%, ΔCOP = +2.8%, performance improvement, adjustment accepted. N_current updated to 800RPM.

[0082] g: After 10 minutes, optimize again and continue to increase the speed to 850 RPM.

[0083] h: Collect new parameters: Q_try=8.6kW, COP_try=3.65.

[0084] i: The calculated ΔQ = -1.1% and ΔCOP = -1.4%, indicating a performance degradation. Therefore, the adjustment is rejected and the direction is reversed.

[0085] j: During the next optimization, the speed was reduced to 800 RPM, and the performance was confirmed to be better than 850 RPM.

[0086] k: Continued to try 790RPM and 810RPM, and found that the performance was best at 800RPM, and the system maintained this speed.

[0087] l: When the ambient temperature rises from 25℃ to 28℃, the system detects the change in operating conditions and restarts the optimization process.

[0088] Specifically, historical data can be used to learn or train corresponding prediction models. For frequently occurring working conditions, the optimal speed can be recorded. When a similar working condition is encountered again, this speed can be directly set or the optimal speed can be predicted, reducing the optimization time.

[0089] Specifically, abnormal operating condition protection is added. When abnormal parameters such as high or low pressure are detected in the system, optimization is immediately suspended and protective control is implemented.

[0090] In summary, the solutions disclosed in the embodiments of the present invention have the following advantages: (1) Significantly improve system energy efficiency: By finding the optimal fan speed in real time, the heat exchange effect and fan power consumption are balanced to the best point, which improves the overall energy efficiency of the system compared with traditional fixed speed control; (2) Strong adaptability: The dynamic optimization algorithm can automatically adapt to different environmental temperatures, inlet water temperatures, system loads and other operating conditions, ensuring that the system can operate in the optimal state under various conditions; (3) Compensation for performance degradation: As equipment ages or heat exchangers become clogged, the optimal operating point of the system will change. The continuous optimization feature of this method can automatically track this change and compensate for performance degradation to a certain extent; (4) Improve system stability: The steady-state determination mechanism ensures that the optimization process is carried out only when the system is running stably, avoiding erroneous adjustments under transient conditions and improving the stability of system control.

[0091] (5) Simple to implement and low cost: The core of this solution is the software algorithm, which can be implemented by simply upgrading the program in the existing variable frequency fan controller without increasing hardware costs.

[0092] Example 3 Please see Figure 3 , Figure 3 This is another heat pump fan speed control system based on capability and energy efficiency optimization disclosed in the embodiments of the present invention. Figure 3 The described heat pump fan speed control system based on capability and energy efficiency optimization is applied in data processing systems / data processing equipment / data processing servers (wherein, the server includes local processing servers or cloud processing servers). For example... Figure 3 As shown, the heat pump fan speed control system based on capacity and energy efficiency optimization may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the heat pump fan speed regulation method based on capacity and energy efficiency optimization described in Embodiment 1.

[0093] Example 4 This invention discloses a computer read storage medium that stores a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the heat pump fan speed regulation method based on capacity and energy efficiency optimization described in Embodiment 1.

[0094] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the heat pump fan speed regulation method based on capability and energy efficiency optimization described in Embodiment 1.

[0095] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0096] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0097] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0098] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0103] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0104] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0105] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0106] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0107] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0108] Finally, it should be noted that the heat pump fan speed regulation method and system based on capacity and energy efficiency optimization disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for speed regulation of a heat pump fan based on capacity and energy efficiency optimization, characterized in that, The method includes: When the heat pump equipment is working, determine whether the heat pump equipment has entered a steady-state operating state, and obtain the steady-state determination result; When the steady-state judgment result is yes, the repeated optimization of the fan speed of the heat pump equipment is initiated. Each time the fan speed is changed, the corresponding capacity parameters and energy efficiency parameters are calculated based on the real-time operating data of the heat pump equipment. Based on the capacity parameters, the energy efficiency parameters, and the preset numerical judgment rules, the optimization process is repeated until the optimal fan speed is determined.

2. The method for speed regulation of a heat pump fan based on capacity and energy efficiency optimization according to claim 1, characterized in that, The step of calculating the corresponding capacity parameters and energy efficiency parameters based on the real-time operating data of the heat pump equipment includes: Calculate the corresponding equipment capacity based on the water delivery parameters of the heat pump equipment; Calculate the ratio of the capacity parameter to the total input power of the heat pump equipment to obtain the corresponding equipment energy efficiency; Calculate the capability difference between the current equipment capability and the equipment capability of the previous optimization cycle, and calculate the ratio between the capability difference and the equipment capability of the previous optimization cycle to obtain the corresponding capability change rate; Calculate the energy efficiency difference between the current equipment energy efficiency and the equipment energy efficiency in the previous optimization cycle, and calculate the ratio between the energy efficiency difference and the equipment energy efficiency in the previous optimization cycle to obtain the corresponding energy efficiency change rate.

3. The heat pump fan speed regulation method based on capacity and energy efficiency optimization according to claim 2, characterized in that, The equipment capability is calculated through the following steps: The specific heat capacity, density, flow rate, outlet temperature, and inlet temperature of the heat pump equipment are acquired in real time. Calculate the temperature difference between the outlet water temperature and the inlet water temperature; The equipment capacity is obtained by calculating the product of the water specific heat capacity parameter, the water density parameter, the water flow rate parameter, and the temperature difference.

4. The method for speed regulation of a heat pump fan based on capacity and energy efficiency optimization according to claim 2, characterized in that, The numerical judgment rules include: When the rate of change of capacity is greater than or equal to the first threshold and the rate of change of energy efficiency is greater than or equal to the second threshold, it is determined that the current optimization belongs to the performance improvement, the wind turbine speed of the current optimization is accepted, and the next optimization is entered. When the rate of change of capacity is less than the first threshold or the rate of change of energy efficiency is less than the second threshold, it is determined that the current optimization belongs to performance degradation, and it is determined whether the two most recent previous optimizations both belong to performance degradation, and a first judgment result is obtained. If the first judgment result is yes, stop the optimization and output the latest accepted fan speed as the optimal fan speed; If the first judgment result is negative, the current fan speed is not accepted, and the next optimization is initiated.

5. The method for speed regulation of a heat pump fan based on capacity and energy efficiency optimization according to claim 4, characterized in that, If the first judgment result is negative, the adjustment direction of the fan speed is changed to a direction different from the current optimization direction, so as to enter the next optimization; the adjustment direction is to increase the speed or decrease the speed.

6. The method for speed regulation of a heat pump fan based on capacity and energy efficiency optimization according to claim 4, characterized in that, In each optimization process, the most recently accepted wind turbine speed is adjusted by the current speed adjustment step size based on the current adjustment direction. The numerical judgment rules include: If two consecutive optimizations in the same adjustment direction both result in improved performance, the speed adjustment step size is increased by a preset ratio. When the rate of change of capability or the rate of change of energy efficiency decreases after a preset number of consecutive optimizations, the current speed adjustment step size is reduced to a preset initial step size. When the difference between the most recently accepted fan speed and the preset maximum or minimum speed is less than a preset difference threshold, the speed adjustment step size is reduced.

7. The method for speed regulation of a heat pump fan based on capacity and energy efficiency optimization according to claim 1, characterized in that, The steady-state operating state includes at least one of the following conditions: The change in the inlet water temperature of the heat pump device is less than the first temperature threshold during a continuous first time period. The ambient temperature change value of the heat pump equipment is less than the second temperature threshold during a continuous second time period. The compressor frequency change value of the heat pump equipment is less than the preset frequency threshold during the third consecutive time period. The time difference between the current time point and the end time of the most recent optimization is less than the preset time threshold.

8. The method for speed regulation of a heat pump fan based on capacity and energy efficiency optimization according to claim 1, characterized in that, The current fan speed is determined in each optimization process using the following steps: Obtain the current operating status data of the heat pump equipment; The current operating condition data is input into the trained optimal speed prediction model to obtain the current optimized wind turbine speed output; the optimal speed prediction model is trained using a training dataset that includes multiple historical operating condition data and corresponding optimal speed labels.

9. A heat pump fan speed control system based on capacity and energy efficiency optimization, characterized in that, The system includes: The judgment module is used to determine whether the heat pump equipment has entered a steady-state operating state when the heat pump equipment is working, and to obtain a steady-state judgment result; The startup module is used to initiate repeated optimization of the fan speed of the heat pump equipment when the steady-state judgment result is yes. The calculation module is used to calculate the corresponding capacity parameters and energy efficiency parameters based on the real-time operating data of the heat pump equipment each time the fan speed is changed. The optimization module is used to repeatedly optimize according to the capacity parameters, the energy efficiency parameters and preset numerical judgment rules until the optimal wind turbine speed is determined.

10. A heat pump fan speed control system based on capacity and energy efficiency optimization, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the heat pump fan speed regulation method based on capacity and energy efficiency optimization as described in any one of claims 1-8.

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