Intelligent defrosting control method and system for split air conditioner

Abstract

The invention relates to an intelligent defrosting control method and system for a split air conditioner, and belongs to the technical field of air conditioning equipment. S2, predicting a frosting state based on a model; s3, intelligent decision making and defrosting starting are carried out; and S4, the defrosting process is optimized and accurately terminated. According to the method, various key parameters influencing the frosting process are collected in real time and comprehensively analyzed, the frosting prediction model is constructed, the state of the frost layer and the influence on the system performance are dynamically and accurately evaluated in a quantitative mode, and the effects of accurate starting, timely response and self-adaptive adjustment are achieved in the aspect of air conditioner defrosting control.

Description

Technical Field

[0001] This invention relates to the field of air conditioning equipment technology, and in particular to an intelligent defrosting control method and system for split air conditioners. Background Technology

[0002] In cold and damp winters, split-type air conditioners (hereinafter referred to as "split air conditioners") operating in heat pump mode are important heating devices. Their heating principle involves absorbing heat from the low-temperature outdoor air through a refrigeration cycle and transferring it indoors for release. However, when the surface temperature of the outdoor unit's heat exchanger (acting as an evaporator) is below the air dew point temperature and below 0°C, water vapor in the air condenses and freezes on its surface as frost. Over time, the frost layer thickens, severely obstructing airflow through the heat exchanger's ductwork. Due to its low thermal conductivity (far lower than that of metal fins), it creates significant thermal resistance, leading to a sharp decline in heat exchange efficiency. This directly manifests as a decrease in system heating capacity, increased compressor power consumption, and a deterioration in the coefficient of performance (COP). In severe cases, it can even lead to compressor liquid return and oil dilution due to poor refrigerant circulation.

[0003] To address the aforementioned frosting problem, existing technologies generally employ periodic or condition-triggered defrosting control strategies. Currently, the most widely used method in the industry is the "time-temperature" based defrosting control method. This method primarily monitors the difference (ΔT) between the outdoor heat exchanger coil temperature and the ambient temperature, combined with a fixed cumulative running timer (T) to determine whether to initiate defrosting. Specifically, when ΔT remains below a certain set threshold for more than a preset time T, the control system determines that frosting is severe and initiates a reverse-cycle defrosting procedure.

[0004] A schematic diagram of the heating capacity performance curve of the traditional method is shown below. Figure 1 The diagram illustrates a typical heating-defrosting cycle under this traditional control logic. In the initial heating phase (Phase I), the system operates efficiently. As frost slowly accumulates (Phase II), heat exchange efficiency begins to decline, and the coil temperature decreases, leading to a reduction in ΔT. When the (ΔT&T) condition is reached (point A), the system enters defrosting (Phase III), at which point indoor heating is interrupted. After defrosting completes (point B), heating resumes (Phase IV).

[0005] However, this traditional defrosting control method has inherent and significant drawbacks, stemming from the simplistic and rigid nature of its judgment logic:

[0006] 1. False Defrosting: In low-humidity environments (such as dry, cold weather), the actual amount of frost is very small or even nonexistent. However, due to fixed timing logic or small temperature fluctuations triggering conditions, the system will still execute the defrosting procedure. This leads to unnecessary interruptions in indoor heating (creating a feeling of coldness) and wastes the energy used for defrosting, seriously affecting the user's comfort experience and the system's seasonal energy efficiency.

[0007] 2. Delayed Defrosting: In high humidity environments (such as rain, snow, or smog), frost grows rapidly and thickly. However, judgments based on ΔT and fixed time may be slow to react, initiating defrosting only after the frost layer has reached a thickness that severely impacts heating efficiency (as shown by the solid line in the figure, the actual heating capacity has already significantly decreased before point A). This means that for a considerable period before defrosting is triggered, users are actually experiencing insufficient heating, which contradicts their heating needs.

[0008] 3. Inaccurate defrosting process: The defrosting process usually adopts a fixed duration or a simple temperature termination method. This may lead to two results: First, insufficient defrosting time, resulting in incomplete removal of the frost layer, and the residual frost will accelerate the next frost formation; second, over-defrosting, which not only wastes energy and time, but may also cause the heat exchanger to be overheated, generating unnecessary thermal stress and affecting reliability in the long term.

[0009] Although subsequent improvements have attempted to address these shortcomings, these methods are essentially still based on linear or simple nonlinear logic judgments using only a few physical parameters. They cannot comprehensively and dynamically perceive and quantify the multiple factors affecting the complex phase change process of frosting (such as the coupling effects of temperature, humidity, wind speed, heat exchanger surface characteristics, and system operating status), let alone achieve accurate prediction of frost growth dynamics. Therefore, the defrosting control curves of existing technologies remain unsatisfactory, exhibiting significant bottlenecks in accuracy, adaptability, and intelligence, resulting in considerable room for improvement in the overall energy efficiency and user experience of split air conditioners during winter heating. Summary of the Invention

[0010] To address the aforementioned technical problems, this invention provides an intelligent defrosting control method and system for split air conditioners. This system can accurately predict the frost growth state in real time and, based on this prediction, enable on-demand start-up, process optimization, and precise termination, thereby completely avoiding false defrosting and defrosting delays, maximizing the effective heating time, improving the overall system energy efficiency, and enhancing user heating comfort.

[0011] A method for intelligent defrosting control of split air conditioners includes the following steps:

[0012] Step S1, Real-time acquisition and monitoring of multi-source information: During the heating operation of the air conditioner, multiple sensors deployed in the system are used to collect and monitor multi-source parameters that directly affect or are related to the frosting process in real time.

[0013] Step S2, Frost State Prediction Based on Model: Construct a frost prediction model, establish a nonlinear mapping relationship between multi-source parameters and the current frost severity, input the real-time collected multi-source parameters into the frost prediction model, the model calculates and outputs at least two quantitative evaluation prediction values; the quantitative evaluation prediction values ​​output by the model include the frost layer thickness prediction value F and the heating efficiency attenuation coefficient η.

[0014] Step S3, Intelligent Decision-Making and Defrosting Start: Based on the quantitative evaluation prediction value of the model prediction, a decision threshold is set. During the operation of the equipment, the real-time prediction value output by the model is continuously compared with the set threshold. Based on the comparison result, a control command is generated to start the defrosting program.

[0015] Step S4, Optimization and Precise Termination of Defrosting Process: During the execution of the defrosting program, the frost prediction model continues to calculate the predicted value F of the decaying frost layer thickness based on multi-source parameters; at the same time, it continuously monitors the recovery of the outdoor heat exchanger tube wall temperature Tp; when the predicted value of the frost layer thickness and the tube wall temperature Tp reach the preset conditions, it is determined that defrosting has been completed, a control command is generated, and the defrosting program is terminated.

[0016] Furthermore, the multi-source parameters include environmental parameters, heat exchanger status parameters, system operating parameters, and air-side parameters; the environmental parameters include: outdoor ambient temperature Ta and relative humidity Rh; the heat exchanger status parameters include: the pipe wall temperature TP at key locations of the outdoor unit heat exchanger; the system operating parameters include: key pressures and temperatures of the refrigerant circulation system, the current operating frequency Fc of the compressor, and the cumulative operating time t for this continuous heating operation; the air-side parameters include: outdoor unit fan current or speed.

[0017] Furthermore, the predicted frost thickness is used to characterize the virtual estimate of the average frost thickness on the surface of the outdoor heat exchanger; the heating efficiency attenuation coefficient is used to characterize the degree of attenuation of the actual heating capacity of the current system due to frost formation relative to the heating capacity under ideal frost-free conditions, and its value is between 0 and 1, where 1 represents no attenuation and 0 represents complete failure.

[0018] Furthermore, step S3 specifically involves: setting a first threshold as a frost thickness warning threshold; setting a second threshold as a heating efficiency decay tolerance threshold; continuously comparing the real-time predicted value output by the model with the set threshold during equipment operation; and generating a control command to start the defrosting program when the predicted frost thickness is ≥ the first threshold or the heating efficiency decay coefficient is ≤ the second threshold.

[0019] Furthermore, the settings of the first threshold and the second threshold are dynamically adjusted based on the user-defined operating mode or the received real-time electricity price information.

[0020] Furthermore, the defrosting program adopts an optimized composite defrosting strategy. Specifically, at the initial stage of the defrosting program, a hot gas bypass defrosting method is first used, allowing a portion of the high-temperature refrigerant to flow through the outdoor heat exchanger for initial defrosting, while maintaining the indoor fan at a low speed as much as possible. After the system pressure tends to balance, the program switches to reverse cycle defrosting, that is, the four-way reversing valve is switched to completely convert to the refrigeration cycle for defrosting.

[0021] Furthermore, the preset conditions in step S4 are as follows: a third threshold is set as the frost thickness value indicating that the frost layer has been basically cleared, and a fourth threshold is set as the temperature value indicating that the frost on the heat exchanger surface has melted and the temperature has risen back to a safe level. When the conditions are met simultaneously: the real-time frost thickness prediction value F drops to ≤ the third threshold and the pipe wall temperature Tp rises to ≥ the fourth threshold, it is determined that defrosting has been completed, and a control command is immediately generated to terminate the defrosting program, and the control system switches back to the normal heating operation mode.

[0022] Furthermore, the frost prediction model adopts a machine learning-based regression model, using multi-source parameters from historical operation processes and frost layer data collected synchronously to represent the current frost severity as training data. Through training, the model learns the complex mapping law from multi-source parameters to frost layer state. The trained model is solidified into an algorithm and embedded into the storage unit of the main controller of the outdoor unit of the air conditioner.

[0023] A split-type air conditioner intelligent defrosting control system includes a data acquisition module, a data processing and prediction module, an intelligent decision-making module, and an execution module;

[0024] The data acquisition module consists of various sensors and signal acquisition circuits, and is responsible for acquiring multi-source parameters in real time.

[0025] The data processing and prediction module is integrated into the main controller and includes the algorithm program of the frost prediction model. It is used to receive signals from the data acquisition module and perform necessary filtering preprocessing, and input the signal data into the frost prediction model for calculation, and output the predicted value of frost layer thickness F and the heating efficiency attenuation coefficient η in real time.

[0026] The intelligent decision-making module is also integrated into the main controller. It receives the output of the data processing and prediction module, compares it with the stored threshold, and generates control instructions for the defrosting program based on the comparison results.

[0027] The execution module includes various actuators of the air conditioning system, specifically including: a four-way reversing valve, an electronic expansion valve, a bypass solenoid valve, a compressor drive circuit, and a fan drive circuit; the execution module receives control commands from the intelligent decision module and drives the corresponding components to perform specific defrosting operations.

[0028] Furthermore, the system also includes a user interaction module, which includes a remote control and a mobile APP interface, for receiving mode setting instructions from the user and transmitting them to the intelligent decision-making module to achieve dynamic adjustment of the threshold.

[0029] The beneficial effects of this invention are as follows: This invention collects and comprehensively analyzes multiple key parameters affecting the frosting process in real time, constructs a frosting prediction model, integrates multi-dimensional conventional sensor signals to dynamically and accurately quantify and evaluate the state of the frost layer and its impact on system performance, and initiates defrosting only when the frost reaches the threshold that truly affects efficiency, eliminating ineffective defrosting cycles in frost-free or micro-frost states; it can provide early warning and initiate defrosting in cases of rapid frost growth, avoiding severe attenuation of heating capacity over a long period due to excessive frost thickness; based on the predicted frost amount and system status, it intelligently selects or combines defrosting modes, and terminates defrosting when the frost layer is just cleared, avoiding energy waste and equipment thermal shock. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the heating capacity performance curve of the traditional method.

[0031] Figure 2 This is a flowchart of the method of the present invention.

[0032] Figure 3 This is a system architecture diagram of the present invention.

[0033] Figure 4 This is a schematic diagram of the system operation principle of the present invention.

[0034] Figure 5 This is a schematic diagram comparing the heat output over time in a typical heating cycle with that of the conventional method and the defrosting control method of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0036] The core of this technical solution lies in abandoning the rigid control logic that relies on a single or a few physical parameter thresholds for judgment in existing technologies. Instead, it introduces a frost prediction model based on multi-source information input. The quantitative indicators output by the model, which reflect the state of the frost layer and the degradation of system performance, serve as the sole or primary basis for defrosting decisions, thereby achieving precise and adaptive on-demand defrosting.

[0037] Reference Figure 2As shown in the figure, this application provides an intelligent defrosting control method for split air conditioners, including the following steps: S1 real-time acquisition and monitoring of multi-source information, S2 model-based prediction of frost state, S3 intelligent decision-making and defrosting initiation, and S4 optimization and precise termination of the defrosting process. The specific details of each step are as follows.

[0038] Step S1: Real-time acquisition and monitoring of multi-source information.

[0039] During air conditioning heating operation, multiple sensors deployed in the system synchronously collect and monitor multi-source parameters that directly affect or are related to the frosting process in real time. These parameters collectively constitute the input feature set for frosting prediction, including but not limited to:

[0040] Environmental parameters: outdoor ambient temperature Ta and relative humidity Rh; these are the most fundamental factors affecting the frost formation rate and the final amount of frost.

[0041] Heat exchanger status parameters: the tube wall temperature TP at key locations of the outdoor unit's heat exchanger, especially in areas prone to frost formation such as bends. This temperature directly reflects the heat exchange status under frost cover and evaporation pressure.

[0042] System operating parameters: key pressures and temperatures of the refrigerant circulation system (such as compressor discharge pressure / temperature, suction pressure / temperature), the current operating frequency of the compressor Fc, and the cumulative operating time t of this continuous heating; these parameters comprehensively reflect the real-time changes in the system load, refrigerant flow rate, and heat exchange efficiency.

[0043] Wind-side parameters include the outdoor unit fan current or speed; they can indirectly reflect changes in wind resistance caused by the thickening of frost.

[0044] Step S2, predict the frosting state based on the model.

[0045] A frosting prediction model is constructed, which establishes a nonlinear mapping relationship between multi-source parameters and the current frosting severity. Real-time collected multi-source parameters are input into the frosting prediction model, which calculates and outputs at least two quantitative assessment prediction values: 1. Frost thickness prediction value F (a virtual estimate representing the average frost thickness on the surface of the outdoor heat exchanger, in millimeters (mm). This value is not a direct physical measurement, but rather an equivalent thickness calculated by the model based on the input parameters); 2. Heating efficiency attenuation coefficient η (a coefficient representing the degree of attenuation of the actual heating capacity of the current system due to frosting relative to the ideal frost-free state, with a value between 0 and 1 (1 representing no attenuation, 0 representing complete failure)).

[0046] Step S3: Intelligent decision-making and defrosting initiation.

[0047] A decision threshold is set based on the quantitative evaluation prediction value of the model. During equipment operation, the real-time predicted value output by the model is continuously compared with the set threshold. Based on the comparison result, a control command is generated to start the defrosting program; specifically:

[0048] Set a first threshold (F_th1) as the frost thickness warning threshold;

[0049] A second threshold (η_th1) is set as the tolerance threshold for the decline in heating efficiency;

[0050] During equipment operation, the real-time predicted value (F,η) output by the model is continuously compared with the set threshold. When the predicted value of frost thickness is greater than or equal to the first threshold or the heating efficiency attenuation coefficient is less than or equal to the second threshold, the intelligent decision module determines that the current frost state has an unacceptable impact on the system performance and then generates a control command to start the defrosting program.

[0051] Step S4: Optimization and precise termination of the defrosting process.

[0052] During the defrosting process, the predictive model does not stop working but continues to calculate the decaying frost thickness prediction value (F) based on the operating parameters (whose characteristics have now changed). Simultaneously, it continuously monitors the recovery of the outdoor heat exchanger tube wall temperature Tp. When the frost thickness prediction value and tube wall temperature Tp reach preset conditions, defrosting is deemed complete, a control command is generated, and the defrosting process is terminated. The preset conditions are as follows: a third threshold (F_th2) is set as the frost thickness value indicating that the frost has been largely cleared; a fourth threshold (i.e., the preset tube wall temperature Tp_th) is set as the temperature value indicating that the frost on the heat exchanger surface has melted and the temperature has risen to a safe level. When both conditions are met—the real-time frost thickness prediction value F decreasing to ≤ the third threshold and the tube wall temperature Tp rising to ≥ the fourth threshold—the intelligent decision module determines that defrosting is complete, immediately generates a control command to terminate the defrosting process, and the control system switches back to normal heating operation mode.

[0053] In another possible embodiment, frost condition prediction is preferentially based on data-driven models using machine learning, such as regression models constructed using algorithms like linear regression, support vector machines (SVR), random forests, or lightweight neural networks. These models are well-suited for handling multivariate, nonlinear mapping problems.

[0054] Model Training: The model needs to be trained offline before it can be used. The training data comes from historical operating data, including: a large amount of the aforementioned multi-source parameters (as input features) covering different climatic conditions and operating conditions, and frost layer data collected simultaneously through other reliable methods (such as a high-precision image recognition system used in the experimental phase or a very small number of weight sensors) (as training labels, such as actual frost layer thickness or performance degradation rate). Through training, the model learns the complex mapping law from multi-source operating parameters to frost layer state.

[0055] Model Deployment: The trained model is solidified into an algorithm and embedded into the storage unit of the main controller (MCU) of the outdoor unit of the air conditioner. During actual operation, the controller calls the model to perform forward inference calculations. By predicting the frosting state, a leap from "simple physical threshold judgment" to "intelligent assessment of complex states" is achieved.

[0056] In another possible embodiment, the set thresholds can be dynamically adjusted. For example, the first and second thresholds can be dynamically adjusted based on the user-defined operating mode (e.g., in the "comfort priority" mode, which requires more stable heating, F_th1 is automatically lowered and η_th1 is raised to make defrosting more frequent; conversely, in the "energy saving priority" mode, the opposite adjustment is made) or received real-time electricity price information. This achieves an optimal balance between comfort, energy efficiency, and operating costs, allowing the defrosting strategy to proactively adapt to different user needs and external conditions.

[0057] In another possible embodiment, the defrosting procedure employs an optimized composite defrosting strategy. For example, initially, hot gas bypass defrosting is used (by controlling the bypass valve to open and directly draw some high-temperature refrigerant to the outdoor heat exchanger), during which the indoor fan can run at low speed to maintain some heat output. After the system pressure tends to balance, it switches to reverse cycle defrosting (by switching the four-way reversing valve to completely switch to a refrigeration cycle for defrosting). This composite mode can reduce indoor temperature fluctuations and improve defrosting speed. The following is an example of the composite defrosting strategy in actual implementation. When the intelligent decision module issues the "start defrosting" command, the execution module operates according to the following refined steps:

[0058] Phase 1: Hot gas bypass defrosting (approximately 30-60 seconds). Keep the four-way reversing valve in heating mode; open the bypass solenoid valve connecting the compressor discharge pipe and the outdoor heat exchanger inlet; some of the high-temperature, high-pressure refrigerant gas bypasses the indoor heat exchanger and is directly injected into the outdoor heat exchanger after being throttled through the bypass pipe (it can be injected through a capillary tube with a fixed orifice) to begin heating and defrosting it.

[0059] During this stage, the indoor fan switches to low speed or intermittent operation. Because the main system is still in heating cycle, with only some refrigerant diverted, the indoor unit can still blow out warm air, greatly alleviating the problem of cold air immediately blowing out when traditional reverse cycle defrosting begins.

[0060] Phase 2: Reverse Circulation Defrosting. Close the bypass solenoid valve; switch the four-way reversing valve, and the system fully enters the standard reverse circulation defrosting mode, using the indoor heat exchanger (which acts as an evaporator at this time) to absorb indoor heat (the indoor fan should stop at this time) to quickly melt the outdoor frost layer; subsequent process monitoring and termination decisions are the same as the defrosting termination process described above.

[0061] The composite defrosting strategy achieves the following: 1. Improved comfort: The first stage effectively buffers the impact of the initial defrosting on the indoor thermal environment, and users can hardly perceive the beginning of the "cold feeling".

[0062] 2. Improved defrosting efficiency and safety: Bypass hot air can quickly raise the temperature of the outdoor heat exchanger, laying a good foundation for subsequent reverse circulation defrosting and potentially shortening the total defrosting time. At the same time, it avoids the drastic pressure fluctuations that may occur when the system switches directly from heating to cooling.

[0063] Reference Figure 3 As shown in the figure, this application embodiment also provides a split air conditioner intelligent defrosting control system, including:

[0064] The data acquisition module consists of various sensors (temperature and humidity sensors, pipe wall temperature sensors, pressure sensors, etc.) and signal acquisition circuits, and is responsible for acquiring multi-source parameters in real time.

[0065] The data processing and prediction module is integrated into the main controller. It contains the algorithm program of the frost prediction model. It is used to receive signals from the data acquisition module and perform necessary filtering preprocessing. It inputs the signal data into the frost prediction model for calculation and outputs the predicted value of frost layer thickness F and the heating efficiency attenuation coefficient η in real time.

[0066] The intelligent decision-making module is also integrated into the main controller. It receives the output of the data processing and prediction module, compares it with the stored threshold, and generates control instructions for the defrosting program based on the comparison results.

[0067] The execution module includes various actuators of the air conditioning system, specifically including: a four-way reversing valve, an electronic expansion valve, a bypass solenoid valve, a compressor drive circuit, and a fan drive circuit; the execution module receives control commands from the intelligent decision module and drives the corresponding components to perform specific defrosting operations.

[0068] To facilitate user operation, the system also includes a user interaction module, which includes a remote control and a mobile APP interface. This module receives mode setting commands from the user and transmits them to the intelligent decision-making module to achieve dynamic adjustment of the threshold.

[0069] like Figure 4 As shown, the overall operation process of the above-mentioned defrosting control system is as follows:

[0070] 1. Start / Heating Operation (Step 101): The system starts up and enters heating mode.

[0071] 2. Real-time acquisition of multi-source information (step 102): Parallel acquisition of parameters such as outdoor ambient temperature (Ta), humidity (Rh), heat exchanger tube wall temperature (Tp), system pressure (P), compressor frequency (Fc), and running time (t). The current compressor operating frequency Fc and the cumulative running time t for this continuous heating can be directly read from the drive circuit. System pressure includes discharge pressure Pd and suction pressure Ps.

[0072] 3. Frost Prediction Model Calculation (Step 103): Input the parameters collected in Step 102 into the preset frost prediction model, calculate and output the predicted frost thickness (F) and heating efficiency attenuation coefficient (η) in real time. Before inputting into the model, the collected data is preprocessed by filtering and normalization, and then packaged into a feature vector [Ta, Rh, Tp, Pd, Ps, Td, Ts, Fc, t], which is input into the frost prediction model in the data processing and prediction module embedded in the main controller every 10 seconds. Here, we take a lightweight random forest regression model trained offline as an example. This model is tested by running the air conditioner for a long time in a laboratory environment chamber under various temperature and humidity combinations (such as Ta: -10℃~7℃, Rh: 60%~95%). The multi-source parameters (including Tp, Rh, Tp, Pd, Ps, Td, Ts, Fc, t) are recorded synchronously to form the input feature vector set. Simultaneously, a high-resolution infrared thermal imager was used to continuously capture images of the outdoor heat exchanger fin surface. Image processing algorithms (e.g., grayscale thresholding and morphological operations) were then used to analyze the acquired thermal images, identifying frost-covered areas. Pixel calibration techniques were then used to calculate the actual average frost thickness (in mm) on the heat exchanger surface, which served as an accurate training label corresponding to each set of input feature vectors. Through supervised training on a large amount of such paired data (input feature vector - actual frost thickness), the model learned a complex nonlinear mapping relationship from multidimensional operating parameters to frost thickness. After receiving real-time feature vectors, the model performed forward inference and output two key values:

[0073] 1) Predicted frost layer thickness F: A floating-point number within the range of 0 to 5 mm, representing the current average frost layer thickness estimated by the model. For example, the output is F = 1.8 mm.

[0074] 2) Heating efficiency decay coefficient η: A floating-point number within the range of 0 to 1, where 1 represents no decay. This value can be directly output by the model or obtained through an empirical formula (such as η = 1 / (1 + k*F), where k is a constant related to the heat exchanger structure) based on the output F value. For example, the output is η = 0.78.

[0075] 4. Decision-making judgment: Whether defrosting is required? (Step 104): Compare the F value output by the model with the preset threshold F_th1 and the η value with the preset threshold η_th1. The judgment logic is: If F ≥ F_th1 or η ≤ η_th1, it is determined that defrosting is required and proceed to step 105; otherwise, return to step 102 to continue monitoring. In this embodiment, fixed decision thresholds are pre-stored in the intelligent decision-making module: The first threshold (frost layer thickness start threshold) F_th1 = 2.5 mm; the second threshold (efficiency decay start threshold) η_th1 = 0.70.

[0076] The decision-making module continuously compares the model output values with the thresholds:

[0077] Situation A: In dry and cold weather (Ta = -5°C, Rh = 65%), the model continuously outputs F = 0.8 mm and η = 0.92. Since F < F_th1 and η > η_th1, it is determined that defrosting is not required and the system continues to heat;

[0078] Situation B: In wet and cold weather (Ta = 2°C, Rh = 90%), after running for a period of time, the model outputs F = 2.6 mm and η = 0.68. At this time, F ≥ F_th1 is established, triggering the defrost start condition;

[0079] Once the start condition is met, the intelligent decision-making module immediately sends a "start defrosting" instruction to the execution module.

[0080] 5. Start the defrosting program (Step 105): Send a control instruction to start the defrosting operation. After receiving the instruction, the execution module performs a standard reverse cycle defrosting according to the preset program:

[0081] 1) Turn off the outdoor fan.

[0082] 2) Switch the four-way reversing valve to make the system switch to a refrigeration cycle (at this time, the indoor heat exchanger becomes an evaporator to absorb heat).

[0083] 3) Adjust the electronic expansion valve to the defrosting opening degree.

[0084] This step may include a composite mode, for example, first perform hot gas bypass defrosting and then switch to reverse cycle defrosting.

[0085] 6. Defrosting process monitoring (step 106): During defrosting, the model continues to run (the input parameters have changed), continuously calculates the F value during decay, and monitors the pipe wall temperature Tp.

[0086] 7. Decision Judgment: Is defrosting complete? (Step 107): Compare the real-time F value with the preset defrosting threshold F_th2, and simultaneously determine whether the pipe wall temperature Tp has risen to the preset temperature Tp_th. If both F≤F_th2 and Tp≥Tp_th are satisfied, then defrosting is determined to be complete, and proceed to step 108; otherwise, return to step 106 to continue defrosting.

[0087] 8. Exit defrosting and resume heating (step 108): Stop defrosting operation, switch the control system back to normal heating mode, return the process to step 101, and start a new cycle.

[0088] 9. (Optional) Dynamic threshold adjustment (dashed box 109): Receive user mode instructions (such as "comfort priority") or external information (such as real-time electricity price) through the user interaction module, and dynamically adjust the decision thresholds (F_th1, η_th1, F_th2, etc.) accordingly. The output of the decision thresholds is applied to the judgment logic in steps 104 and 107.

[0089] To verify the effectiveness of the implementation scheme of this application, such as Figure 5 As shown in the comparison curves, the performance advantages of this application compared to traditional methods are intuitively demonstrated. The horizontal axis represents time. The vertical axis represents the system's heating capacity, which can be understood as the amount of heat supplied to the room.

[0090] exist Figure 5 In the middle, the performance curve of the traditional method (solid line):

[0091] Phase I (High-efficiency heating period): Heating capacity remains at a high level.

[0092] Point A (defrosting trigger point for traditional methods): Based on the fixed time-temperature method, defrosting is triggered only after the heating capacity has significantly decreased due to the accumulation of frost.

[0093] Phase II (Severe Capacity Decline Period): The period from when the frost layer begins to affect the point A to trigger defrosting is relatively long, and users continuously experience insufficient heating.

[0094] Phase III (Defrosting Period): The system enters the defrosting phase, the heating capacity drops to zero, indoor heating stops, and users feel a noticeable "coldness".

[0095] Point B (End point of defrosting using traditional methods): Defrosting may end due to a fixed duration.

[0096] Phase IV (Recovery Period): Resumption of heating.

[0097] exist Figure 5 In the middle, the performance curve of the method in this application (dashed line):

[0098] Point A' (Defrosting trigger point of this application): Based on model prediction, defrosting is triggered in a timely manner when the heating efficiency just begins to decline and the effect of frost layer first appears (far earlier point A).

[0099] Therefore, its capacity decay period (Phase II') is shorter, the decay is smaller, and the heating fluctuations felt by users are greatly reduced.

[0100] Stage III' (Defrosting period of this application): Due to timely activation and a thinner frost layer, the defrosting time may be shorter.

[0101] Point B' (Defrosting End Point of this Invention): Based on both model and temperature judgments, defrosting is precisely ended, potentially avoiding overheating.

[0102] In summary, the area under the curve (representing total heat supply) is significantly larger than that of the traditional method throughout the entire cycle, and the heating interruption time is shorter and the fluctuation is more gradual, thus reflecting a comprehensive improvement in comfort and energy efficiency.

[0103] In the description of embodiments of the present invention, the terms "first," "second," "third," and "fourth" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," "third," or "fourth" may explicitly or implicitly include one or more of that feature. In the description of the present invention, unless otherwise stated, "a plurality of" means two or more.

[0104] In the description of embodiments of the present invention, the term "and / or" is used only to describe the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " generally indicates that the preceding and following associated objects are in an "or" relationship.

[0105] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent defrosting control of split-type air conditioners, characterized in that, Includes the following steps: Step S1, Real-time acquisition and monitoring of multi-source information: During the heating operation of the air conditioner, multiple sensors deployed in the system are used to collect and monitor multi-source parameters that directly affect or are related to the frosting process in real time. Step S2, Frost State Prediction Based on Model: Construct a frost prediction model, establish a nonlinear mapping relationship between multi-source parameters and the current frost severity, input the real-time collected multi-source parameters into the frost prediction model, the model calculates and outputs at least two quantitative evaluation prediction values; the quantitative evaluation prediction values ​​output by the model include the frost layer thickness prediction value F and the heating efficiency attenuation coefficient η. Step S3, Intelligent Decision-Making and Defrosting Start: Based on the quantitative evaluation prediction value of the model prediction, a decision threshold is set. During the operation of the equipment, the real-time prediction value output by the model is continuously compared with the set threshold. Based on the comparison result, a control command is generated to start the defrosting program. Step S4, Optimization and Precise Termination of Defrosting Process: During the execution of the defrosting program, the frost prediction model continues to calculate the predicted value F of the decaying frost layer thickness based on multi-source parameters; at the same time, it continuously monitors the recovery of the outdoor heat exchanger tube wall temperature Tp; when the predicted value of the frost layer thickness and the tube wall temperature Tp reach the preset conditions, it is determined that defrosting has been completed, a control command is generated, and the defrosting program is terminated.

2. The intelligent defrosting control method for split-type air conditioners according to claim 1, characterized in that, The multi-source parameters include environmental parameters, heat exchanger status parameters, system operating parameters, and air-side parameters; The environmental parameters include: outdoor ambient temperature Ta and relative humidity Rh; the heat exchanger status parameters include: the pipe wall temperature TP at key locations of the outdoor unit heat exchanger; the system operating parameters include the key pressure and temperature of the refrigerant circulation system, the current operating frequency Fc of the compressor, and the cumulative operating time t for this continuous heating; the air-side parameters include the outdoor unit fan current or speed.

3. The intelligent defrosting control method for split-type air conditioners according to claim 1, characterized in that, The predicted frost thickness is used to characterize the virtual estimate of the average frost thickness on the surface of the outdoor heat exchanger; the heating efficiency attenuation coefficient is used to characterize the degree of attenuation of the actual heating capacity of the current system due to frost formation relative to the heating capacity under ideal frost-free conditions, and its value is between 0 and 1, where 1 represents no attenuation and 0 represents complete failure.

4. The intelligent defrosting control method for split-type air conditioners according to claim 1, characterized in that, Step S3 specifically involves: setting a first threshold as a frost thickness warning threshold; setting a second threshold as a heating efficiency decay tolerance threshold; continuously comparing the real-time predicted value output by the model with the set threshold during equipment operation; and generating a control command to start the defrosting program when the predicted frost thickness is ≥ the first threshold or the heating efficiency decay coefficient is ≤ the second threshold.

5. The intelligent defrosting control method for split-type air conditioners according to claim 4, characterized in that, The first and second thresholds are dynamically adjusted based on the user-defined operating mode or the received real-time electricity price information.

6. The intelligent defrosting control method for split air conditioners according to claim 1, characterized in that, The defrosting program employs an optimized composite defrosting strategy. Specifically, at the initial stage of the defrosting program, a hot gas bypass defrosting method is used first, allowing a portion of the high-temperature refrigerant to flow through the outdoor heat exchanger for initial defrosting, while maintaining the indoor fan at a low speed as much as possible. Once the system pressure has reached equilibrium, the program switches to reverse cycle defrosting, that is, the four-way reversing valve is switched to fully convert to a refrigeration cycle for defrosting.

7. The intelligent defrosting control method for split-type air conditioners according to claim 1, characterized in that, The preset conditions in step S4 are as follows: a third threshold is set as the frost thickness value indicating that the frost layer has been basically cleared, and a fourth threshold is set as the temperature value indicating that the frost on the heat exchanger surface has melted and the temperature has risen to a safe level. When the conditions are met simultaneously: the real-time frost thickness prediction value F drops to ≤ the third threshold and the pipe wall temperature Tp rises to ≥ the fourth threshold, it is determined that defrosting has been completed, and a control command is immediately generated to terminate the defrosting program and the control system switches back to the normal heating operation mode.

8. The intelligent defrosting control method for split air conditioners according to claim 1, characterized in that, The frost prediction model adopts a regression model based on machine learning. It uses multi-source parameters from historical operation processes and frost layer data collected synchronously to represent the current frost severity as training data. Through training the model, it learns the complex mapping law from multi-source parameters to frost layer state. The trained model is solidified into an algorithm and embedded into the storage unit of the main controller of the outdoor unit of the air conditioner.

9. A split-type air conditioner intelligent defrosting control system, employing the method described in any one of claims 1 to 8, characterized in that, It includes a data acquisition module, a data processing and prediction module, an intelligent decision-making module, and an execution module; The data acquisition module consists of various sensors and signal acquisition circuits, and is responsible for acquiring multi-source parameters in real time. The data processing and prediction module is integrated into the main controller and includes the algorithm program of the frost prediction model. It is used to receive signals from the data acquisition module and perform necessary filtering preprocessing, and input the signal data into the frost prediction model for calculation, and output the predicted value of frost layer thickness F and the heating efficiency attenuation coefficient η in real time. The intelligent decision-making module is also integrated into the main controller. It receives the output of the data processing and prediction module, compares it with the stored threshold, and generates control instructions for the defrosting program based on the comparison results. The execution module includes various actuators of the air conditioning system, specifically including: a four-way reversing valve, an electronic expansion valve, a bypass solenoid valve, a compressor drive circuit, and a fan drive circuit; The execution module receives control commands from the intelligent decision module and drives the corresponding components to perform specific defrosting operations.

10. A split-type air conditioner intelligent defrosting control system according to claim 9, characterized in that, The system also includes a user interaction module, which includes a remote control and a mobile APP interface, for receiving mode setting instructions from users and transmitting them to the intelligent decision-making module to achieve dynamic adjustment of thresholds.

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