Air source heat pump variable parameter defrosting control method based on visual recognition technology

Abstract

The application relates to the technical field of heat pump control, and discloses an air source heat pump variable parameter defrosting control method based on visual recognition technology, which comprises the following steps: acquiring an outdoor heat exchanger image and temperature data; extracting macroscopic coverage representing a frost accumulation area and microscopic characteristic parameters representing physical properties of a frost layer; calculating a thermal attenuation index, determining a correction factor based on the deviation of the index and the macroscopic coverage, and calculating a target defrosting heat load in combination with the microscopic characteristic parameters; determining a target operating frequency of a variable frequency compressor and a target opening degree of an electronic expansion valve according to the target defrosting heat load, and executing defrosting; and monitoring the macroscopic coverage and the coil temperature in real time, and exiting defrosting when the visual readiness and the thermal readiness conditions are simultaneously met. The application realizes accurate prediction of defrosting heat load and on-demand defrosting by introducing a correction factor to compensate for a visual blind area, and solves the problems of incomplete defrosting and low energy efficiency.

Description

Technical Field

[0001] This invention relates to the field of heat pump control technology, and in particular to a variable parameter defrosting control method for air source heat pumps based on visual recognition technology. Background Technology

[0002] When air-source heat pump units operate in low-temperature and high-humidity environments, frost easily accumulates on the surface of the outdoor heat exchanger, leading to increased heat exchange resistance and air-side resistance, which in turn reduces the unit's heating performance. Existing defrosting control strategies mainly rely on temperature sensors, pressure sensors, and timers to determine the timing of defrosting by monitoring coil temperature, ambient temperature duration, or changes in refrigerant high pressure. This logic, based on indirect thermal parameters, struggles to accurately distinguish between dust and dirt clogging on the heat exchanger surface and actual frost buildup. Furthermore, it fails to detect the uniformity of frost distribution on the heat exchanger surface, easily resulting in delayed defrosting or false "defrosting without frost," causing energy waste and indoor heating interruptions.

[0003] With the application of image processing technology, defrosting control solutions based on machine vision have gradually emerged. These solutions typically utilize image acquisition equipment to obtain images of the heat exchanger surface and calculate the area ratio of the frosted region using image segmentation algorithms. However, existing visual defrosting technologies have limitations. Visual sensors can only acquire image information of the outer surface of the heat exchanger, and due to limitations in the field of view and obstruction, they cannot directly observe deep frost or ice blockage inside the heat exchanger fins. In special operating conditions where the surface frost layer is thin but severe ice blockage has formed internally due to airflow disturbances, relying solely on surface image data can lead to incorrect defrosting initiation judgments. Furthermore, existing technologies typically use only the frost area as a single evaluation indicator, ignoring the influence of the frost layer's microstructure (such as density and crystal morphology) on the actual defrosting heat. Under the same coverage area, the latent heat of melting required for loose needle-like frost differs significantly from that required for a dense ice shell. If the defrosting time is set solely based on area, it will lead to a mismatch between the defrosting energy supply and actual demand. In addition, existing defrosting actuators mostly adopt a fixed frequency or fixed opening control mode, lacking a variable parameter adjustment mechanism that dynamically adjusts the compressor frequency and electronic expansion valve opening according to the real-time frost load characteristics, making it difficult to balance defrosting speed and energy efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a variable parameter defrosting control method for air source heat pumps based on visual recognition technology, which solves the problems of delayed defrosting start-up, mismatch between defrosting heat supply and actual frost accumulation, and incomplete defrosting under complex environmental conditions.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A variable-parameter defrosting control method for air-source heat pumps based on visual recognition technology is applied to air-source heat pump units that include an outdoor heat exchanger, an image acquisition unit, a thermal sensing unit, a variable-frequency compressor, and an electronic expansion valve. The method first performs a data acquisition step to obtain surface image data of the outdoor heat exchanger, and simultaneously acquires outdoor ambient temperature and coil temperature values.

[0007] After acquiring the data, feature extraction and analysis are performed. Image processing algorithms are used to analyze the surface image data, outputting two-dimensional feature parameters: one is the macroscopic coverage rate, which characterizes the area ratio of the frost-covered region, and the other is the microscopic feature parameter, which characterizes the physical properties of the frost layer.

[0008] Based on the acquired multidimensional data, heat load calculation is performed. First, the thermal attenuation index, which characterizes the degree of heat exchanger performance degradation in the outdoor heat exchanger, is calculated. Then, the numerical deviation between this thermal attenuation index and the aforementioned macroscopic coverage rate is calculated. Based on this numerical deviation, a correction factor is determined, which is used to characterize the degree of frost formation in the visual blind spot. Finally, the target defrosting heat load is calculated by combining microscopic characteristic parameters and the correction factor.

[0009] Based on the calculated target defrosting heat load, control commands are generated for the actuators. The target operating frequency of the variable frequency compressor and the target opening degree of the electronic expansion valve are determined, driving the unit to perform defrosting operation. During defrosting operation, the system monitors the macroscopic coverage and coil temperature values ​​in real time. When both visual and thermal readiness conditions are met, the unit exits defrosting mode.

[0010] Furthermore, to ensure the spatiotemporal consistency of multi-source data, the data acquisition process includes timing synchronization verification. The system records the moment when the image acquisition unit completes image acquisition and the moment when the thermal sensing unit completes temperature sampling, and calculates the absolute value of the time difference between the two moments. Only when the absolute value of this time difference is less than or equal to a preset time delay deviation threshold is the data of the current group determined to be valid and used for subsequent calculations; otherwise, the data is discarded to avoid calculation errors caused by timing misalignment.

[0011] Before the image data enters the feature extraction process, a visual fault detection step is set up. The image information entropy of the surface image data and the current actual heat exchange temperature difference are calculated. When the image information entropy is lower than the preset blur lower limit and the actual heat exchange temperature difference is lower than the preset effective temperature difference threshold, it is determined that the image acquisition unit has a fault such as lens obstruction or blurring. At this time, the use of the current frame image data is blocked to prevent erroneous visual information from interfering with the control logic.

[0012] For the specific implementation of feature extraction, a deep neural network algorithm is adopted. The macroscopic coverage rate is obtained by performing pixel-level semantic segmentation on the surface image data and statistically analyzing the proportion of pixels identified as frost layers within the region of interest. The microscopic feature parameter is the texture density coefficient obtained by the texture analysis algorithm. This coefficient is used to quantify the physical morphology of the frost layer from loose needle-like crystals to a dense ice shell, and its value trend is positively correlated with the physical density of the frost layer.

[0013] Regarding the logic for determining the correction factor, this invention identifies internal frost by comparing the differences between thermal performance and visual perception. Specifically, the thermal performance degradation index is normalized, and the difference between the normalized thermal performance degradation index and the macroscopic coverage rate is calculated. When this difference exceeds a preset deviation tolerance threshold, it indicates that the decrease in heat exchange performance is greater than the visually visible frost extent, suggesting a large amount of frost inside the heat exchanger fins. At this point, the correction factor is increased using a nonlinear correction model. The role of this correction factor is to compensate for the additional heat demand caused by the visually invisible internal frost of the heat exchanger.

[0014] The equivalent mass method is used to calculate the target defrosting heat load. First, the equivalent frost density is obtained by mapping microscopic characteristic parameters. Then, the equivalent frost mass is obtained by multiplying the total surface area of ​​the outdoor heat exchanger, the preset baseline thickness, the macroscopic coverage, the equivalent frost density, and the correction factor. Finally, the final target defrosting heat load is calculated by multiplying the equivalent frost mass by the latent heat of phase change of ice, and adding the heat capacity required to raise the temperature of the outdoor heat exchanger's metal body.

[0015] Regarding the variable parameter control of the actuators, the target operating frequency of the variable frequency compressor is determined using a combination of feedforward and feedback methods. The base frequency is determined based on the ratio of the target defrosting heat load to the preset expected defrosting time. During defrosting, the rate of increase of the high-pressure side pressure is monitored in real time, and the frequency correction is calculated based on the deviation between the actual rate and the target rate. The base frequency and the frequency correction are then superimposed to obtain the final target operating frequency. The target opening of the electronic expansion valve also employs composite control. The base opening is determined through linear mapping based on the target operating frequency of the variable frequency compressor, and the compressor suction superheat is monitored in real time. The opening correction is calculated based on the superheat deviation, and the base opening and the opening correction are then superimposed to obtain the final target opening.

[0016] Regarding the defrost exit mechanism, this invention employs dual-confirmation logic. The visual readiness condition is set when the sliding average of the macroscopic coverage is below a preset residual threshold to ensure thorough removal of surface frost. The thermal readiness condition is set when the coil temperature exceeds a preset exit threshold to ensure the overall heat exchanger temperature recovers. The control logic requires that the defrost exit operation be executed only when both the visual and thermal readiness conditions are met simultaneously, or when the defrost operation time reaches the mandatory protection time limit, thus balancing defrosting thoroughness with system safety.

[0017] In addition, after exiting defrost mode, an adaptive water-spinning step is executed. The system determines the water-spinning speed and running time of the outdoor unit fan based on the target defrost heat load calculated above. The larger the target defrost heat load, the more water is generated by melting, so the set water-spinning speed is higher and the running time is longer, in order to use centrifugal force to remove residual moisture from the surface of the heat exchanger and prevent secondary icing.

[0018] In summary, the present invention has at least one of the following beneficial technical effects:

[0019] 1. This invention introduces a correction factor by calculating the numerical deviation between the thermal attenuation index and the macroscopic coverage rate, enabling the identification of frost formation inside the heat exchanger fins that cannot be directly observed by the image acquisition unit. This correction factor effectively compensates for the blind spots inherent in relying solely on visual recognition, making the calculated target defrosting heat load closer to the actual total frost accumulation. This avoids insufficient defrosting energy due to missed detection of internal frost layers and solves the control failure problem when there is ice blockage inside the heat exchanger but no frost on the surface under harsh operating conditions.

[0020] 2. Based on the calculated target defrosting heat load and microscopic characteristic parameters, this invention dynamically adjusts the operating frequency of the variable frequency compressor and the opening of the electronic expansion valve. This variable parameter control strategy ensures that the heat supply during the defrosting process matches the actual frost melting demand, avoiding energy waste caused by fixed high-frequency operation during slight frost formation, while also guaranteeing defrosting capability under dense frost conditions, thereby improving the unit's energy efficiency ratio during the defrosting cycle.

[0021] 3. This invention employs a dual exit mechanism combining visual readiness and thermal readiness. Defrosting only ends when the surface frost layer disappears and the coil temperature rises back to a safe threshold. This logic effectively prevents premature termination of defrosting due to uneven heating of local temperature sensors, solving the problem of incomplete defrosting residue. Combined with the adaptive high-speed water-spinning step after defrosting, it further removes melted water from the heat exchanger surface, reducing the risk of secondary icing and delaying the arrival of the next frosting cycle. Attached Figure Description

[0022] Figure 1This is a schematic diagram of the system hardware architecture of the present invention;

[0023] Figure 2 This is an overall flowchart of the defrosting control method of the present invention;

[0024] Figure 3 This is a diagram of the dual-channel deep convolutional neural network architecture of the present invention;

[0025] Figure 4 This is a framework diagram of the variable parameter control strategy of the present invention;

[0026] Figure 5 This is a comparison diagram of the defrosting effect of the present invention;

[0027] Figure 6 This is a schematic diagram of the AI ​​feature partitioning of the present invention.

[0028] Among them, 10 is the outdoor heat exchanger; 20 is the image acquisition unit; 30 is the thermal sensing unit; 31 is the outdoor ambient temperature sensor; 32 is the coil temperature sensor; 40 is the control processing unit; 41 is the AI ​​edge computing module; 42 is the main logic control module; 43 is the defrost decision control module; 44 is the execution unit control module; 50 is the variable frequency compressor; 60 is the electronic expansion valve; and 70 is the four-way reversing valve. Detailed Implementation

[0029] The following is in conjunction with the appendix Figure 1 - Appendix Figure 6 The present invention will be further described in detail below.

[0030] This invention provides a variable parameter defrosting control method for air source heat pumps based on visual recognition technology. This method is applied to air source heat pump units that include variable frequency actuators. (Reference) Figure 1 As shown, the hardware system for executing this method includes: an outdoor heat exchanger 10, an image acquisition unit 20, a thermal sensing unit 30, a control processing unit 40, a variable frequency compressor 50, an electronic expansion valve 60, and a four-way reversing valve 70.

[0031] The image acquisition unit 20 is installed at a preset observation position on the outdoor heat exchanger 10, and its field of view covers the fin surface area of ​​the outdoor heat exchanger 10. The image acquisition unit 20 is configured to acquire surface image data of the outdoor heat exchanger 10 according to a preset scanning cycle. The image acquisition unit 20 integrates a lens heating assembly, which is electrically connected to the control processing unit 40 and configured to heat the lens upon receiving a defogging command. The image acquisition unit 20 is connected to the control processing unit 40 via a digital signal communication interface to transmit the acquired image data to the control processing unit 40.

[0032] The thermal sensing unit 30 includes an outdoor ambient temperature sensor 31 and a coil temperature sensor 32. The outdoor ambient temperature sensor 31 is located at the air inlet of the outdoor unit and is used to detect the outdoor ambient temperature. The coil temperature sensor 32 is attached to the wall of the refrigerant coil of the outdoor heat exchanger 10 and is used to detect the surface temperature of the coil. The thermal sensing unit 30 is configured to convert the detected analog temperature signal into a digital signal and send it to the control processing unit 40.

[0033] The control processing unit 40 includes an AI edge computing module 41 and a main logic control module 42. The AI ​​edge computing module 41 internally deploys a deep convolutional neural network algorithm and is configured to receive image data transmitted from the image acquisition unit 20 and perform feature extraction operations on the image data. The AI ​​edge computing module 41 outputs coverage parameters characterizing the area of ​​the frost-covered region and texture density parameters characterizing the microstructure of the frost layer. The main logic control module 42 is connected to the AI ​​edge computing module 41, the thermal sensing unit 30, the variable frequency compressor 50, the electronic expansion valve 60, and the four-way reversing valve 70. The main logic control module 42 is configured to receive visual feature parameters output by the AI ​​edge computing module 41 and temperature parameters output by the thermal sensing unit 30, and generate control commands based on a preset thermodynamic mapping algorithm.

[0034] The variable frequency compressor 50, electronic expansion valve 60, and four-way reversing valve 70 constitute the actuators of the heat pump system. The variable frequency compressor 50 is connected to the main logic control module 42 and is configured to adjust its operating speed according to the frequency commands output by the main logic control module 42. The electronic expansion valve 60 is connected to the main logic control module 42 and is configured to adjust the refrigerant flow according to the opening degree commands output by the main logic control module 42. The four-way reversing valve 70 is connected to the main logic control module 42 and is configured to switch the refrigerant flow direction according to the reversing commands of the main logic control module 42, thereby switching between the heating cycle and the defrosting cycle.

[0035] refer to Figure 2 As shown, this method is executed by the control processing unit 40, and the overall workflow is described as follows:

[0036] In the data synchronization acquisition step, the control processing unit 40 periodically triggers the image acquisition unit 20 to acquire the current frame image of the outdoor heat exchanger 10, and simultaneously reads the outdoor ambient temperature value and coil temperature value detected by the thermal sensing unit 30.

[0037] In the data validity verification step, the main logic control module 42 calculates the information entropy of the current frame image and the actual heat exchange temperature difference. The actual heat exchange temperature difference is the difference between the outdoor ambient temperature and the coil temperature. The main logic control module 42 determines whether the image information entropy is lower than a preset entropy threshold and whether the actual heat exchange temperature difference is lower than a preset temperature difference threshold. When both conditions are met, the main logic control module 42 sends a heating and defogging command to the image acquisition unit 20 and maintains the current operating state of the system; when either condition is not met, the acquired data is confirmed to be valid and the next processing step is initiated.

[0038] In the visual feature extraction step, the AI ​​edge computing module 41 performs segmentation and classification operations on the effective image, outputting frost coverage parameters and frost density coefficients. The frost density coefficient is a normalized value used to quantify the physical properties of frost, ranging from loose needle-like crystals to a dense ice shell.

[0039] In the multi-dimensional parameter calculation step, the main logic control module 42 calls the preset visual-temperature difference mapping model to calculate the theoretically predicted temperature difference based on the frost coverage rate parameter and the frost density coefficient. The main logic control module 42 calculates the ratio of the actual heat exchange temperature difference to the theoretically predicted temperature difference to obtain the internal frost correction factor. Combining the frost coverage rate parameter, the frost density coefficient, and the internal frost correction factor, the main logic control module 42 calculates the equivalent frost accumulation mass and the target defrosting heat load.

[0040] refer to Figure 4 As shown, in the variable parameter control strategy generation step, the main logic control module 42 compares the target defrost heat load with the defrost start threshold. When the target defrost heat load is greater than or equal to the defrost start threshold, the main logic control module 42 generates a defrost control command. Based on the target defrost heat load, frost density coefficient, and internal frost correction factor, the main logic control module 42 calculates the target operating frequency of the variable frequency compressor 50 and the target opening degree of the electronic expansion valve 60, respectively.

[0041] During the defrosting execution and termination steps, the main logic control module 42 controls the four-way reversing valve 70 to switch, drives the variable frequency compressor 50 to operate at the target operating frequency, and adjusts the electronic expansion valve 60 to the target opening degree. During defrosting operation, the control processing unit 40 continuously monitors the frost coverage parameter and coil temperature. When the frost coverage parameter is lower than the residual threshold and the coil temperature is higher than the exit temperature threshold, the main logic control module 42 controls the system to exit the defrosting mode and resume heating operation.

[0042] The control processing unit 40 executes the data synchronous acquisition process to establish a multi-dimensional feature dataset with spatiotemporal consistency, providing a benchmark for subsequent deviation correction calculations. The control processing unit 40 triggers the acquisition task based on an internal clock interrupt signal. The system has a set scan cycle. The scanning cycle is an adjustable parameter, configured to be set to a long cycle (e.g., 5 to 10 minutes) in non-defrosting mode and automatically adjusted to a short cycle (e.g., 30 to 60 seconds) in defrosting mode to meet real-time requirements under different operating conditions. When a clock interrupt is triggered, the control processing unit 40 generates a synchronization trigger signal and sends it to the shutter control port of the image acquisition unit 20 and the analog-to-digital conversion interface of the thermal sensing unit 30.

[0043] Image acquisition unit 20 responds to the synchronization trigger signal to perform optical imaging on the surface of outdoor heat exchanger 10 and acquire raw image frames. The image acquisition unit 20 integrates a photosensitive element, which is either a CMOS image sensor or a CCD image sensor. For nighttime or low-light environments, the image acquisition unit 20 is configured to automatically activate the infrared supplementary lighting component based on the ambient light intensity to acquire near-infrared image data. The photosensitive principle of the image acquisition unit and the specific implementation of the infrared supplementary lighting circuit can be achieved by those skilled in the art using existing photoelectric conversion technology and LED driving circuits, which are well-known technologies in the field and will not be elaborated upon here.

[0044] The control processing unit 40 processes the acquired raw image frames. Region of Interest (ROI) extraction is performed. Since the camera's field of view includes non-heat-exchange areas such as the casing, support, or background environment, directly processing the full-frame image would introduce noise interference and increase computational load. The control processing unit 40 has a pre-stored coordinate mapping matrix configured to crop the original image frame into an image to be processed that only contains the heat exchanger fin area. The cropping process is achieved through the following matrix operations:

[0045] ;

[0046] in, This indicates the pixel coordinates of the image to be processed. The grayscale value or color value at that location. Represents the original image frame. This represents the starting vertex coordinates of the region of interest in the original image coordinate system. and These are pixel indices. , and These represent the width and height of the region of interest, respectively.

[0047] The thermal sensing unit 30 responds to the synchronization trigger signal and samples the analog voltage signals from the outdoor ambient temperature sensor 31 and the coil temperature sensor 32. To eliminate signal fluctuations caused by electromagnetic interference, the control processing unit 40 processes the continuously sampled signals. The current outdoor ambient temperature value is calculated by performing a moving average filter on each temperature data point. and coil temperature values The filter calculation formula is as follows:

[0048] ;

[0049] in, This represents the filtered temperature value, specifically referring to... or , This represents the instantaneous sampled value output by the sensor. The sampling interval is... This is the length of the sliding window.

[0050] The control processing unit 40 performs timestamp alignment verification on the image data and temperature data. The control processing unit 40 also records the image acquisition completion time. and the time when temperature sampling is completed The system is configured with a maximum permissible time delay deviation. The control processing unit 40 calculates the absolute value of the time difference. The multimodal data of the current group are considered to be consistent in the time dimension when the time difference meets the following conditions:

[0051] ;

[0052] If the data is determined to be consistent, the control processing unit 40 packages it into the same frame structure of the data buffer for subsequent feature extraction and logical operations; if the time difference is greater than the maximum allowable delay deviation, the data is determined to be out of sync, the system discards the current group of data and immediately triggers a resampling operation to prevent mismatch between visual features and thermal parameters caused by time misalignment.

[0053] The main logic control module 42 executes this verification process, which aims to identify lens dirt, line-of-sight obstruction or abnormal water condensation in the image acquisition unit 20 through consistency analysis of multi-dimensional features, and prevent false defrosting judgments caused by visual signal distortion.

[0054] The main logic control module 42 processes the cropped images to be processed. Gray-level histogram statistics and information entropy calculations are performed. Information entropy is a physical parameter that measures the richness of image texture and the amount of information. For heat exchangers with fins, copper tubes, and various frost layers on their surfaces, normal imaging exhibits a high gray-level change rate and texture complexity, corresponding to a high information entropy value. When the lens is covered by water mist, completely blocked by snow, or in a low-light blinding state, the image exhibits a single gray-level distribution, corresponding to a low information entropy value. The main logic control module 42 converts the image to be processed into single-channel gray-level data and statistically analyzes the probability distribution of each gray level in the image. Subsequently, the image information entropy is calculated using the Shannon entropy formula. The calculation formula is as follows:

[0055] ;

[0056] in, Image information entropy is expressed in bits. This represents the total number of gray levels in the image, and its value is 256 in an 8-bit depth image. This represents the grayscale index, with a value range of [value range missing]. ; Represents grayscale level The probability of a pixel appearing in an image, which is the ratio of the number of pixels at that gray level to the total number of pixels.

[0057] The main logic control module 42 calculates the current actual heat exchange temperature difference. This is used to characterize the thermodynamic operating state of the outdoor heat exchanger 10. The actual heat exchange temperature difference reflects the load on the heat exchanger and the driving force for heat exchange between the air side and the refrigerant side. The calculation formula is as follows:

[0058] ;

[0059] in, This represents the actual heat exchange temperature difference, and its absolute value is used to indicate the temperature difference range. This refers to the outdoor ambient temperature. This refers to the coil temperature value.

[0060] The main logic control module 42 performs state determination based on the principle of logical consistency between visual features and thermal parameters. The system has a preset lower limit threshold for information entropy. and effective threshold temperature difference In one specific embodiment, the lower bound threshold of information entropy. The value range is set to 1.5 to 2.5 bits, and the effective threshold for temperature difference is... The value range is set to 2℃ to 4℃.

[0061] The main logic control module 42 performs the following logic judgment: When the system is in the initial stage of heating operation or at a low load, if the heat exchanger surface is clean and frost-free, and the fin structure is clearly visible, the actual heat exchange temperature difference is... Smaller, but with lower image information entropy Maintain at a high level. If image information entropy is detected. Below the lower limit of information entropy This indicates that the image is extremely smooth or monotonous, while the actual heat transfer temperature difference is... Below the effective temperature difference threshold If so, it is determined to be a sensor malfunction. The physical basis for this is that if the low-entropy smoothing characteristic of the image is caused by a dense ice shell or thick frost layer, according to the principle of heat conduction, the thermal resistance of the frost layer will lead to a huge temperature difference between the air side and the refrigerant side (i.e., It should be much larger than Conversely, if the image shows a smooth (low entropy) but the temperature difference is very small, it indicates that the heat exchanger surface is not actually frosted. The image anomaly is caused by foreign objects obstructing the lens surface, water vapor condensation, or a faulty image sensor. The judgment logic expression is as follows:

[0062] ;

[0063] in, This is a visual fault flag bit, with a value of 1 indicating a fault state and a value of 0 indicating a normal state; Represents the logical AND operation.

[0064] The main logic control module 42 uses visual fault flags. The status execution processing strategy is implemented. When the visual fault flag is set... When the value is 1, the main logic control module 42 generates a lens maintenance command and sends it to the image acquisition unit 20, activating the lens heating assembly to evaporate surface condensation or melt snow. Simultaneously, the main logic control module 42 marks the image data of the current frame as invalid. In subsequent defrost load calculations, the weighting coefficients calculated based on this visual image are set to zero, and control is maintained solely based on thermal parameters or by using the visual features of the previous valid frame until the visual fault flag is set. Reset to 0. When the visual fault flag bit... When the value is 0, the main logic control module 42 confirms that the current visual data is valid and transmits it to the AI ​​edge computing module 41 for depth feature extraction.

[0065] The AI ​​edge computing module 41 performs the preprocessing process to suppress distortion and noise introduced by the characteristics of the imaging device itself and environmental factors, and to concentrate computing resources on the fin area with heat exchange function.

[0066] The AI ​​edge computing module 41 performs geometric distortion correction on the received valid images. Since the image acquisition unit 20 is typically installed in the narrow interior space of the outdoor unit, a short focal length, wide-angle lens is used to cover the entire heat exchanger surface, which leads to barrel distortion at the image edges. To prevent distortion from causing area integration errors in the calculation of frost texture density, the system uses a polynomial distortion model to remap the original pixel coordinates. The AI ​​edge computing module 41 calculates the corrected pixel coordinates using pre-calibrated distortion coefficients. The correction model is as follows:

[0067] ;

[0068] in, This represents the relative coordinates of the distorted image with respect to the principal point (Optical Center). In other words, if the pixel coordinates of the original image are... The principal point coordinates are ,but , ; Represents the corrected ideal relative coordinates; This represents the Euclidean distance from a pixel to the principal point of the image, i.e. ; , , These are the radial distortion coefficients of the lens. These coefficients are determined by the Zhang Zhengyou calibration method or other optical calibration methods and stored in non-volatile memory when the image acquisition unit 20 leaves the factory.

[0069] The AI ​​edge computing module 41 performs Gaussian smoothing filtering on the corrected image. Considering the high-frequency vibrations of the compressor and fan during the operation of the air-source heat pump, and the shot noise introduced by the increased sensor gain in low-light outdoor environments, these high-frequency noises can interfere with subsequent feature extraction of the micro-texture of the frost layer. The AI ​​edge computing module 41 uses a two-dimensional Gaussian convolution kernel to perform convolution operations on the image to suppress high-frequency noise. The convolution operation formula is as follows:

[0070] ;

[0071] in, This represents the pixel grayscale value after noise reduction; This represents the image after geometric correction. The kernel radius is configured to range from 1 to 3. Let Gaussian kernel function be defined mathematically as follows:

[0072] ;

[0073] in, The standard deviation of the Gaussian distribution is used to control the smoothness. This parameter is preset according to the signal-to-noise ratio characteristics of the image acquisition unit, and the typical value range is 0.5 to 1.5.

[0074] The AI ​​edge computing module 41 performs adaptive histogram equalization (CLAHE) processing on the denoised image. Due to varying outdoor lighting conditions and shadows on the heat exchanger surface, the image exhibits uneven local contrast. To enhance the contrast between the frost layer and the fin background, particularly improving the visibility of texture details in low-contrast frost features, the system performs block-based contrast-limited enhancement. In this embodiment, the contrast limit threshold (ClipLimit) of the CLAHE algorithm is set to 2.0 to 4.0, and the grid size (TileGridSize) is set to 8×8 pixel blocks to prevent excessive enhancement of background noise. After enhancement processing, the system outputs a normalized enhanced image.

[0075] The AI ​​edge computing module 41 performs fine extraction of regions of interest based on a mask. The heat exchanger edge still includes non-heat exchange components such as copper tube bends and sheet metal supports. These areas do not frost or have different frost characteristics than the fins; including them in the calculation would affect the accuracy of the coverage. The AI ​​edge computing module 41 has a pre-set binary mask matrix. The size of this matrix is ​​consistent with the image resolution, where the elements corresponding to the effective fin regions have a value of 1, and the elements corresponding to the background and interfering component regions have a value of 0. The AI ​​edge computing module 41 will enhance the image. With mask matrix Perform pixel-by-pixel Hadamard product operations to generate the final input tensor. :

[0076] ;

[0077] Through the above calculations, the pixel values ​​of all non-finned regions in the image are forced to zero, ensuring that the subsequent convolutional neural network extracts features only from the effective heat transfer surface. The AI ​​edge computing module 41 then processes the preprocessed input tensor... Adjust the size to fit the input layer of the neural network and perform standardization.

[0078] refer to Figure 3 As shown, the AI ​​edge computing module 41 loads and runs a multi-task deep convolutional neural network, which employs a shared backbone-dual-head output architecture. The input is a preprocessed image tensor. The data is transmitted to the shared feature extraction backbone network. This backbone network is configured to extract general low-level features of the image, including edges, corners, and texture primitives. In this embodiment, the backbone network uses a lightweight MobileNetV3 or a pruned ResNet-18 structure to adapt to the computational resource constraints of the embedded controller. After convolution and downsampling operations in the backbone network, the system generates a high-dimensional shared feature map. .

[0079] Shared feature map The feature splitting nodes in the network are transmitted to two independently running parallel functional task branches: the macroscopic coverage segmentation branch and the microscopic texture density analysis branch.

[0080] The macroscopic coverage segmentation branch is configured to perform pixel-level semantic segmentation. This branch employs an upsampled decoder structure, fusing shallow features from the backbone network through skip connections to restore spatial resolution. This branch outputs a binarized probability map of the same size as the input image, where the value of each pixel represents the probability that the point belongs to the frost category. The AI ​​edge computing module 41 performs threshold truncation on the probability map, typically setting the probability threshold to 0.5, to generate a binary segmentation mask. Based on this mask, the A1 edge computing module 41 calculates the frost coverage parameter. To eliminate the impact of non-heat-exchange areas (such as supports and background) on calculation accuracy, the calculation formula only counts pixels within the region of interest (ROI):

[0081] ;

[0082] in, and These are the width and height of the input image, respectively; For the binary segmentation mask in coordinates The value at this location is 1 if it is determined to be frost, and 0 otherwise. This is the region of interest mask defined in the previous steps. If the coordinate is located in the effective fin region, the value is 1; otherwise, it is 0.

[0083] The microtexture density analysis branch is configured to perform image regression tasks, focusing on high-frequency texture information in the feature maps. Frost layers of different physical densities visually exhibit different texture roughness: loose sublimated frost displays high-frequency, chaotic needle-like textures; while dense condensed frost or melted-re-frozen ice shells display low-frequency, smooth, hardened textures. During network training, the output label of this branch is calibrated based on the frost mass per unit area, allowing the output value to characterize the physical compactness of the frost layer. The microtexture density analysis branch introduces a global average pooling layer to compress spatial features into channel feature vectors, which are then mapped to a normalized value, the frost density coefficient, through a fully connected layer. .

[0084] The AI ​​edge computing module 41 calculates the final frost density coefficient using the following mapping function:

[0085] ;

[0086] in, is the frost density coefficient, with a value range of [0,1]. The closer the value is to 0, the looser the frost layer is; the closer the value is to 1, the denser the frost layer is. Use the Sigmoid activation function; The number of feature channels; and These are the weights and bias parameters for the fully connected layer; This indicates a global average pooling operation; Indicates the texture analysis branch number Feature map of each channel.

[0087] The AI ​​edge computing module 41 calculates the frost coverage parameters. and frost density coefficient The output is synchronously sent to the main logic control module 42. Through this dual-channel decoupling design, the system can distinguish between large areas of thin frost (high...). ,Low ) and small areas of thick ice (low) ,high Two completely different operating conditions. The main logic control module 42 judges the thermal resistance characteristics based on this: the former has high coverage but low thermal resistance and low air resistance, so it does not require rapid drying and defrosting; the latter has low coverage but seriously hinders heat exchange and is difficult to remove, so it requires a defrosting strategy with high heat load.

[0088] The main logic control module 42 calculates the system's heat exchange efficiency decay index. During the frosting process, the thermal resistance effect of the frost layer and its blockage effect on the airflow channels lead to a decrease in heat exchange efficiency, which is reflected in the macroscopic thermal parameters, such as coil temperature. With ambient temperature The temperature difference between them has increased abnormally. The main logic control module 42 collects the ambient temperature in real time. and coil temperature And combined with the compressor operating frequency Calculate the normalized temperature difference decay rate under the current operating conditions. The calculation formula is as follows:

[0089] ;

[0090] in, express The normalized temperature difference decay rate at time is restricted to a strict range of [0,1] by the Clip function. If the calculated result is less than 0, it is taken as 0, and if it is greater than 1, it is taken as 1. and These are the ambient temperature and coil temperature sampled in real time, respectively. The frost-free reference temperature difference at the current compressor frequency and ambient temperature is obtained by consulting a pre-stored frequency-temperature-temperature difference reference database. If the current operating point is located between discrete record points in the database, the main logic control module 42 uses bilinear interpolation to calculate the current reference temperature difference. The maximum allowable temperature difference limit of the system is the critical temperature difference value determined in a laboratory environment when the heat exchanger frosts down to the point that the heating capacity decreases by 30% to 40%.

[0091] The main logic control module 42 calculates the internal frosting correction factor based on the deviation between visual and thermal characteristics. When internal frost or bottom-initiated frost occurs in the heat exchanger, the external visual sensor captures the frost coverage. The heat exchange efficiency degradation index is low, but the heat exchange efficiency degradation index is low. However, it exhibits a high value. In this case, relying solely on visual features will lead to defrosting lag. The main logic control module 42 utilizes the sigmoid function to construct a nonlinear correction model, focusing on gain compensation for inconsistent regions with large thermal attenuation but small visual coverage. The formula for calculating the correction factor is as follows:

[0092] ;

[0093] in, This is an internal frost correction factor, used to characterize the implicit internal frost weight; This is the sensitivity coefficient, used to adjust the response rate of the correction factor to the deviation, typically ranging from 5 to 10. The temperature difference decay rate calculated in the previous steps; The frost coverage rate calculated in the aforementioned dual-channel feature decoupling analysis step; This is the deviation tolerance threshold, used to filter out minor fluctuations caused by measurement noise, with a value range of 0.1 to 0.2. This formula clarifies that when thermal attenuation exceeds visual coverage, It will quickly approach 1, thus increasing the weight of thermal parameters in the final decision.

[0094] The main logic control module 42 integrates visual features with the deduced thermal correction features to generate the final comprehensive defrosting load index. This index is the core decision-making basis for the defrost control logic to determine whether to switch to defrost mode. The system uses a dynamic weighting method to synthesize the surface visual load and the internal inferred load. The calculation formula is as follows:

[0095] ;

[0096] in, This represents the comprehensive defrost load index, and its upper limit is limited to 1 by a minimum value function. Frost coverage; The frost density coefficient is the one calculated in the preceding steps; The basic weighting coefficient is usually set to 0.6 to 0.7 to ensure that visual judgment is the primary method under normal working conditions. The temperature difference decay rate; This is the internal frost correction factor. The term in this formula (1+) As a scaling factor, when the risk of internal frost is detected (i.e. The weight contribution of the thermal dimension is temporarily increased to twice the original value to ensure that the defrost load index can reach the trigger threshold in time under visual blind spot conditions.

[0097] Regarding the thermal parameters involved in the above steps, those skilled in the art should understand that the coil temperature... The change is thermodynamically equivalent to the unit's intake pressure Changes in compressor operating current The changes. In embodiments without a coil temperature sensor, the main logic control module 42 can directly acquire the pressure value fed back by the suction pressure sensor and convert it into the corresponding saturated evaporation temperature, or acquire the real-time current value of the compressor and use the mapping relationship between current and condensation / evaporation temperature for equivalent substitution. The specification clearly states that the thermal parameters described in the claims cover temperature, pressure, current, and their derived calculated values. These parameters can all characterize the thermal resistance change characteristics of the heat exchanger and belong to equivalent means with the same technical concept.

[0098] The defrosting decision control module 43 calculates the equivalent frost density under the current operating conditions. Texture density coefficients acquired by the visual sensor Although it reflects the microscopic compactness of the frost layer, it is a normalized value. For physical quality calculations, the system maps it to a physical density value. This embodiment sets a lower limit for the physical density of the frost layer. This corresponds to loose, dendritic crystalline frost, with a typical value of 100-150 kg / m³. 3 and the upper limit of physical density This corresponds to a dense ice shell or transparent ice, with a typical value of 800-900 kg / m³. 3 The calculation formula is as follows:

[0099] ;

[0100] in, This represents the estimated physical density of the frost layer; and These are the preset frost layer density boundary constants; This refers to the frost density coefficient output in the aforementioned dual-channel feature decoupling analysis step. The defrosting decision control module 43 calculates the equivalent total frost mass on the surface of the total heat exchanger. This step combines visually visible surface frost with internal, invisible frost corrected through thermal simulation. The system introduces the effective total surface area of ​​the heat exchanger. and the estimated average frost thickness benchmark The calculation formula is as follows:

[0101] ;

[0102] in, Indicates the total mass of the equivalent frost layer; This refers to the total fin and pipe surface area of ​​the outdoor heat exchanger. This parameter is determined by the specific geometric dimensions of the heat exchanger and is pre-stored in the controller. The characteristic frost thickness is a reference value, usually ranging from 1 mm to 2 mm. This parameter serves as a calibration coefficient for mapping two-dimensional coverage to three-dimensional volume. Its value is based on the average frost thickness measured per unit coverage under standard laboratory conditions. Frost coverage; This is the internal frost amount weighting coefficient, used to adjust the blind zone correction factor. The weight of the impact on the total mass typically ranges from 0.3 to 0.5. The internal frost correction factor, calculated in the aforementioned blind spot correction mechanism step, improves the calculated total quality when severe internal frost exists.

[0103] The defrosting decision control module 43 calculates the target heat load required for defrosting based on the principle of energy conservation. This heat load includes the energy (latent heat and sensible heat) required to raise the frost layer from its current temperature to its melting point and completely melt it, as well as the heat capacity required to heat the heat exchanger metal body to the defrosting termination temperature. The calculation formula is as follows:

[0104] ;

[0105] in, Indicates the target defrosting heat load; The specific heat capacity of ice is 2.1 kJ / (kg·℃). This is the melting point temperature of ice, taken as 0℃; This is the coil temperature at the moment defrosting starts, and it is usually a negative value. The latent heat of fusion of ice is 334 kU / kg; The mass of the metal body of the outdoor heat exchanger, including copper tubes and aluminum foil, is a fixed parameter; This is the equivalent specific heat capacity of the heat exchanger material; this parameter is based on the mass of copper in the heat exchanger. With aluminum quality and their respective specific heat capacities , The weighted average is calculated to obtain, that is ; The preset defrost termination temperature target is usually set to 15°C to 20°C to ensure that the moisture not only melts but also evaporates or flows away smoothly.

[0106] The defrosting decision control module 43 will calculate the target heat load. Converted to the target operating frequency of the compressor and the opening configuration of the electronic expansion valve The system has pre-stored heating capacity curves of the compressor at different frequencies. The defrost decision control module 43 determines the defrost time based on the preset desired defrost time. The defrosting time is set to 3 to 5 minutes. First, calculate the average heating power required for defrosting. :

[0107] ;

[0108] Subsequently, the defrosting decision control module 43 searches the pre-stored compressor performance database for components that meet the heating capacity requirements. The lowest operating frequency is used as the target frequency. This process enables precise control over defrosting on demand: for For smaller, thinner frost conditions, the calculated At lower frequencies, the system will operate at a lower frequency to achieve a low-noise and energy-efficient defrosting mode; for In cases of heavy ice, the system will output high-frequency commands and provide high-intensity enthalpy output through high pressure differential and high-flow refrigerant to enter high-load defrosting mode.

[0109] Those skilled in the art should understand that the above regarding The calculation is a feedforward estimate based on a physical model. In actual control, the defrosting decision control module 43 can also perform closed-loop correction of the target value by combining the real-time monitored high pressure rise rate. This control strategy based on feedforward-feedback composite also falls within the technical scope of the present invention based on target heat load calculation.

[0110] The execution unit control module 44 determines the compressor's basic operating frequency. The compressor's heating capacity is positively correlated with its operating frequency, but is simultaneously constrained by the nonlinearity of the evaporation and condensation temperatures, i.e., the pressure difference. Because the coil temperature is extremely low, the suction specific volume is large, and the mass flow rate is low during the initial defrosting phase, ambient temperature compensation is required for the theoretical power. The execution unit control module 44 calls a preset frequency-power-ambient temperature three-dimensional characteristic graph, which is fitted by experimentally calibrating the heating output coefficient per unit frequency under different ambient temperatures. The calculation model for the basic operating frequency is as follows:

[0111] ;

[0112] in, Indicates the basic operating frequency, in Hz; The average heating power requirement is obtained from the aforementioned calculation steps of equivalent frost layer quality and target heat load. For the compressor at the current ambient temperature The volumetric efficiency correction factor is obtained by linear interpolation in a pre-stored lookup table based on the current measured ambient temperature. The value range is usually between 0.7 and 0.95. The reference heating capacity per unit frequency of the compressor under standard operating conditions, expressed in kW / Hz; This is the low-temperature compensation gain coefficient, measured in Hz / ℃, used to compensate for the additional mechanical losses caused by the increase in lubricating oil viscosity due to low temperatures. The standard operating temperature is typically 7°C. This represents the current outdoor ambient temperature.

[0113] The execution unit control module 44 calculates the frequency correction amount based on process status feedback. The base frequency is used as the open-loop control target. To ensure that the defrosting process conforms to the expected temperature rise curve, the system monitors the high-pressure side pressure. Or the corresponding rate of increase in condensation temperature. The execution unit control module 44 operates at a preset sampling period. (For example, 1 second) Acquire the high-pressure signal and calculate its real-time rate of change. If the pressure rise rate is lower than the target value, it indicates that the actual defrosting heat absorption is greater than expected or the compressor performance is deteriorating, requiring an increase in frequency; conversely, the frequency should be decreased. The correction amount is calculated using a proportional feedback control algorithm:

[0114] ;

[0115] in, for Frequency correction amount at any given time; This is the proportional gain coefficient, set according to the system's thermal inertia; The target high pressure rise rate is set to 0.5 to 1.5 bar / s to ensure that the refrigerant can quickly build up the pressure difference required to melt the ice layer; the latter is the actual high pressure rise rate calculated in real time.

[0116] The execution unit control module 44 generates the final target drive frequency. The system incorporates safety boundary constraints and resonance point avoidance measures. At specific speeds, the compressor can generate mechanical resonance with the piping system, leading to noise and stress concentration. The system has a pre-stored set of resonant frequency exclusion zones. The execution unit control module 44 performs the following logical processing on the superimposed frequencies:

[0117] ;

[0118] ;

[0119] ;

[0120] in, For unconstrained target frequencies; The center frequency of the resonance forbidden zone is obtained by the arithmetic mean of the upper and lower limits; and These are the minimum allowable operating frequency of the compressor, such as 15Hz, and the maximum operating frequency, such as 90Hz. This is the restricted area for resonant frequencies; For safety margin, a range of 1Hz to 2Hz is typically used. When the calculated jump frequency (e.g.) Exceeding or When restrictions apply, the system will prioritize execution. or Boundary constraints. The output after the above logical judgment. This is the final instruction sent to the frequency converter drive.

[0121] The compressor frequency mentioned in the specification refers to the fundamental frequency of the electrical signal used to control the compressor motor speed; its physical essence is controlling the mass flow rate of the refrigerant. In embodiments employing a brushless DC motor or a permanent magnet synchronous motor, this frequency control strategy is equivalent to controlling the motor speed or the torque current component in vector control. Adjustment. Any technical means of changing refrigerant flow by adjusting drive energy is covered within the scope of the variable frequency mapping strategy described in the claims of this invention.

[0122] The execution unit control module 44 calculates the feedforward base opening degree based on the compressor operating frequency. This step achieves baseline matching of the refrigerant mass flow rate, ensuring that the compressor frequency is within a certain range. When the pressure changes, the throttling area of ​​the expansion valve can respond linearly and synchronously, preventing low-pressure abnormalities caused by insufficient liquid supply or liquid slugging risks caused by excessive liquid supply. The execution unit control module 44 establishes a linear mapping model between the compressor frequency and the expansion valve opening.

[0123] ;

[0124] in, express The basic opening degree of the electronic expansion valve at any given time, in steps; The variable frequency mapping strategy for the aforementioned compressor is determined as follows: The ultimate goal is to drive the frequency at any given moment; The flow matching coefficient characterizes the increase in expansion valve opening required per unit frequency increment; this coefficient is based on the compressor cylinder displacement. With expansion valve fully open flow coefficient The ratio was determined by calibration, with a typical value of 3 to 5 steps / Hz; The base offset opening is set as the total number of steps in the valve body stroke. 10% to 15% is used to cover the mechanical dead zone of the expansion valve and provide the initial flow area required to maintain the minimum circulation of the system.

[0125] The execution unit control module 44 calculates the feedback correction opening based on the intake superheat. This step, as a fine-tuning step, compensates for flow deviations caused by differences in ambient temperature and pipeline length, and ensures that the compressor suction port is in a preset overheated state. Execution unit control module 44 Preset control cycle Real-time acquisition of compressor suction temperature (e.g., 2 to 5 seconds) and inhalation pressure Regarding inhalation pressure The system queries the corresponding saturated evaporation temperature through its internally stored refrigerant thermophysical property database. The system first calculates the current measured superheat. :

[0126] ;

[0127] Subsequently, the execution unit control module 44 calculates the superheat deviation. ,in To achieve the target superheat, the temperature is typically set between 2°C and 5°C during defrosting. The correction for the current control cycle is calculated using an incremental PI (proportional-integral) algorithm.

[0128] ;

[0129] in, for The increment of opening correction at any given time; This is the proportional coefficient for expansion valve control; The integral coefficient; and These represent the overheating deviation between the current sampling time and the previous sampling time, respectively. At the initial moment of defrosting mode activation (i.e.... The system will adjust the deviation from the previous time step. Initialize to 0. This feedback mechanism ensures that the valve is quickly closed when a risk of liquid return is detected (low superheat), or the valve is opened when the liquid supply is insufficient (high superheat).

[0130] The execution unit control module 44 generates the final target opening degree of the electronic expansion valve. The system performs opening smoothing and boundary limiting. To prevent refrigerant flow noise or pressure surges caused by excessively rapid valve needle movement, the system limits the amplitude of the superimposed opening.

[0131] ;

[0132] ;

[0133] in, From the time of defrosting start ( ) up to the current time ( The cumulative sum of feedback corrections; This is the minimum safe opening level in defrost mode to prevent the compressor from overheating due to complete shutdown; This represents the total number of physical full-opening steps for the electronic expansion valve (e.g., 480 or 2000 steps). The x_Clip function is used to strictly limit the calculation results to a specific value. Within the closed interval. The execution unit control module 44 will... The pulses are converted into a drive pulse sequence and sent to the stepper motor of the expansion valve.

[0134] Regarding the inhalation pressure involved in the above steps As those skilled in the art should understand, the saturated evaporation temperature can be obtained directly by a pressure sensor installed at the inlet of the gas-liquid separator or the suction port of the compressor; in low-cost models without a low-pressure sensor, the temperature value measured by the evaporator coil temperature sensor can also be used to approximate the saturated evaporation temperature. This indirect measurement method based on temperature-derived pressure is a well-known technique in the field and is also covered within the scope of protection of this invention.

[0135] Meanwhile, the electronic expansion valve opening degree and its unit step count mentioned in the specification are a typical description of a needle valve-type electronic expansion valve driven by a stepper motor. Technically, this feature is equivalent to controlling the flow cross-sectional area of ​​the throttling element. If the system uses a pulse width modulation controlled electromagnetic expansion valve, the opening degree corresponds to the duty cycle of the PWM signal; if a thermostatic expansion valve is used in conjunction with electric heating for auxiliary regulation, it corresponds to the heating power. The expansion valve regulation strategy summarized in the claims covers all technical means of changing the refrigerant flow resistance through electrical signals.

[0136] The defrosting decision control module 43 calculates the real-time residual frost coverage and generates a visually ready status flag. During the defrosting process, the vision sensor continuously acquires image data of the outdoor heat exchanger and extracts the pixel areas of residual frost using the aforementioned image segmentation algorithm. To address the issue of high-brightness noise in the image caused by water vapor atomization interference during the later stages of defrosting, the module employs a moving average filter to continuously... Image features of frames (e.g., 5 to 10 frames) are smoothed, and the smoothed frost coverage is calculated. :

[0137] ;

[0138] in, for Frost coverage after time-domain smoothing at any given moment; for The raw instantaneous coverage output by the real-time vision sensor; For frames. The system sets a visual persistence threshold. This threshold is typically set between 396% and 5%, allowing for a very small amount of unstructured residue. The logic for determining visual readiness is as follows:

[0139] ;

[0140] in, for The visual readiness indicator for each moment: 1 represents visual confirmation that defrosting is complete, and 0 represents that it is not complete. This is the preset tolerance threshold for residual frost. This step ensures that the heat exchanger surface is free of large areas of frost coverage in an optical dimension.

[0141] The defrosting decision control module 43 monitors thermal parameters and generates a thermal readiness status flag. This step confirms that the heat exchanger metal body has acquired sufficient enthalpy to ensure that frost not only melts into liquid water but also possesses enough sensible heat to slide off or evaporate from the fin gaps, preventing secondary icing. The system sets the defrost exit temperature threshold. The typical value is 12℃ to 18℃. The logic for determining the thermal readiness state is as follows:

[0142] ;

[0143] in, for A sign indicating that the thermal is ready at any given moment; The coil temperature is collected in real time. For embodiments where a coil temperature sensor is not directly installed, the defrost decision control module 43 collects the condensing pressure. That is, the high pressure during defrosting is converted into the corresponding saturated condensation temperature. It is compared with the preset pressure corresponding to the temperature threshold, and its physical essence is also to characterize the thermal state of the heat exchanger.

[0144] The defrost decision control module 43 performs a comprehensive logical judgment and generates the final defrost termination command. The system requires both visual residue-free operation and thermal compliance to be met simultaneously for normal exit to be triggered. Furthermore, to prevent the system from entering an indefinite waiting period due to sensor malfunctions, such as camera obstruction by foreign objects or temperature probe detachment, a maximum safe defrosting time has been introduced. This is typically set to 10 to 12 minutes as a mandatory protection condition. The comprehensive judgment logic formula is as follows:

[0145] ;

[0146] in, This is a Boolean control command. When the result is true, it triggers the four-way valve to switch and stops the compressor from high-frequency operation. This represents the logical AND operation; Represents a logical OR operation; This represents the running time of the current defrosting cycle; This is the preset maximum forced exit time protection threshold.

[0147] The specific technical effect of this logical mechanism is that: when but When this occurs, it indicates that although the temperature at the sensor measuring point has risen, there is still unmelted ice in other areas of the heat exchanger, such as the leeward side or the bottom, indicating localized unmelted ice. In this case, the system continues defrosting until the ice is visually confirmed to have disappeared. but When the temperature reaches a certain point, it indicates that although the surface frost has melted, the temperature of the metal pipe wall is still low and the moisture has not evaporated effectively. At this time, the system continues to run for a while until the temperature reaches the target, using the heat storage of the metal to evaporate the residual moisture and delay the start time of frosting in the next cycle.

[0148] The execution unit control module 44 performs pressure balancing and gravity draining operations. Before the four-way valve reverses, the system controls the compressor to reduce its frequency to shutdown or the minimum holding frequency, and keeps the electronic expansion valve fully open. This process continues for a preset draining time. The time is typically set to 30 to 60 seconds. During this period, the melted water on the surface of the heat exchanger flows along the fin pattern to the bottom water collection tray under the action of gravity. At the same time, the pressure on the high and low pressure sides of the system gradually tends to balance, reducing the pressure shock and refrigerant flow noise when the four-way valve switches.

[0149] The execution unit control module 44 calculates and executes the adaptive high-speed water-throwing speed. In order to use centrifugal force and airflow shear force to blow away the surface tension water film adhering to the fin surface, the system estimates the equivalent frost layer mass based on the aforementioned steps. That is, the total mass of water that has been converted into liquid water, and the current outdoor ambient temperature. Dynamic programming is used to calculate the fan speed. The speed calculation model is as follows:

[0150] ;

[0151] in, The target water-throwing speed is expressed in RPM. To overcome the minimum effective purging speed required to overcome fan static friction, it is set to 30% to 40% of the rated speed; This refers to the maximum safe operating speed allowed for the fan motor. The equivalent frost layer quality calculated in the preceding steps; The maximum water holdup constant designed for the heat exchanger is used to normalize the water load. This constant depends on the fin spacing and total surface area of ​​the heat exchanger. This is the water load weighting coefficient, with a value ranging from 0.4 to 0.6; The critical freezing temperature is taken as 0℃; Outdoor ambient temperature; This is the low-temperature compensation coefficient, expressed in RPM / ℃. This formula reflects the physical logic that the greater the amount of melted water, the greater the required purging momentum, and the lower the ambient temperature, the faster the purging speed is needed to prevent secondary icing and shorten the drying time. The execution unit control module 44 drives the DC brushless motor... Runtime For example, 10 to 30 seconds.

[0152] The execution unit control module 44 performs fan stall detection and abnormal protection. In blizzard or freezing rain conditions, the outdoor unit fan grille may be physically blocked by snow or icicles. During the water-spraying action, the system collects the motor phase current in real time. The actual rotational speed fed back by the Hall sensor The system constructs a stall detection criterion based on the motor model:

[0153] ;

[0154] in, for The stall status flag at any given time; This is the measured effective value of the motor current; The preset overcurrent protection threshold; The issued instruction speed is as described above. ; For feedback rotational speed; The allowable speed following error threshold. When A state that is continuously 1 exceeds the safe time window (e.g., 2 seconds) when the execution unit control module 44 determines that a physical obstruction has occurred.

[0155] The execution unit control module 44 executes the reverse troubleshooting or shutdown protection strategy. If determined... The system immediately stops the current forward drive signal and attempts to output a reverse rotation torque command to drive the fan in reverse. The reverse rotation speed is set to... For example, 200 RPM to 300 RPM, duration is For example, 5 seconds. The reversing motion uses the impact force of the reverse airflow to clear snow or loose, stuck ice from the grille. If passing through After, for example, 3 times, try rotating clockwise and counterclockwise. If the value is still 1, the system will send an abnormal fault code for the outdoor fan through the cloud communication module and force the exit of the water-spraying process, and enter the subsequent heating process in the form of natural air cooling or pure passive heat dissipation to prevent the motor windings from overheating and burning out.

[0156] Specific application examples:

[0157] This embodiment applies to a 5HP (horsepower) low-temperature air source heat pump unit, installed in a high-humidity environment in the Yangtze River Basin (ambient temperature -2℃, relative humidity 85%).

[0158] Image acquisition unit 20: It adopts a 2-megapixel wide-angle industrial camera, covering 95% of the fin area of ​​the outdoor heat exchanger, and integrates a 5W heating resistance ring (lens heating component).

[0159] Control processing unit 40: An embedded controller based on the ARM Cortex-A72 architecture, with a built-in NPU (2.0 TOPS computing power) for running the MobileNetV3 lightweight CNN network.

[0160] Actuators: DC inverter compressor (0-100Hz), 500-step electronic expansion valve, four-way reversing valve.

[0161] Detailed Explanation of Control Process

[0162] Phase 1: Defrosting Triggered Under Complex Operating Conditions (Variable Parameter Strategy Takes Effect)

[0163] At time T=0: The system is in heating mode. Ambient temperature. =-2℃, coil temperature = -8℃.

[0164] Data sampling and verification: The main logic control module 42 triggers sampling.

[0165] The image shows that the bottom of the heat exchanger is covered with a high-density ice shell, but the upper and middle parts have only sparse white frost.

[0166] Thermal data: Actual temperature difference =6℃, and the compressor current is too high.

[0167] AI analysis:

[0168] (Coverage) = 45% (visually not fully covered).

[0169] (Texture density) = 0.85 (identified as dense ice shell texture).

[0170] Multidimensional parameter calculation:

[0171] If relying solely on vision (due to low coverage), traditional algorithms will fail to detect defrosting.

[0172] This invention calculates the temperature difference decay rate. It was found to reach 0.9 (extremely high thermal resistance).

[0173] Calculate the internal frost correction factor :because The output of the sigmoid function .

[0174] Calculate the overall defrosting load :exist Under the weighted average, the negative exponent exceeded the threshold, accurately triggering defrosting.

[0175] Target generation:

[0176] Calculate equivalent mass Although the area is small, the density is high and the correction factor is high, so the calculation is... =1.2kg.

[0177] Generate command: Compressor target frequency =85Hz (high-frequency powerful defrosting), expansion valve opening =380 steps.

[0178] Phase Two: Defrosting Execution and Logic Termination (To Avoid Incomplete Defrosting)

[0179] =180 seconds: Defrosting in progress.

[0180] Coil temperature rebounded to (It meets the traditional thermal exit threshold).

[0181] In this solution: AI continuously monitors and detects that there are still remnants of ice shell at the bottom. =15% (greater than the visual persistence threshold of 5%).

[0182] Decision-making: Logical judgment The result was False. The system refused to exit, maintained defrosting, and continued to tackle residual frost at the bottom using latent heat.

[0183] =240 seconds:

[0184] Visual confirmation =3% (visual readiness).

[0185] Coil temperature =14℃ (thermal ready).

[0186] Decision: If both conditions are met, the exit command is triggered.

[0187] Phase 3: Adaptive water ejection (to prevent secondary icing)

[0188] =240 seconds: The system calculates that the mass of melted water is approximately 1.2 kg, and the ambient temperature is -2℃.

[0189] Substitute into the formula to calculate the water-throwing speed =650RPM. The fan reverses at high speed for 20 seconds to force out any residual moisture between the fins, preventing it from freezing into ice bridges when the machine is restarted.

[0190] Experimental verification and effect comparison

[0191] To verify the effectiveness of the present invention, comparative tests were conducted in a standard enthalpy difference laboratory.

[0192] Experimental setup

[0193] Operating Condition A (Severe Operating Condition): Dry bulb temperature -5℃, wet bulb temperature -4.6℃ (relative humidity 90%), simulating severe ice-water mixing and frosting.

[0194] Comparison objects:

[0195] Control group (existing technology): The traditional time-temperature method was used (running for 45 minutes and triggering when the coil temperature is < -7℃, and stopping when the coil temperature is > 12℃).

[0196] Experimental group (this invention): A variable parameter control method based on vision-thermal fusion.

[0197] Experimental results data

[0198] Performance indicators Control group (time-temperature method) Experimental group (this invention) Increase Remark Defrost trigger accuracy There were two instances of false defrosting when there was no frost. 0 misjudgments 100% The control group misjudged the situation due to strong winds and a drop in temperature; the experimental group filtered this out using information entropy verification. Average defrosting time 5 minutes and 30 seconds 4 minutes and 10 seconds shortened by 24% Experimental group based on Precise energy matching prevented minor frost from becoming major fire. Defrosting cleanliness Approximately 15% remains at the bottom. Bottom residue <2% promote Thanks to the combination of visual, human, and logical termination criteria Resumption of heating time 120 seconds 75 seconds 37.5% The water-throwing strategy effectively reduces the blockage of the air duct caused by secondary icing. Periodic average COP 2.65 2.92 An increase of 10.2% Reduces heat loss caused by ineffective defrosting and incomplete defrosting.

[0199] Results Analysis

[0200] Regarding incomplete defrosting: In the control group, defrosting was stopped immediately when the coil temperature reached 12℃. However, at this time, the ice layer at the bottom of the heat exchanger had not yet melted due to gravity, leading to faster frosting in the next cycle (a vicious cycle). The experimental group, through visual confirmation, extended the defrosting time by 60 seconds, thoroughly eliminating dead spots, which in turn extended the next heating cycle by 20 minutes.

[0201] Regarding variable parameter adjustment: During the thin frost cycle, the experimental group automatically reduced the compressor frequency to 50Hz, which not only reduced defrosting noise but also avoided the risk of lubricant deterioration caused by excessively high exhaust temperature.

[0202] Figure 5The figure shows a comparison of temperature rise curves during the defrosting control process between the embodiments of the present invention and existing technologies. The horizontal axis represents the defrosting operation time, and the vertical axis represents the surface temperature of the heat exchanger coil. The dashed line in the figure represents the control curve of the existing technology that relies solely on the temperature threshold. It determines that defrosting has ended when the preset exit threshold (8°C) is reached at around 6 minutes, causing the system to exit prematurely, leaving unmelted ice on the heat exchanger surface. The solid line in the figure represents the curve using the visual fusion control strategy of the present invention. Although the temperature has reached the target at 6 minutes, the control logic forces the system to continue running because the AI ​​vision module detects that the ice texture features have not completely disappeared. The system only exits the defrosting mode normally at 11 minutes after the AI ​​confirms that there is no frost on the surface. This comparison intuitively demonstrates that the present solution can effectively solve the technical problem of achieving the target temperature but incomplete defrosting.

[0203] Figure 6 This diagram illustrates a partitioning scheme based on AI feature parameters in an embodiment of the present invention. The horizontal axis represents the visual frost coverage rate. The vertical axis represents the texture density coefficient. The figure illustrates how the operating conditions are divided into different control zones based on these two characteristic dimensions: samples (solid squares) distributed in the high-density area in the upper left of the chart are identified by the system as a dense ice shell condition, corresponding to the execution of a high-frequency, powerful defrosting strategy to deal with hard ice that is difficult to melt; samples (hollow dots) distributed in the low-density, high-coverage area in the lower right of the chart are identified as a loose frost layer condition, corresponding to the execution of a low-frequency, energy-saving defrosting strategy; and samples (gray triangles) distributed in the double-low area in the lower left of the chart are identified as a clean / disturbed state, and the system maintains the heating mode without taking action.

Claims

1. A variable parameter defrosting control method for air source heat pumps based on visual recognition technology, applied to an air source heat pump unit including an outdoor heat exchanger (10), an image acquisition unit (20), a thermal sensing unit (30), a variable frequency compressor (50), and an electronic expansion valve (60), characterized in that, Includes the following steps: Acquire surface image data and coil temperature values ​​of the outdoor heat exchanger (10); Feature extraction is performed on the surface image data to output macroscopic coverage rate representing the area of ​​the frost-covered region and microscopic feature parameters representing the physical properties of the frost layer. Calculate the thermal attenuation index characterizing the degree of heat exchange performance degradation of the outdoor heat exchanger (10), determine the correction factor based on the numerical deviation between the thermal attenuation index and the macro coverage rate, and calculate the target defrost heat load by combining the micro characteristic parameters and the correction factor. The target operating frequency of the variable frequency compressor (50) and the target opening degree of the electronic expansion valve (60) are determined according to the target defrosting heat load, and the unit is controlled to perform defrosting operation. During defrosting operation, the macroscopic coverage rate and the coil temperature are monitored in real time. When both visual readiness and thermal readiness conditions are met, the unit is controlled to exit defrosting mode.

2. The air source heat pump variable parameter defrosting control method based on visual recognition technology according to claim 1, characterized in that, The steps of acquiring surface image data of the outdoor heat exchanger (10), outdoor ambient temperature values, and coil temperature values ​​further include: Record the data acquisition times of the image acquisition unit (20) and the thermal sensing unit (30), and calculate the absolute value of the time difference between the two acquisition times; The data of the current group is determined to be valid and used for subsequent calculations only when the absolute value of the time difference is less than or equal to a preset time delay deviation threshold.

3. The air source heat pump variable parameter defrosting control method based on visual recognition technology according to claim 1, characterized in that, Before performing feature extraction on the surface image data, the following steps are included: Calculate the image information entropy of the surface image data and the current actual heat exchange temperature difference; If the image information entropy is lower than the preset fuzzy lower limit and the actual heat exchange temperature difference is lower than the preset effective temperature difference threshold, then the image acquisition unit (20) is determined to be faulty, and the use of the current frame image data is blocked.

4. The air source heat pump variable parameter defrosting control method based on visual recognition technology according to claim 1, characterized in that, The feature extraction step for the surface image data employs a deep neural network algorithm, specifically including: The surface image data is semantically segmented and the percentage of frosted pixels is statistically analyzed to obtain the macroscopic coverage rate; Texture analysis is performed on the surface image data to obtain a texture density coefficient, which is used as the microscopic feature parameter. The texture density coefficient is used to quantify the density of the frost layer, and its value is positively correlated with the density of the frost layer.

5. The air source heat pump variable parameter defrosting control method based on visual recognition technology according to claim 1, characterized in that, The step of determining the correction factor includes: Calculate the difference between the thermal attenuation index and the macroscopic coverage rate; When the difference exceeds the preset deviation tolerance threshold, the correction factor is increased through a nonlinear correction model. The correction factor is used to compensate for the heat demand caused by the invisible frost inside the heat exchanger.

6. The air source heat pump variable parameter defrosting control method based on visual recognition technology according to claim 1, characterized in that, The steps for calculating the target defrost heat load include: The equivalent frost layer density is determined using the aforementioned microscopic characteristic parameters; The equivalent frost mass is obtained by multiplying the total surface area, reference thickness, macro coverage, equivalent frost density, and correction factor of the outdoor heat exchanger (10). The target defrosting heat load is calculated based on the latent heat of phase change corresponding to the equivalent frost mass and the heat capacity of the metal body of the outdoor heat exchanger (10).

7. The air source heat pump variable parameter defrosting control method based on visual recognition technology according to claim 1, characterized in that, The steps for determining the target operating frequency of the variable frequency compressor (50) include: The base frequency is determined based on the ratio of the target defrosting heat load to the preset defrosting time; The rate of pressure rise on the high-pressure side during the defrosting process is monitored in real time, and the frequency correction is calculated based on the deviation between this rate and the target rate. The target operating frequency is obtained by superimposing the base frequency and the frequency correction amount.

8. The air source heat pump variable parameter defrosting control method based on visual recognition technology according to claim 1, characterized in that, The steps for determining the target opening degree of the electronic expansion valve (60) include: The basic opening degree is determined by linear mapping based on the target operating frequency of the variable frequency compressor (50); The compressor suction superheat is monitored in real time during the defrosting process, and the opening correction amount is calculated based on the deviation between the compressor suction superheat and the target superheat. The target opening is obtained by superimposing the base opening and the opening correction amount.

9. The air source heat pump variable parameter defrosting control method based on visual recognition technology according to claim 1, characterized in that, In the step of controlling the unit to exit defrost mode: The visual readiness condition is: the sliding average of the macroscopic coverage is lower than a preset residual threshold. The thermal readiness condition is: the coil temperature value is higher than the preset exit threshold. The trigger logic for the control unit to exit the defrost mode is as follows: the exit operation is executed if and only if the visual readiness condition and the thermal readiness condition are met simultaneously, or if the defrost operation time reaches the forced protection time.

10. The air source heat pump variable parameter defrosting control method based on visual recognition technology according to claim 1, characterized in that, After exiting defrost mode, it also includes: The water-spinning speed of the outdoor unit fan is determined based on the magnitude of the target defrosting heat load; The greater the target defrosting heat load, the higher the set water-spraying speed and the longer the running duration.

Images