Intelligent defrosting control method, refrigeration system, controller and storage medium

Abstract

The application discloses an intelligent defrosting control method, a refrigeration system, a controller and a storage medium, and relates to the technical field of defrosting control. The method comprises the following steps: acquiring first operation parameters and initial reference parameters of a refrigeration unit; performing thickness calculation processing on the first operation parameters and the initial reference parameters based on a frost layer calculation model to obtain a first frost layer thickness; determining a first defrosting control mode according to a first comparison result between the first frost layer thickness and a preset thickness threshold; the first defrosting control mode is one of the following: a thin-frost working condition control mode, a medium-frost working condition control mode and a thick-frost working condition control mode; determining a target defrosting device from the refrigeration unit according to the first defrosting control mode, and controlling the target defrosting device to perform dynamic defrosting control processing on a cold storage, so that intelligent defrosting is realized on the cold storage. The method can reduce the hardware cost, reliably and accurately realize defrosting operation, enhance the defrosting effect and reduce the system energy efficiency.

Description

Technical Field

[0001] This application relates to the field of defrosting control technology, and in particular to an intelligent defrosting control method, refrigeration system, controller and storage medium. Background Technology

[0002] R32 refrigerant cold storage units are refrigeration systems that use R32 refrigerant. R32 refrigerant, also known as difluoromethane, is a new type of refrigerant characterized by energy saving, carbon reduction, environmental friendliness, and non-toxicity. During the operation of R32 refrigerant cold storage units, evaporator frosting can occur. Frost reduces the evaporator's heat exchange efficiency, leading to a decrease in the R32 refrigerant evaporation temperature and an increase in compressor load. If defrosting is not timely or performed improperly, it will increase unit energy consumption and may also affect the quality of stored goods due to temperature fluctuations. Current defrosting technologies rely on additional hardware (such as infrared sensors and weight sensors) for frost detection, increasing equipment costs and reducing accuracy. This makes it difficult to reliably and accurately guide defrosting operations, easily resulting in poor defrosting performance and high system energy consumption. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes an intelligent defrosting control method, a refrigeration system, a controller, and a storage medium, which can reduce hardware costs, reliably and accurately realize defrosting operation, enhance defrosting effect, and reduce system energy efficiency.

[0004] In a first aspect, embodiments of this application provide an intelligent defrosting control method applied to a controller in a refrigeration system. The refrigeration system includes: a controller, a cold storage unit, and a refrigeration unit; the refrigeration unit includes: an evaporator, a compressor, a hot gas defrosting valve, a condenser fan, an evaporator fan disposed in the evaporator, and an electrically auxiliary defrosting unit; the exhaust port of the compressor is connected to the condenser fan, and the suction port of the compressor is connected to the evaporator fan; the compressor is also connected to the evaporator fan via a defrosting branch, and the hot gas defrosting valve is disposed on the defrosting branch;

[0005] The method includes:

[0006] Obtain the first operating parameters and initial reference parameters of the refrigeration unit;

[0007] The first frost layer thickness is obtained by performing thickness estimation processing based on the first operating parameters and the initial benchmark parameters according to the frost layer estimation model; wherein the frost layer estimation model includes: an input layer, a calculation layer and an output layer connected in sequence; the thickness estimation processing includes: data processing, weighted calculation processing and inverse normalization processing performed in sequence; the weighted calculation processing adopts a preset multiple linear regression formula;

[0008] A first defrosting control mode is determined based on a first comparison result between the first frost layer thickness and a preset thickness threshold; the first defrosting control mode is one of the following: thin frost condition control mode, medium frost condition control mode, and thick frost condition control mode;

[0009] Based on the first defrosting control mode, a target defrosting device is determined from the refrigeration unit, and the target defrosting device is controlled to perform dynamic defrosting control processing on the cold storage, thereby achieving intelligent defrosting of the cold storage.

[0010] Secondly, embodiments of this application provide a refrigeration system, including: a controller, a cold storage, and a refrigeration unit; the refrigeration unit includes: an evaporator, a compressor, a hot gas defrosting valve, a condenser fan, an evaporator fan disposed in the evaporator, and an electrically auxiliary defrosting unit; the exhaust port of the compressor is connected to the condenser fan, and the suction port of the compressor is connected to the evaporator fan; the compressor is also connected to the evaporator fan via a defrosting branch, and the hot gas defrosting valve is disposed on the defrosting branch; the controller is capable of executing the intelligent defrosting control method as described in any one of the embodiments of the first aspect.

[0011] Thirdly, embodiments of this application provide a controller, including at least one processor and a memory for communicatively connecting to the at least one processor; the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform the intelligent defrosting control method as described in any of the embodiments of the first aspect.

[0012] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the intelligent defrosting control method as described in any of the embodiments of the first aspect.

[0013] The embodiments of this application include:

[0014] The refrigeration system includes a controller, a cold storage unit, and a refrigeration unit. The refrigeration unit includes an evaporator, a compressor, a hot gas defrosting valve, a condenser fan, an evaporator fan installed in the evaporator, and an electrically auxiliary defrosting unit. The compressor's exhaust port is connected to the condenser fan, and the compressor's suction port is connected to the evaporator fan. The compressor is also connected to the evaporator fan via a defrosting branch, on which the hot gas defrosting valve is installed. During intelligent defrosting control of the cold storage, the controller first acquires the first operating parameters and initial reference parameters of the refrigeration unit. Then, based on the frost layer estimation model, the thickness is calculated using the first operating parameters and the initial reference parameters to obtain the first frost layer thickness. The frost layer estimation model includes an input layer, a calculation layer, and an output layer connected sequentially. The thickness calculation process includes sequential data processing, weighted calculation processing, and denormalization processing. The weighted calculation processing uses a preset multivariate linear... The system employs a regression formula; without adding additional hardware, it can accurately calculate the current frost thickness based on the frost layer calculation model and the operating parameters of the refrigeration unit, reducing hardware costs and providing a reliable reference for subsequent dynamic defrosting control. Then, a first defrosting control mode is determined based on a first comparison result between the first frost thickness and a preset thickness threshold. The first defrosting control mode is one of the following: thin frost control mode, medium frost control mode, or thick frost control mode. The defrosting control mode is determined based on the calculated current frost thickness, thus reliably and accurately guiding the subsequent dynamic defrosting control. Finally, a target defrosting device is determined from the refrigeration unit according to the first defrosting control mode, and the target defrosting device is controlled to perform dynamic defrosting control on the cold storage, achieving intelligent defrosting. This reliably and accurately achieves dynamic defrosting operation, enhancing the defrosting effect and reducing system energy efficiency. In other words, the embodiments of this application can reduce hardware costs while reliably and accurately achieving defrosting operation, enhancing the defrosting effect, and reducing system energy efficiency. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of a system architecture for performing an intelligent defrosting control method according to an embodiment of this application;

[0016] Figure 2 This is a flowchart illustrating an embodiment of the intelligent defrosting control method provided in this application;

[0017] Figure 3 This is a schematic diagram of the model structure of a frost layer estimation model provided in one embodiment of this application;

[0018] Figure 4 This is a schematic diagram of the hardware structure of a controller provided in one embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0020] It should be noted that although a logical order is shown in the flowcharts in this application, in some cases, the steps shown or described may be performed in a different order than that shown in the flowcharts. In the description of this application, "several" means one or more, and "more" means two or more. The terms "first" and "second" are used only to distinguish technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of technical features indicated, or implicitly indicating the order in which the technical features are indicated.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] This application provides an intelligent defrosting control method, a refrigeration system, a controller, and a computer-readable storage medium, relating to the field of defrosting control technology. The method includes: acquiring first operating parameters and initial reference parameters of the refrigeration unit; calculating a first frost layer thickness based on a frost layer estimation model using the first operating parameters and initial reference parameters; determining a first defrosting control mode based on a first comparison result between the first frost layer thickness and a preset thickness threshold; the first defrosting control mode being one of the following: a thin frost condition control mode, a medium frost condition control mode, or a thick frost condition control mode; determining a target defrosting device from the refrigeration unit according to the first defrosting control mode; and controlling the target defrosting device to perform dynamic defrosting control processing on the cold storage, thereby achieving intelligent defrosting of the cold storage. This method can reduce hardware costs, reliably and accurately achieve defrosting operation, enhance defrosting effect, and reduce system energy efficiency.

[0023] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0024] like Figure 1As shown, the refrigeration system 100 includes: a controller 101, a cold storage 110, and a refrigeration unit; the refrigeration unit includes: an evaporator 102, a compressor 106, a hot gas defrosting valve 107, a condenser fan 105, an evaporator fan 103 installed in the evaporator 102, and an electrically auxiliary defrosting unit 104; the exhaust port of the compressor 106 is connected to the condenser fan 105, and the suction port of the compressor 106 is connected to the evaporator fan 103; the compressor 106 is also connected to the evaporator fan 103 through a defrosting branch 109, on which a hot gas defrosting valve 107 is installed. Furthermore, a throttling valve 108 is installed on the connecting pipe between the evaporator 102 and the condenser fan 105.

[0025] Specifically, the controller 101 is used to execute the intelligent defrosting control method provided in the embodiments of this application, thereby reducing hardware costs while reliably and accurately realizing defrosting operation, enhancing defrosting effect and reducing system energy efficiency.

[0026] Those skilled in the art will understand that the system structure shown in the figures does not constitute a limitation on the embodiments of this application, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0027] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0028] It will be understood by those skilled in the art that the system architecture and application scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. It is known by those skilled in the art that with the evolution of system architecture and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0029] Based on the above system structure, various embodiments of the intelligent defrosting control method of this application are presented below.

[0030] Firstly, such as Figure 2 As shown, this intelligent defrosting control method can be applied to, for example... Figure 1The controller in the refrigeration system shown includes: a controller, a cold storage unit, and a refrigeration unit; the refrigeration unit includes: a hot gas defrosting valve, a condenser fan, an evaporator fan installed in the evaporator, and an electrically auxiliary defrosting unit; the compressor's exhaust port is connected to the condenser fan, and the compressor's suction port is connected to the evaporator fan; the compressor is also connected to the evaporator fan through a defrosting branch, and a hot gas defrosting valve is installed on the defrosting branch; the intelligent defrosting control method may include, but is not limited to, steps S100 to S400.

[0031] Step S100: Obtain the first operating parameters and initial reference parameters of the refrigeration unit.

[0032] To further explain step S100, the controller has a parameter acquisition function, which acquires the first operating parameters and the initial reference parameters.

[0033] Specifically, the first operating parameter includes four key data categories: evaporator surface temperature, refrigerant inlet pressure, refrigerant outlet pressure, fan operating current, and refrigerant saturation temperature. Specifically, the evaporator surface temperature can be obtained using existing temperature sensors, the refrigerant inlet and outlet pressures can be obtained using existing pressure sensors, and the fan operating current can be obtained using existing current sensors. The corresponding refrigerant saturation temperature is then matched by looking up the pressure value in a pre-configured R32 refrigerant saturation temperature lookup table.

[0034] Specifically, the initial reference parameters include the initial current. It can be understood that before the refrigeration unit is started for the first time, the initial state is recorded, that is, the initial reference parameters under a non-frosting state are recorded, which serve as the basis for subsequent ΔI calculations.

[0035] Therefore, sensor calibration is required before step S100: the pressure sensor, temperature sensor, and current sensor in the refrigeration system must be calibrated to ensure that the data measurement accuracy meets the system requirements.

[0036] In this embodiment of the application, the first operating parameters and initial reference parameters of the refrigeration unit are collected through step S100, thereby laying a data foundation for subsequent thickness estimation processing.

[0037] Step S200: Based on the frost layer estimation model, the thickness of the first frost layer is obtained by performing thickness estimation processing according to the first operating parameters and the initial benchmark parameters; wherein the frost layer estimation model includes: an input layer, a calculation layer and an output layer connected in sequence; the thickness estimation processing includes: data processing, weighted calculation processing and inverse normalization processing performed in sequence; the weighted calculation processing adopts a preset multiple linear regression formula.

[0038] Specifically, such as Figure 3 As shown, Figure 3This is a schematic diagram of the frost layer estimation model provided in one embodiment of this application; the frost layer estimation model 300 has a three-level structure, including an input layer 301, a calculation layer 302, and an output layer 303 connected in sequence. It is understood that the frost layer estimation model 300 is pre-established. Before step S100, the parameters of the frost layer estimation model 300 need to be pre-configured: pressure difference coefficient. Temperature deviation coefficient Decrease coefficient The fitted value of the model constant term C, and the parameter normalization / inverse normalization coefficients. Model coefficient calibration: During the unit installation and commissioning phase, experimental scenarios with different frost thicknesses (0mm, 1mm, 2mm, 3mm, 4mm, 5mm) are manually constructed. Data on ΔP, ΔT, and ΔI under each scenario are collected, and the coefficients are fitted using a multiple linear regression formula to generate a model suitable for this refrigeration unit. , , The C value is stored in the controller. This application does not impose specific restrictions on the establishment process of the frost layer estimation model 300.

[0039] According to some embodiments of this application, step S200 is further described. Step S200: Based on the frost layer estimation model, the thickness is calculated according to the first operating parameters and the initial reference parameters to obtain the first frost layer thickness, including but not limited to steps S210 to S230.

[0040] Step S210: Through the input layer, the normalized pressure difference, temperature deviation value, and current drop magnitude are obtained by data processing based on the first operating parameters and the initial reference parameters.

[0041] The specific data processing steps in this step include the following:

[0042] Step S211: Subtract the refrigerant outlet pressure from the refrigerant inlet pressure to obtain the pressure difference. Specifically, the formula used to calculate the pressure difference is: ΔP = P1 - P2; where ΔP is the pressure difference, P1 is the refrigerant inlet pressure, and P2 is the refrigerant outlet pressure.

[0043] Step S212: Subtract the refrigerant saturation temperature from the surface temperature to obtain the temperature deviation value. Specifically, the formula used to calculate the temperature deviation value is: ΔT = T1 - T2; where ΔT is the temperature deviation value, T1 is the surface temperature of the evaporator, and T2 is the refrigerant saturation temperature.

[0044] Step S213: Subtract the fan operating current from the initial current to obtain the current drop. Specifically, the formula used to calculate the current drop is: ΔI = I0 - I1, where ΔI is the current drop, I0 is the initial current, and I1 is the fan operating current.

[0045] Step S214: Normalize the calculated pressure difference, temperature deviation, and current decrease, respectively, to obtain the normalized pressure difference, temperature deviation, and current decrease. Specifically, the normalization range is 0 to 1, meaning that the normalized pressure difference, temperature deviation, and current decrease values ​​range from 0 to 1. Normalization eliminates the influence of parameter unit differences on the calculation.

[0046] Step S220: Through the calculation layer, the pressure difference, temperature deviation value, and current drop amplitude are input into the preset multiple linear regression formula for weighted calculation to obtain the thickness calculation result.

[0047] In this step, the controller integrates a frost layer thickness estimation algorithm. The calculation layer performs weighted calculations on the parameters (i.e., normalized pressure difference, temperature deviation value, and current drop amplitude) obtained from the input layer using a preset multiple linear regression formula, thereby obtaining the thickness calculation result.

[0048] Specifically, the specific expression for the multiple linear regression formula (also known as the multiple regression model) is as follows:

[0049] ;

[0050] in, This is the result of thickness calculation. It is the pressure differential coefficient. It is the temperature deviation coefficient. C is the descent rate coefficient, and C is the model constant. The pressure difference coefficient, temperature deviation coefficient, descent rate coefficient, and model constant are all obtained by fitting experimental data from more than 30 sets of different frost layer thicknesses and different cold storage loads (20%-100% rated load) to ensure goodness of fit and that the estimation error is ≤0.2mm.

[0051] Step S230: Through the output layer, the thickness calculation result is inversely normalized to obtain the thickness of the first frost layer.

[0052] In this step, specifically, the output layer inversely normalizes the thickness calculation results to obtain the actual thickness of the first frost layer. It also outputs the estimated error range (±0.2mm), providing a precision reference for subsequent defrosting decisions.

[0053] Through step S200, this application can accurately calculate the current frost layer thickness based on the frost layer calculation model and the operating parameters of the refrigeration unit without adding additional hardware equipment. This reduces hardware costs and provides a reliable reference for subsequent dynamic defrosting control.

[0054] Step S300: Determine the first defrosting control mode based on the first comparison result between the first frost layer thickness and the preset thickness threshold; the first defrosting control mode is one of the following: thin frost condition control mode, medium frost condition control mode, and thick frost condition control mode.

[0055] In this step, the preset thickness threshold includes a first threshold and a second threshold, with the second threshold being greater than the first threshold.

[0056] In one embodiment, the first threshold =1mm, second threshold =3mm. This application embodiment sets a preset thickness threshold, including a first threshold and a second threshold, to perform thickness grading on the calculated frost layer thickness, thereby enabling dynamic defrosting control in subsequent processes.

[0057] According to some embodiments of this application, step S300 is further described. Step S300: Determine the first defrosting control mode based on the first comparison result between the first frost layer thickness and the preset thickness threshold, including but not limited to steps S310 to S330.

[0058] Step S310: If the first comparison result is that the frost layer thickness is less than or equal to the first threshold, determine the first defrosting control mode as the thin frost condition control mode.

[0059] Step S320: If the first comparison result is that the frost layer thickness is greater than the first threshold and less than or equal to the second threshold, determine the first defrosting control mode as the medium frost condition control mode.

[0060] Step S330: If the first comparison result is that the frost layer thickness is greater than the second threshold, determine the first defrosting control mode as the thick frost condition control mode.

[0061] Based on the three defined value ranges, the frosting condition is determined, and the defrosting control mode to be used is dynamically determined:

[0062] Specifically, when the first comparison result is the thickness of the first frost layer If the frost layer in the cold storage is determined to be thin frost, the thin frost condition control mode is adopted; in the thin frost condition control mode, defrosting is not activated, and the frost formation is alleviated only by increasing the speed / frequency of the evaporator fan.

[0063] Specifically, when the first comparison result is If the frost layer in the cold storage is determined to be of medium thickness, then a medium-frost control mode will be adopted. In this mode, "R32 refrigerant hot gas defrosting" will be activated. This involves opening the hot gas defrosting valve to introduce high-temperature R32 refrigerant discharged from the compressor into the evaporator. The heat released during the condensation of the R32 refrigerant will be used for defrosting. Simultaneously, the condenser fan frequency will be reduced to minimize heat loss. The defrosting termination condition is "ΔP ≤ 0.1 MPa and T1 ≥ 5℃" to avoid residual frost.

[0064] Specifically, when the first comparison result is If the frost layer in the cold storage is determined to be thick frost, the thick frost control mode will be adopted. In this mode, the "R32 refrigerant hot gas defrosting + electric auxiliary defrosting unit" will be activated. This means that, based on the R32 high-temperature refrigerant condensation and heat dissipation defrosting, the electric auxiliary defrosting unit will be triggered (based on the actual frost thickness). The power of the electrically assisted defrosting unit is dynamically adjusted: When the thickness is between 3mm and 4mm, the auxiliary power used is equal to 50% of the rated power. When the thickness is >4mm, the auxiliary power used is equal to 100% of the rated power to ensure thorough defrosting; defrosting constraint control: if the temperature inside the storage room rises by more than 1℃ during the defrosting process, the auxiliary power of the electric auxiliary defrosting unit is immediately reduced to prioritize the stability of the storage room temperature. The defrosting ends when "ΔP≤0.1MPa and T1≥5℃" to avoid residual frost layer.

[0065] By determining the defrost control mode based on the calculated current frost layer thickness through steps S310 to S330, the subsequent dynamic defrost control process can be reliably and accurately guided.

[0066] Step S400: Determine the target defrosting device from the refrigeration unit according to the first defrosting control mode, and control the target defrosting device to perform dynamic defrosting control processing on the cold storage, so as to realize intelligent defrosting of the cold storage.

[0067] It is understandable that in this step, the specific process of dynamic defrosting control will differ depending on the type of the first defrosting control mode.

[0068] According to some embodiments of this application, step S400 includes, but is not limited to, steps S401 to S404.

[0069] Step S401: When the first defrosting control mode is the thin frost condition control mode, the target defrosting device in the refrigeration unit is determined to be: the evaporator fan.

[0070] Step S402: Increase the frequency of the evaporator fan according to the first preset adjustment ratio to increase airflow disturbance in the cold storage and slow down the frosting speed.

[0071] In this step, the first preset adjustment ratio can be pre-configured according to actual needs, and this application does not impose specific restrictions on the value of the first preset adjustment ratio. The first preset adjustment ratio is used to adjust the fan speed, and the fan speed is positively correlated with the fan frequency; increasing the fan speed is equivalent to increasing the fan frequency.

[0072] Step S403: After a preset cycle, obtain the updated second operating parameters of the refrigeration unit, and perform thickness estimation processing based on the second operating parameters and the initial reference parameters to obtain the updated second frost layer thickness.

[0073] In this step, the preset cycle can be pre-configured according to actual needs, and this application does not impose specific restrictions on the value of the preset cycle. It is understood that the specific process of thickness estimation based on the second operating parameters and the initial reference parameters is the same as the specific process of steps S210 to S230 above, and this application will not repeat it here.

[0074] Step S404: Based on the second comparison result between the second frost layer thickness and the first threshold, determine the second defrosting control mode, and control the target defrosting device to perform dynamic defrosting control processing on the cold storage according to the second defrosting control mode.

[0075] In this step, if the second comparison result is that the second frost layer thickness is less than or equal to the first threshold, the frequency of the fan is maintained; if the second comparison result is that the second frost layer thickness is greater than the first threshold, the second defrosting control mode is determined to be the medium frost condition control mode, and the system is automatically switched to the medium frost condition control mode to control the target defrosting device to perform dynamic defrosting control processing on the cold storage.

[0076] For example, this application implements a thin frost condition control mode. The overall process:

[0077] Step S1: Parameter Acquisition and Calculation: During the operation of the refrigeration unit, the controller acquires the first operating parameters at a set frequency or preset cycle, and calculates the current frost thickness using the frost layer calculation model. When judged as It enters the thin frost working condition control mode at this time.

[0078] Step S2: Control Execution: The controller determines that a thin layer of frost has formed in the cold storage and adopts the thin frost condition control mode: the defrosting program is not started, but the evaporator fan speed is increased according to the first preset adjustment ratio (equivalent to increasing the fan frequency) to enhance airflow disturbance and slow down the frosting speed.

[0079] Step S3: Continuous monitoring: Refresh the second operating parameters at a preset cycle, and calculate the current frost layer thickness based on the second operating parameters and the frost layer estimation model. ,like Not exceeding Maintain the evaporator fan in frequency-increasing mode; if Exceed It automatically switches to the mid-frost operating mode.

[0080] The embodiments of this application implement a dynamic defrosting control mechanism for thin frost through steps S401 to S404.

[0081] According to some embodiments of this application, step S400 includes, but is not limited to, steps S405 to S408.

[0082] Step S405: When the first defrosting control mode is the mid-defrosting operation control mode, the target defrosting device is determined from the refrigeration unit, including: evaporator fan, hot gas defrosting valve, and condenser fan.

[0083] Step S406: Turn off the evaporator fan, open the hot gas defrosting valve and adjust it to the first opening degree, and reduce the frequency of the condenser fan according to the second preset ratio.

[0084] In this step, the first opening can be pre-configured according to the actual situation. This application does not impose specific restrictions on the value of the first opening.

[0085] Specifically, the second preset adjustment ratio can be pre-configured according to actual needs, and this application does not impose specific restrictions on the value of the second preset adjustment ratio. The second preset adjustment ratio is used to adjust the fan speed, and the fan speed is positively correlated with the fan frequency; reducing the fan speed is equivalent to reducing the fan frequency.

[0086] Step S407: After a preset cycle, reacquire the updated second operating parameters of the refrigeration unit, and determine the current surface temperature of the evaporator and the updated pressure difference based on the second operating parameters.

[0087] In this step, the preset cycle can be pre-configured according to actual needs, and this application does not impose specific restrictions on the value of the preset cycle. It is understood that the current surface temperature of the evaporator can be directly obtained from the second operating parameters, and the updated pressure difference can be calculated based on the second operating parameters in step S211, which will not be elaborated here.

[0088] Step S408: When the updated pressure difference is less than or equal to the pressure threshold and the current surface temperature is greater than or equal to the preset temperature threshold, close the hot gas defrosting valve and restart the evaporator fan to complete the defrosting control process.

[0089] For example, this application implements a frost condition control mode ( ). The overall process:

[0090] Step S1: Parameter Acquisition and Calculation: During the operation of the refrigeration unit, the controller acquires the first operating parameters at a set frequency or preset cycle, and calculates the current frost thickness using the frost layer calculation model. When judged as When the system enters the mid-frost control mode, the temperature inside the warehouse must not exceed the constraint threshold.

[0091] Step S2: Initiate hot defrosting:

[0092] Step 1: Turn off the evaporator fan, open the hot gas defrost valve to the first opening degree, introduce the high-temperature R32 refrigerant discharged from the compressor into the evaporator, and use the heat dissipated when the high-temperature R32 refrigerant condenses to defrost.

[0093] Step 2: Adjust the condenser fan to operate at a reduced frequency according to the second preset ratio to reduce the loss of system heat to the outside.

[0094] Step 3: Monitor the changes in the evaporator surface temperature T1 and ΔP according to a preset cycle. Simultaneously update the current frost thickness in real time using a frost layer calculation model. To determine the progress of defrosting.

[0095] Step S3: Defrosting ends: When ΔP is detected to be less than or equal to the set pressure threshold and the surface temperature T1 is greater than or equal to the set temperature threshold, close the hot gas defrosting valve, restart the evaporator fan, and complete the defrosting process.

[0096] The embodiments of this application implement an automatic defrosting control mechanism for medium-thickness frost layers through steps S405 to S408.

[0097] According to some embodiments of this application, step S400 includes, but is not limited to, steps S409 to S413.

[0098] Step S409: When the first defrost control mode is the thick frost condition control mode, the target defrost device is determined from the refrigeration unit, including: evaporator fan, hot gas defrost valve, condenser fan and electric auxiliary defrost unit.

[0099] Step S410: Turn off the evaporator fan, open the hot gas defrosting valve and adjust it to the second opening degree, and reduce the frequency of the condenser fan according to the second preset ratio; the second opening degree is greater than the first opening degree.

[0100] In this step, the second opening is the maximum opening of the hot gas defrosting valve, and the second opening must be greater than the first opening.

[0101] Step S411: Dynamically determine the target power based on the current frost thickness and temperature inside the storage chamber obtained from real-time monitoring, and control the operation of the electric-assisted defrosting unit according to the target power to accelerate the melting of the frost.

[0102] In this step, specifically, the current frost layer thickness... When the frost layer thickness is between 3mm and 4mm, the auxiliary power used is equal to 50% of the rated power, based on the current frost layer thickness. When the temperature is greater than 4mm, the auxiliary power used is equal to 100% of the rated power to ensure thorough defrosting. If the temperature inside the warehouse rises by more than 1°C during the defrosting process, the auxiliary power of the electric auxiliary defrosting unit is immediately reduced, and then restored to the set auxiliary power after the temperature inside the warehouse drops, so as to prioritize the stability of the warehouse temperature.

[0103] Step S412: After a preset cycle, reacquire the updated second operating parameters of the refrigeration unit, and determine the current surface temperature of the evaporator and the updated pressure difference based on the second operating parameters.

[0104] In this process, the preset cycle can be pre-configured according to actual needs, and this application does not impose specific restrictions on the value of the preset cycle. It is understood that the specific process of thickness calculation based on the second operating parameters and the initial reference parameters is the same as the specific process of steps S210 to S230 above, and will not be repeated here.

[0105] Step S413: When the updated pressure difference is less than or equal to the pressure threshold and the current surface temperature is greater than or equal to the preset temperature threshold, the electric auxiliary defrosting unit, the hot gas defrosting valve, and the evaporator fan are closed in sequence to complete the defrosting control process.

[0106] For example, this application implements a thick frost condition control mode. The overall process:

[0107] Step S1: Parameter Acquisition and Calculation: During the operation of the refrigeration unit, the controller acquires the first operating parameters at a set frequency or preset cycle, and calculates the current frost thickness using the frost layer calculation model. When judged as At this time, it enters the thick frost condition control mode.

[0108] Step S2: Initiate hot gas defrosting and electric-assisted defrosting:

[0109] Step 1: Turn off the evaporator fan and open the hot gas defrost valve to the second opening position. Introduce the high-temperature R32 refrigerant discharged from the compressor into the evaporator, utilizing the heat dissipated during the condensation of the R32 refrigerant for defrosting. Adjust the condenser fan to operate at a reduced frequency according to the second preset ratio to minimize heat loss from the system to the outside.

[0110] Step 2: Based on the current frost thickness The specific value is set to control the auxiliary power of the electrically assisted defrosting unit to 50% or 100% of the rated power, and to operate according to the set auxiliary power to accelerate the melting of the frost layer.

[0111] Step 3: Monitor the temperature inside the frost chamber in real time. If the temperature exceeds the constraint threshold, temporarily reduce the electric auxiliary power, and restore the set auxiliary power once the temperature drops. Simultaneously, update the current frost layer thickness every 15 seconds using a thickness estimation model. The auxiliary power of the electrically assisted defrosting unit is dynamically adjusted.

[0112] Step S3: Defrosting ends: When ΔP is detected to be less than or equal to the set pressure threshold and the surface temperature T1 is greater than or equal to the set temperature threshold, the electric auxiliary unit and the hot gas defrosting valve are closed in sequence, and the evaporator fan is restarted to complete the defrosting process.

[0113] The embodiments of this application implement an automatic defrosting control mechanism for thick frost through steps S409 to S413.

[0114] In this embodiment, through steps S100 to S400, during the intelligent defrosting control of the cold storage, the controller first acquires the first operating parameters and initial reference parameters of the refrigeration unit; secondly, based on the frost layer calculation model, the thickness is calculated according to the first operating parameters and initial reference parameters to obtain the first frost layer thickness; without adding additional hardware equipment, the current frost layer thickness can be calculated relatively accurately based on the frost layer calculation model and the operating parameters of the refrigeration unit, reducing hardware costs and providing a reliable reference for subsequent dynamic defrosting control; then, based on the first frost layer thickness... The first comparison result between the frost layer thickness and a preset thickness threshold determines the first defrost control mode. The first defrost control mode is one of the following: thin frost control mode, medium frost control mode, or thick frost control mode. The defrost control mode is determined based on the calculated current frost layer thickness, thereby reliably and accurately guiding the subsequent dynamic defrost control processing. Finally, according to the first defrost control mode, a target defrost device is determined from the refrigeration unit, and the target defrost device is controlled to perform dynamic defrost control processing on the cold storage, achieving intelligent defrosting of the cold storage. This reliably and accurately achieves dynamic defrost operation, enhancing the defrost effect and reducing system energy efficiency. Therefore, the embodiments of this application can reduce hardware costs while reliably and accurately achieving defrost operation, enhancing the defrost effect, and reducing system energy efficiency.

[0115] According to the following embodiments of this application, the intelligent defrosting control method of this application further includes steps S500 to S600.

[0116] Step S500: During the defrosting control process, the temperature inside the cold storage is monitored in real time; if the temperature inside the cold storage exceeds the safe temperature range, the defrosting control process is forcibly terminated.

[0117] If the temperature inside the storage exceeds the safe temperature range during this step, defrosting will be forcibly terminated, and the refrigeration cycle will be restored first.

[0118] The intelligent defrosting control method of this application also provides the following abnormal protection mechanism: if the R32 refrigerant pressure is detected to exceed the safety threshold during the defrosting process, the hot gas valve is immediately closed and the pressure relief procedure is initiated; if the calculation error of the thickness estimation model exceeds 0.5mm, a sensor calibration reminder is triggered.

[0119] Step S600: After the defrost control process ends, generate defrost data records.

[0120] In this step, after each defrosting, the frost thickness, defrosting time, energy consumption, storage temperature fluctuation curve, and actual adaptability data of k1, k2, and k3 during the defrosting process are automatically recorded to form a historical database for optimizing subsequent defrosting parameters; data storage is completed.

[0121] This application embodiment implements an anomaly handling mechanism through steps S500 to S600 to maintain the normal operation of the refrigeration system, and also implements a data processing mechanism to optimize the intelligent defrosting control mechanism in the future.

[0122] It is understood that the refrigeration system provided in this application embodiment can refer to a large cold chain warehouse, or it can refer to an energy-saving refrigerator or energy-saving freezer. That is to say, the intelligent defrosting control method provided in this application embodiment can also be applied to energy-saving refrigerators and energy-saving freezers.

[0123] It is important to emphasize that current mainstream defrosting technologies have significant drawbacks: Firstly, the timing of defrosting is often inefficient, employing either "fixed-time defrosting" or "single-temperature triggering" without considering the actual thickness of the frost layer. Defrosting too early with thin frost wastes energy, while delaying defrosting with thick frost leads to heat exchange failure, especially when cold storage loads fluctuate significantly. Secondly, defrosting methods are energy-intensive, with traditional electric defrosting requiring additional electricity and causing a rapid temperature rise in the evaporator during heating, resulting in temperature fluctuations in the cold storage. While some hot-air defrosting technologies utilize the condensation heat of R32 refrigerant, this is not matched to the frost thickness, leading to incomplete defrosting with thick frost and excessive heat consumption with thin frost. Thirdly, frost detection is costly, as existing detection methods (such as infrared sensors and weight sensors) require additional hardware, increasing equipment costs and maintenance complexity. Finally, "indirect calculations" often rely solely on refrigerant temperature without considering R32 temperature. The low accuracy of refrigerant pressure and fan operating parameters makes it difficult to precisely guide defrosting. Existing technologies struggle to balance the relationship between defrosting effectiveness, energy consumption, and storage temperature stability, necessitating a low-cost and accurate intelligent defrosting solution based on the characteristics of R32 refrigerant. This application, "Dynamic Frost Sensing Based on Multi-Parameter Coupling of R32 Refrigerant + On-Demand Matching Defrosting Method," achieves intelligent defrosting through algorithm optimization without adding complex hardware. Specifically, the intelligent defrosting control method provided in this application relies on dynamic sensing of frost thickness and differentiated defrosting strategies to achieve on-demand defrosting. This avoids energy waste caused by traditional fixed defrosting or excessive defrosting, significantly reducing the overall energy consumption of the unit. It also solves the problems of accidental defrosting of thin frost and incomplete defrosting of thick frost in traditional technologies, effectively reducing the risk of evaporator blockage and compressor overload, and extending the service life of the unit. During the defrosting process, temperature constraint control can prevent sudden rises and falls in storage temperature, effectively ensuring the storage quality of temperature-sensitive goods such as fresh produce and pharmaceuticals. At the same time, only a few sensors need to be added to the existing unit, without complex hardware modifications, resulting in low modification costs. It can be quickly adapted to mainstream R32 refrigerant cold storage units such as 1.5P and 2P, and has strong versatility. In addition, the entire process automatically completes the "perception-decision-execution" closed loop through algorithms without human intervention, greatly reducing the workload of operation and maintenance, improving operation and maintenance efficiency, and can flexibly cope with complex cold storage scenarios such as high humidity and dynamic loads, adapting to the storage needs of multiple fields such as food, pharmaceuticals, and cold chain logistics. Among them, the introduction of a thickness estimation model using multiple regression linear equations improves the accuracy of frost layer thickness estimation, providing core technical support for precise defrosting.

[0124] like Figure 4 As shown, the present invention also provides a controller, comprising:

[0125] The processor 401 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0126] The memory 402 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 402 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 402 and called and executed by the processor 401 using the intelligent defrosting control method of the embodiments of this application.

[0127] Input / output interface 403 is used to implement information input and output;

[0128] The communication interface 404 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0129] Bus 405 transmits information between various components of the device (e.g., processor 401, memory 402, input / output interface 403, and communication interface 404);

[0130] The processor 401, memory 402, input / output interface 403 and communication interface 404 are connected to each other within the device via bus 405.

[0131] This application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-described intelligent defrosting control method.

[0132] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0134] The above provides a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by this application.

Claims

1. A method of intelligent defrosting control, the method comprising: The application relates to a controller applied to a refrigeration system, wherein the refrigeration system comprises a controller, a cold storage and a refrigeration unit; the refrigeration unit comprises an evaporator, a compressor, a hot gas defrosting valve, a condensing fan, an evaporating fan arranged in the evaporator and an electric auxiliary defrosting unit; an exhaust port of the compressor is connected with the condensing fan, and an air inlet of the compressor is connected with the evaporating fan; the compressor is further connected with the evaporating fan through a defrosting branch, and the defrosting branch is provided with the hot gas defrosting valve. The method comprises the following steps: obtaining first operation parameters and initial reference parameters of the refrigeration unit; wherein the first operation parameters comprise a surface temperature of the evaporator, a refrigerant inlet pressure, a refrigerant outlet pressure, a fan operation current and a refrigerant saturation temperature; the initial reference parameters are recorded in a frost-free state, and the initial reference parameters comprise an initial current; performing thickness calculation processing on the first operation parameters and the initial reference parameters based on a frost layer calculation model to obtain a first frost layer thickness; wherein the frost layer calculation model comprises an input layer, a calculation layer and an output layer which are sequentially connected; the thickness calculation processing comprises data processing, weighting calculation processing and inverse normalization processing which are sequentially performed; the weighting calculation processing adopts a preset multiple linear regression formula; determining a first defrosting control mode according to a first comparison result between the first frost layer thickness and a preset thickness threshold value; the first defrosting control mode is one of a thin frost working condition control mode, a medium frost working condition control mode and a thick frost working condition control mode; determining a target defrosting device from the refrigeration unit according to the first defrosting control mode, controlling the target defrosting device to perform dynamic defrosting control processing on the cold storage and realizing intelligent defrosting on the cold storage.

2. The intelligent defrost control method of claim 1, wherein, The method that performs thickness calculation processing on the first operation parameters and the initial reference parameters based on the frost layer calculation model to obtain the first frost layer thickness comprises the following steps: performing data processing on the first operation parameters and the initial reference parameters through the input layer to obtain a normalized pressure difference, a temperature deviation value and a current drop amplitude; wherein the data processing comprises the following steps: subtracting the refrigerant outlet pressure from the refrigerant inlet pressure to obtain the pressure difference; subtracting the refrigerant saturation temperature from the surface temperature of the evaporator to obtain the temperature deviation value; subtracting the fan operation current from the initial current to obtain the current drop amplitude; and performing normalization on the calculated pressure difference, temperature deviation value and current drop amplitude respectively to obtain the normalized pressure difference, temperature deviation value and current drop amplitude; inputting the pressure difference, temperature deviation value and current drop amplitude into a preset multiple linear regression formula through the calculation layer to perform weighting calculation processing and obtain a thickness calculation result; performing inverse normalization processing on the thickness calculation result through the output layer to obtain the first frost layer thickness.

3. The intelligent defrost control method of claim 1, wherein, The preset thickness threshold value comprises a first threshold value and a second threshold value, and the second threshold value is greater than the first threshold value; the method that determines the first defrosting control mode according to the first comparison result between the first frost layer thickness and the preset thickness threshold value comprises the following steps: In a case where the first comparison result is that the frost layer thickness is less than or equal to the first threshold value, the first defrosting control mode is determined as a thin frost working condition control mode; In a case where the first comparison result is that the frost layer thickness is greater than the first threshold value and less than or equal to a second threshold value, the first defrosting control mode is determined as a medium frost working condition control mode; In a case where the first comparison result is that the frost layer thickness is greater than the second threshold value, the first defrosting control mode is determined as a thick frost working condition control mode.

4. The intelligent defrost control method of claim 3, wherein, The target defrosting device is determined from the refrigerating unit according to the first defrosting control mode, and the target defrosting device is controlled to perform a dynamic defrosting control process on the cold storage. In a case where the first defrosting control mode is the thin frost working condition control mode, the target defrosting device is determined as the evaporative fan from the refrigerating unit. The evaporative fan is frequency-raised according to a first preset adjustment ratio to increase air flow disturbance in the cold storage and delay frosting speed. After a preset period, updated second operating parameters of the refrigerating unit are obtained, and the thickness calculation process is performed according to the second operating parameters and the initial reference parameters to obtain an updated second frost layer thickness. A second defrosting control mode is determined according to a second comparison result between the second frost layer thickness and the first threshold value, and the target defrosting device is controlled to perform a dynamic defrosting control process on the cold storage according to the second defrosting control mode.

5. The intelligent defrost control method of claim 2, wherein, The target defrosting device is determined from the refrigerating unit according to the first defrosting control mode, and the target defrosting device is controlled to perform a dynamic defrosting control process on the cold storage. In a case where the first defrosting control mode is the medium frost working condition control mode, the target defrosting device includes an evaporative fan, a hot gas defrosting valve, and a condensing fan. The evaporative fan is closed, the hot gas defrosting valve is opened and adjusted to a first opening degree, and the condensing fan is frequency-reduced according to a second preset ratio. After a preset period, updated second operating parameters of the refrigerating unit are obtained, and the thickness calculation process is performed according to the second operating parameters and the initial reference parameters to obtain an updated second frost layer thickness. In a case where the updated pressure difference is less than or equal to a pressure threshold value and the current surface temperature is greater than or equal to a preset temperature threshold value, the hot gas defrosting valve is closed, and the evaporative fan is restarted to complete the defrosting control process.

6. The intelligent defrost control method of claim 5, wherein, The target defrosting device is determined from the refrigerating unit according to the first defrosting control mode, and the target defrosting device is controlled to perform a dynamic defrosting control process on the cold storage. In a case where the first defrosting control mode is the thick frost working condition control mode, the target defrosting device includes an evaporative fan, a hot gas defrosting valve, a condensing fan, and an electric auxiliary defrosting unit. The evaporative fan is closed, the hot gas defrosting valve is opened and adjusted to a second opening degree, and the condensing fan is frequency-reduced according to a second preset ratio; the second opening degree is greater than the first opening degree. According to the current frost thickness and the temperature in the cold storage obtained by real-time monitoring, a target power is dynamically determined, and the electric auxiliary defrosting unit is controlled to operate according to the target power to accelerate the melting of the frost; After a preset period of time, updated second operating parameters of the refrigerating unit are re-acquired, and a current surface temperature of the evaporator and an updated pressure difference are determined according to the second operating parameters; In a case where the updated pressure difference is less than or equal to a pressure threshold value and the current surface temperature is greater than or equal to a preset temperature threshold value, the electric auxiliary defrosting unit, the hot gas defrosting valve and the evaporator fan are sequentially closed, and the defrosting control process is completed.

7. The intelligent defrost control method of any of claims 4-6, wherein, The method further comprises: During the defrosting control process, the temperature in the cold storage is detected in real time, and in a case where the temperature in the cold storage exceeds a safe temperature range, the defrosting control process is forcibly ended; After the defrosting control process is ended, defrosting data records are generated.

8. A refrigeration system characterized by, The method comprises: a controller, a cold storage and a refrigerating unit; the refrigerating unit comprises an evaporator, a compressor, a hot gas defrosting valve, a condensing fan, an evaporating fan arranged in the evaporator and an electric auxiliary defrosting unit; an exhaust port of the compressor is connected with the condensing fan, and a suction port of the compressor is connected with the evaporating fan; the compressor is further connected with the evaporating fan through a defrosting branch, and the hot gas defrosting valve is arranged on the defrosting branch; the controller can execute the intelligent defrosting control method according to any one of claims 1 to 7.

9. A controller characterized by comprising: The controller comprises at least one processor and a memory connected with the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the intelligent defrosting control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to execute the intelligent defrosting control method according to any one of claims 1 to 7.

Images