Energy-saving control method and system of intelligent cabinet, storage medium and program product

Abstract

The invention provides an energy-saving control method and system of an intelligent cabinet, a storage medium and a program product, and relates to the technical field of refrigerator energy conservation. The method comprises the steps that a thermal inertia filtering coefficient is updated according to an opening and closing state signal of a refrigerator door; resetting the thermal inertia filtering coefficient to be a first preset value in an opening state; iteratively converging the thermal inertia filtering coefficient from a first preset value to a second preset value based on an attenuation step length corresponding to the duration of the opening state when the opening state is converted into the closing state; inputting the real-time air temperature in the refrigerator compartment and the current thermal inertia filtering coefficient into a preset temperature filtering model to obtain a virtual food temperature at the current moment; if the current time is within the pre-cooling time window, the difference value between the reference set temperature and the pre-cooling compensation value is set as the final target temperature; and according to the temperature difference value between the virtual food temperature and the final target temperature, a frequency adjusting instruction for driving the compressor to operate is generated. By implementing the method, the overall energy consumption of equipment is reduced.

Description

Technical Field

[0001] This application relates to the field of energy-saving technology for refrigerators, and in particular to an energy-saving control method, system, storage medium, and program product for an intelligent refrigerator. Background Technology

[0002] With the popularization of energy conservation and emission reduction concepts and the iterative updates of smart home appliance technology, the energy efficiency level of smart freezers, as high-energy-consuming appliances that operate around the clock in homes or shops, has become a key indicator for measuring product competitiveness. Users not only expect freezers to provide a stable low-temperature environment to ensure the quality of food storage, but also urgently hope that the equipment can minimize power consumption while maintaining cooling performance.

[0003] The relevant technology primarily relies on air temperature sensors within the freezer compartment for feedback regulation. By placing NTC temperature sensors within the refrigerator or freezer compartment, the controller collects real-time air temperature readings from the sensors and compares them to the user-set target stop and start temperatures. When the detected air temperature exceeds the start-up threshold, the controller determines that cooling is required and outputs a command to start the compressor or increase its speed; when the air temperature drops to the stop threshold, it controls the compressor to stop or maintain low-speed operation. Through this direct feedback mechanism based on real-time air temperature values, the system ensures that the ambient temperature within the compartment remains within the set range.

[0004] However, the air inside a freezer compartment has a relatively low specific heat capacity, while the stored food has a relatively high specific heat capacity, resulting in a significant difference in their thermal inertia. When users frequently open and close the door or briefly retrieve items, the influx of warm outside air causes a rapid and dramatic increase in the air temperature inside the compartment. However, the internal temperature of the food itself does not actually change significantly and remains within a safe preservation range. Because the relevant technology primarily responds to air temperature changes, this instantaneous temperature rise may be misinterpreted by the system as a huge cooling load, causing the compressor to immediately operate at high frequency and high power. This over-response to a non-essential heat load results in the compressor consuming a large amount of unnecessary energy when the food does not require strong cooling, increasing the overall energy consumption of the freezer. Summary of the Invention

[0005] This application provides an energy-saving control method, system, storage medium, and program product for intelligent cabinets, which addresses the problem of reducing ineffective cooling work caused by instantaneous fluctuations in air temperature and lowering the energy consumption of the equipment.

[0006] In a first aspect, this application provides an energy-saving control method for an intelligent freezer, applied to an energy-saving control system for an intelligent freezer, the method comprising: The system monitors the opening and closing status signal of the refrigerator door in real time and updates the thermal inertia filter coefficient based on the opening and closing status signal. The thermal inertia filter coefficient is a variable used to adjust the temperature following speed. If the switch status signal indicates an on state, then the thermal inertia filter coefficient is reset to a first preset value; If the switch status signal indicates a change from an on state to a off state, the duration of the current on state is obtained, and the thermal inertia filter coefficient is iteratively converged from the first preset value to the second preset value based on the attenuation step size corresponding to the duration. The real-time air temperature inside the refrigerator compartment and the current thermal inertia filter coefficient are input into a preset temperature filter model to calculate the virtual food temperature at the current moment. If the current system time is within the pre-cooling time window before the high-frequency usage period predicted by the user habit model, the difference between the baseline set temperature and the pre-cooling compensation value is set as the final target temperature. The user habit model is constructed based on the door opening frequency distribution within the historical time window. The frequency adjustment command for driving the compressor is generated based on the temperature difference between the virtual food temperature and the final target temperature.

[0007] By employing the above technical solution, the system captures the opening and closing actions of the refrigerator door in real time and dynamically adjusts the thermal inertia filter coefficient accordingly. This coefficient is used to transform drastically fluctuating air temperature into a more stable virtual food temperature. Combined with a pre-cooling strategy based on user habit prediction, the system no longer controls the compressor solely based on instantaneous air temperature fluctuations, but rather adjusts it based on a virtual temperature that better reflects the true thermal state of the food. This method, to a certain extent, shields the control logic from the interference of hot air rushing in due to door opening and closing, reduces frequent compressor starts and stops or unnecessary high-frequency cooling due to sudden increases in air temperature, and lowers the overall energy consumption of the equipment.

[0008] In some embodiments, the step of iteratively converging the thermal inertia filter coefficient from the first preset value to the second preset value based on the decay step size corresponding to the duration specifically includes: The decay step size is selected from a preset time-step mapping table based on the duration, and the duration is negatively correlated with the decay step size. Within each preset control cycle, the current thermal inertia filter coefficient is updated by decreasing according to the attenuation step size until the thermal inertia filter coefficient is equal to the second preset value.

[0009] By adopting the above technical solution, the system establishes a mapping relationship between the duration of door opening and the attenuation step of the filter coefficient. This allows the thermal inertia filter coefficient to adaptively match the degree of cold energy loss during its recovery from the door-opening disturbance state to a stable state. For door opening behaviors of different durations, the system can apply differentiated temperature smoothing forces, ensuring that the convergence curve of the virtual food temperature neither lags behind the actual cooling demand nor introduces noise from the air temperature due to excessive recovery. This guarantees that the compressor's operating frequency accurately matches the actual load demand after the door is closed, reducing energy waste caused by excessive control signals.

[0010] In some embodiments, the step of constructing the user habit model based on the door opening frequency distribution within a historical time window specifically includes: Record the number of times the door is opened in each time period within a preset number of days in the past, and generate a histogram of door opening frequency; The sliding window algorithm is used to identify continuous high-frequency regions in the door opening frequency histogram, and these continuous high-frequency regions are marked as the high-frequency usage periods. The time period of a preset number of minutes before the start time corresponding to the high-frequency usage period is marked as the pre-cooling time window.

[0011] By adopting the above technical solution, the system performs statistical and sliding window analysis on historical door opening data, accurately identifies users' high-frequency usage periods, and locks the pre-cooling time window accordingly. This allows the freezer to shift from passive response to active management, pre-storing cold during low-energy periods before the peak heat load arrives. This strategy not only avoids forcing the compressor to operate at a low energy efficiency ratio due to drastic temperature fluctuations during high-frequency door opening, but also smooths the compressor's operating curve, improving the overall operating energy efficiency while ensuring the cooling effect.

[0012] In some embodiments, after the step of inputting the real-time air temperature inside the refrigerator compartment and the current thermal inertia filter coefficient into a preset temperature filter model to calculate the virtual food temperature at the current moment, the method further includes: If the current system time is during the high-frequency usage period, the reference set temperature is directly set to the final target temperature; If the current system time is not within the pre-cooling time window and the high-frequency usage period at the same time, then the energy-saving target temperature including energy-saving compensation is set as the final target temperature.

[0013] By adopting the above technical solution, the system dynamically switches between the baseline set temperature, pre-cooling target temperature, and energy-saving target temperature based on the matching results between the current time and the user's habit model. During low-load periods when pre-cooling is not required and usage is not frequent, the system automatically increases the target temperature setpoint to reduce the temperature difference between the room and the outside, thereby directly reducing the rate of cold air leakage from the cabinet. This time-segmented temperature management strategy, while ensuring the quality of food during high-frequency usage periods, minimizes the cold air loss and compressor work during long standby cycles, achieving refined energy-saving operation around the clock.

[0014] In some embodiments, after the step of the switch state signal indicating a change from an on state to a off state, the method further includes: Collect dynamic response data of temperature inside the smart freezer to cooling power; The ratio of temperature change rate to input power is calculated based on the dynamic response data to obtain the load thermal inertia coefficient characterizing the current cargo loading state; The rated current reference of the evaporator fan is corrected based on the load thermal inertia coefficient, and the real-time collected stator current of the fan is compared with the corrected rated current reference to generate the evaporator blockage index after removing load interference. The duration required for defrosting is predicted based on the evaporator blockage index, and the required cooling capacity compensation value for the smart freezer to maintain the duration is calculated. When the current latent heat stored in the phase change energy storage module is greater than the required cooling capacity, a defrosting-coordinated cooling release control command is generated.

[0015] By adopting the above technical solution, the system accurately quantifies the degree of evaporator blockage by combining load thermal inertia and fan current characteristics, and introduces the latent heat storage of the phase change energy storage module as a constraint condition for defrosting decisions. Cooperative control is only activated when there is a genuine need for defrosting and the energy storage module has sufficient cooling capacity to compensate for the loss of heat. This logic not only avoids ineffective defrosting heating energy consumption due to misjudgment of frost thickness, but more importantly, it utilizes the cooling capacity of the phase change material to cover the cooling gap during defrosting, preventing the defrosting heat from causing a rise in compartment temperature. This eliminates the need for the compressor to perform additional high-energy-consumption work to lower the compartment temperature after defrosting.

[0016] In some embodiments, the step of correcting the rated current reference of the evaporator fan based on the load thermal inertia coefficient, and comparing the real-time collected fan stator current with the corrected rated current reference to generate an evaporator blockage index after removing load interference, specifically includes: The load thermal inertia coefficient is converted into a flow resistance compensation factor based on a preset load-flow resistance mapping function. The flow resistance compensation factor is used to characterize the degree of gain of cargo accumulation on the air duct circulation resistance. The rated current reference of the evaporator is weighted and calculated based on the flow resistance compensation factor to obtain the dynamic current threshold under the current load condition; The absolute value of the difference between the stator current of the fan and the dynamic current threshold is calculated, and the absolute value of the difference is divided by the dynamic current threshold for normalization to obtain the evaporator blockage index. The evaporator blockage index is used to quantitatively characterize the rate of change in wind resistance caused only by the accumulation of frost.

[0017] By adopting the above technical solution, the system derives a flow resistance compensation factor using the load thermal inertia coefficient and dynamically corrects the rated current reference of the fan. This separates the wind resistance change caused by cargo accumulation from the total wind resistance, allowing the calculation of the evaporator blockage index caused only by frost accumulation. This method reduces the interference of cargo load on defrosting judgment and ensures the accuracy of defrosting timing. By eliminating "false defrosting" triggered by cargo blocking the airflow, the system reduces the number of ineffective heater starts and subsequent recooling energy consumption, ensuring that each defrosting operation directly contributes to the recovery of heat exchange efficiency.

[0018] In some embodiments, after the step of generating a defrost-coordinated cooling release control command when the current latent heat stored in the phase change energy storage module is greater than the cooling capacity compensation requirement, the method further includes: In response to the defrosting and cooling control command, the airflow system is controlled to enter the heat shield defrosting mode. In the heat shield defrosting mode, the defrosting heater operates and the phase change energy storage module releases cold to the refrigerator compartment. Calculate the rate of temperature rise of the virtual food during the duration; The cooling air volume of the phase change energy storage module is dynamically adjusted according to the temperature rise rate, so that the temperature of the virtual food is maintained within a preset safe threshold range.

[0019] By adopting the above technical solution, the system monitors the temperature rise rate of virtual food in real time during the heat shield defrost mode and dynamically adjusts the airflow of the phase change energy storage module accordingly. This closed-loop control creates a precise cold barrier within the compartment while the defrost heater is operating, forcibly locking the food temperature within a safe range. This mechanism effectively counteracts the diffusion of defrost heat into the compartment, eliminating the need for the compressor to start high-intensity recooling after defrosting, reducing the impact of the defrosting process on the compartment's thermal environment, and achieving deep energy savings in the defrosting process.

[0020] Secondly, this application provides an intelligent freezer energy-saving control system, the system comprising: one or more processors and a memory; The memory is coupled to the one or more processors. The memory is used to store computer program code, which includes computer instructions. The one or more processors call the computer instructions so that the system can implement the energy-saving control method for an intelligent cabinet provided in the above embodiments, which will not be described in detail here.

[0021] Thirdly, this application provides a computer-readable storage medium including instructions that, when executed on an intelligent freezer energy-saving control system, enable the system to implement an energy-saving control method for an intelligent freezer provided in the above embodiments, which will not be elaborated further here.

[0022] Fourthly, this application provides a computer program product that, when running on an intelligent freezer energy-saving control system, enables the system to implement an energy-saving control method for an intelligent freezer provided in the above embodiments, which will not be elaborated here.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. The system monitors the refrigerator door's opening and closing status and duration, dynamically adjusting the thermal inertia filter coefficient to construct a virtual food temperature model that reflects the true thermal state of the food. This mechanism uses a variable coefficient filtering algorithm to smooth the instantaneous fluctuations in the air temperature inside the refrigerator compartment, ensuring that the compressor's frequency adjustment commands are directly correlated with the relatively sluggish changes in the virtual food temperature, rather than the air temperature with its lower heat capacity. This approach matches the refrigeration system's response logic with the thermal inertia characteristics of the actual load, reducing frequent compressor frequency adjustments or excessive work triggered by sudden changes in air temperature, and improving the system's energy efficiency under dynamic disturbances.

[0024] 2. The system constructs a user habit model based on historical door opening frequency data and implements a multi-stage temperature management strategy including pre-cooling, high-frequency use, and energy-saving maintenance. By identifying high-frequency use periods and performing pre-cooling compensation in the preceding time window, the system shifts peak cooling work to lower-load periods with higher energy efficiency, thus suppressing temperature fluctuations during high-frequency door opening. Simultaneously, it automatically increases the target set temperature during inactive periods, reducing the temperature difference between the room and the outside. This method optimizes the matching degree between cooling supply and user demand in the time dimension, reducing the overall cooling leakage rate and average operating power consumption.

[0025] 3. The system incorporates the load thermal inertia coefficient to correct the fan's rated current reference, separating the wind resistance change caused by cargo accumulation from the total flow resistance. This allows for the calculation of the evaporator blockage index, which only characterizes frost accumulation, improving the accuracy of defrosting timing. Upon entering defrost mode, the system utilizes the latent heat storage of the phase change energy storage module for cooling compensation and dynamically adjusts the released airflow based on the virtual food temperature rise rate. This mechanism creates a cooling barrier within the compartment while defrosting and heating, suppressing the impact of defrosting heat on food temperature and reducing the recooling load after defrosting. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating an energy-saving control method for an intelligent cabinet according to an embodiment of this application; Figure 2 This is a schematic diagram of a process for defrosting control in the intelligent freezer energy-saving control system of this application embodiment; Figure 3 This is a schematic diagram of the physical device structure of an intelligent freezer energy-saving control system in the embodiments of this application. Detailed Implementation

[0027] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0029] The following describes the process of the method provided in this implementation. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating an energy-saving control method for an intelligent cabinet in an embodiment of this application.

[0030] S101. Real-time acquisition and monitoring of the refrigerator door's opening and closing status signal, and updating the thermal inertia filter coefficient based on the opening and closing status signal.

[0031] Among them, the switch status signal refers to the electrical signal used to provide feedback on whether the refrigerator door is currently open or closed; the thermal inertia filter coefficient is a variable that adjusts the temperature following speed, used to convert drastically fluctuating air temperature into a gradually changing virtual food temperature.

[0032] This step is executed continuously throughout the entire operation of the smart freezer after it is powered on, regardless of whether the refrigerator door is stationary and closed, frequently opened and closed, or continuously open. The application scenarios cover various freezer operation scenarios, including daily household use and frequent food retrieval in shops. The core function is to capture the real-time impact of changes in the door's status on the internal thermal environment.

[0033] Specifically, the intelligent freezer energy-saving control system continuously collects door opening and closing status data through detection components such as door magnetic sensors and infrared sensors deployed at the door hinges or door frame edges, forming a continuous opening and closing status signal stream. The system analyzes this signal stream in real time to determine whether the door maintains its original state (open or closed) or undergoes a state switch (open to closed or closed to open).

[0034] In some embodiments, this step can be implemented by combining a door magnetic sensor with signal analysis logic: Optionally, the system can implement the following steps: Activate the door magnetic sensor to collect the contact status data between the refrigerator door and the door frame at a frequency of 10ms / time. When the door is open, the sensor outputs a high-level signal and when it is closed, it outputs a low-level signal. The system's signal processing module receives the level signal output by the sensor, filters out signal noise with a duration of less than 50ms caused by slight shaking of the door, and generates a stable on / off state signal. Determine the state type based on the filtered signal. If it is an open state or a state switching trigger signal, trigger the initial update command of the thermal inertia filter coefficient. If it is a closed state, keep the current filter coefficient unchanged. It can be understood that when the refrigerator door is opened, hot air from outside rushes into the cabinet, causing the air temperature inside the cabinet to fluctuate instantaneously. After the door is closed, the temperature inside the cabinet needs to gradually return to the set temperature. Different on / off states have different requirements for temperature regulation. Therefore, the system can dynamically update the thermal inertia filter coefficient based on the analysis results. By adjusting this coefficient, subsequent temperature calculations can adapt to the changes in the thermal environment under the current door state, avoiding false responses of the refrigeration system due to instantaneous fluctuations in air temperature.

[0035] S102. Reset the thermal inertia filter coefficient to the first preset value.

[0036] The first preset value refers to the initial value of the thermal inertia filter coefficient that the system pre-sets for the refrigerator door opening state. This value has been experimentally calibrated and can adapt to the scenario of rapid air temperature change caused by the influx of hot air when the door is opened.

[0037] Specifically, during continuous monitoring of the switch status signals, once the system detects a signal indicating an open state (including newly opened and continuously open), it immediately performs a filter coefficient reset operation. This is because when the refrigerator door is opened, hot outside air rapidly enters the cabinet, causing the air temperature inside, especially near the door, to rise sharply in a short period. If the filter coefficient from the previous closed state is used, subsequent virtual food temperature calculations may not respond promptly to changes in the thermal environment, resulting in a significant deviation between the calculated value and the actual food temperature. The first preset value is a larger value specifically set for the open state, enhancing the temperature filtering model's sensitivity to changes in air temperature, allowing the virtual food temperature to quickly reflect changes in the heat load inside the cabinet after the door is opened.

[0038] Optionally, the system can pre-store a first preset value parameter library in the memory, which contains first preset values ​​corresponding to different ambient temperatures (5℃-35℃). For example, the first preset value is set to 0.8 in an environment of 25℃. When the switch status signal indicates that the switch is in the open state, the system simultaneously collects real-time data from the ambient temperature sensor outside the cabinet. Based on the collected ambient temperature, the system matches the corresponding first preset value from the parameter library and directly replaces the current thermal inertia filter coefficient with the matched value.

[0039] S103. Select the decay step size from the preset time-step mapping table based on the duration of the current activation state.

[0040] Among them, the duration of the current open state refers to the time interval between the triggering of the refrigerator door open signal and the triggering of the close signal; the attenuation step size refers to the decrease of the thermal inertia filter coefficient in each preset control cycle; the time-step mapping table is a pre-set data table that records the correlation between the opening duration and the corresponding attenuation step size, and the duration and the attenuation step size are negatively correlated.

[0041] Specifically, when the system detects that the switch status signal changes from open to closed, it calculates the duration of the door opening – by recording the system time triggered by the open signal and the system time triggered by the close signal, the difference between the two is the duration of the opening.

[0042] Because longer opening times result in more hot air entering the cabinet and greater loss of cold air, the filter coefficient needs to be adjusted more slowly to ensure the accuracy of the virtual food temperature calculation. Conversely, shorter opening times result in less cold air loss, allowing the filter coefficient to recover to a stable state more quickly. Therefore, the system queries a preset time-step mapping table, which has been configured with different decay step sizes for different durations based on experimental data. For example, an opening time of less than 10 seconds corresponds to a decay step size of 0.1, 10-30 seconds corresponds to 0.05, and more than 30 seconds corresponds to 0.02. The system matches the corresponding decay step size to the calculated duration.

[0043] In some embodiments, when the system detects an on state signal, it records and stores the current system timestamp T1; when it detects a off state signal, it records the current system timestamp T2, calculates T2-T1 to obtain the duration t of the current on state; it calls a preset time-step mapping table, which is divided into four intervals: [0,10s), [10s,30s), [30s,60s), and [60s,+∞), with corresponding decay step sizes of 0.1, 0.05, 0.03, and 0.01, respectively. Based on the interval to which the calculated t belongs, the corresponding decay step size is selected.

[0044] S104. In each preset control cycle, the current thermal inertia filter coefficient is updated by decreasing according to the decay step size until the thermal inertia filter coefficient is equal to the second preset value.

[0045] Among them, the preset control cycle refers to the fixed time interval set by the system for updating the thermal inertia filter coefficient; the second preset value refers to the target value of the thermal inertia filter coefficient set by the system in advance for the stable operation state after the refrigerator door is closed. This value is less than the first preset value and can achieve stable temperature filtering; the decreasing update refers to the update method of gradually reducing the thermal inertia filter coefficient according to the attenuation step size.

[0046] Specifically, after obtaining the attenuation step size, the system initiates a decreasing update process for the filter coefficient according to a preset control cycle (e.g., 5 seconds / cycle). The initial filter coefficient is the first preset value when the door opens. In each control cycle, the system subtracts the attenuation step size from the current filter coefficient to obtain the updated filter coefficient, which is then used as the initial value for the next cycle. This process continues until the updated filter coefficient equals the second preset value (e.g., 0.2), at which point the update stops.

[0047] This gradual reduction method is used because after the door is closed, the air temperature inside the cabinet will gradually decrease under the action of the refrigeration system, but the temperature of the food changes relatively slowly. By slowly adjusting the filter coefficient, the virtual food temperature can accurately reflect the real temperature change trend of the food, avoiding the distortion of virtual temperature calculation caused by the rapid decrease of the coefficient, and thus ensuring that the compressor's operating frequency adjustment meets the actual refrigeration needs.

[0048] In some embodiments, the system can obtain a preset control cycle of 5 seconds and a second preset value of 0.2, and obtain the attenuation step size s selected in S103; start a cycle timer, trigger an update command every 5 seconds, and each time it is triggered, read the current thermal inertia filter coefficient C, calculate Cs to obtain the updated coefficient C'; store C' as the new current filter coefficient, and at the same time determine whether C' is equal to 0.2. If it is not equal, continue to wait for the next control cycle; if it is equal, stop the timer and end the update process.

[0049] S105. Input the real-time air temperature inside the refrigerator compartment and the current thermal inertia filter coefficient into the preset temperature filter model to calculate the virtual food temperature at the current moment.

[0050] Among them, the refrigerator compartment refers to the enclosed space in the smart freezer used to store food; the preset temperature filtering model refers to the algorithm model pre-built by the system to calculate the virtual food temperature by combining air temperature and thermal inertia filtering coefficient. This model has been experimentally calibrated and can simulate the temperature change pattern of food; the virtual food temperature refers to the temperature value calculated by the temperature filtering model that can reflect the real thermal state of the food, which is different from the instantaneous fluctuation of air temperature.

[0051] Specifically, the system uses high-precision temperature sensors (such as NTC temperature sensors) deployed inside the refrigerator compartment to collect air temperature data at a fixed frequency, filtering out abnormal fluctuations caused by sensor noise to obtain a stable real-time air temperature. Simultaneously, the system acquires the thermal inertia filter coefficient updated in step S102 or S104—this coefficient has been dynamically adjusted based on the door's opening and closing status and duration to adapt to the current thermal environment. These two data points are then input into a preset temperature filtering model. The model uses a weighted fusion algorithm to smooth out drastically fluctuating air temperatures: when the filter coefficient is large (during the door opening or immediate closing phase), the model assigns a higher weight to air temperature, allowing the virtual food temperature to respond quickly to changes in the thermal environment; when the filter coefficient is small and stable (during the door's long-term closing phase), the model reduces the weight of air temperature, highlighting the hysteresis characteristics of food temperature, ultimately outputting a virtual food temperature that accurately reflects the true temperature of the food, avoiding misjudgments by the refrigeration system due to instantaneous fluctuations in air temperature.

[0052] In some embodiments, this step can be implemented by combining a weighted average filtering model with data preprocessing: Optionally, the system can start the NTC temperature sensor in the refrigerator compartment to collect air temperature data at a frequency of 1 second / time, take the average value of 3 consecutive collections, filter out single abnormal fluctuations, and obtain the real-time air temperature; read the thermal inertia filter coefficient at the current moment from the system storage module, which has been updated through S102 or S104; input the real-time air temperature and thermal inertia filter coefficient into the weighted average filtering model, and the model calculates according to the logic of "virtual food temperature = real-time air temperature × thermal inertia filter coefficient + virtual food temperature at the previous moment × (1 - thermal inertia filter coefficient)" to obtain and store the virtual food temperature at the current moment; Optionally, the system can also achieve this through an exponential smoothing filter model combined with dynamic adjustment logic: A temperature sensor collects air temperature data at a frequency of 500ms / time, and the collected data is smoothed using a sliding window algorithm (window size 5) to obtain the real-time air temperature; the current thermal inertia filter coefficient is read, and if the coefficient is in an updating state (such as the decreasing phase after the door has just closed), the smoothing coefficient of the model is bound to the thermal inertia filter coefficient, and dynamically adjusted synchronously; the processed real-time air temperature and the bound smoothing coefficient are input into the exponential smoothing filter model to calculate the current virtual food temperature, and the calculation result is recorded for the next iteration. It is understood that other types of temperature filtering models, such as Kalman filtering, can also be used to implement this step, and this is not limited here.

[0053] S106. Set the reference temperature to the final target temperature.

[0054] Among them, the high-frequency usage period refers to the time period predicted by the user habit model when users take and put in food more frequently. This period is obtained by analyzing historical door opening frequency data; the baseline set temperature refers to the standard target temperature preset by the system to ensure the fresh storage of food, such as 4℃ in refrigeration mode and -18℃ in freezing mode; the final target temperature refers to the target temperature value used by the system to control the operation of the compressor.

[0055] Specifically, after obtaining the virtual food temperature, the system calls the built-in clock module to get the current system time (accurate to the minute), and then queries the list of high-frequency usage periods (such as multiple consecutive or discrete time intervals) marked in the user's usage habit model. The current system time is matched with the time intervals in the list; if the current time falls within any high-frequency usage period interval, it is determined to be in a high-frequency usage period. At this point, the system directly uses the baseline set temperature as the final target temperature—because during high-frequency usage periods, frequent opening and closing of the door causes a continuous influx of hot air from outside, making the internal temperature prone to fluctuations. Adjusting the target temperature might affect the safety of food storage. The baseline set temperature, however, is a verified safe storage temperature that ensures that the food remains in a suitable low-temperature environment even with frequent door openings, while avoiding additional burden on the refrigeration system due to target temperature adjustments.

[0056] In some embodiments, the system can also obtain the current system time from the clock module; read the list of high-frequency usage periods from the user usage habit model storage data; traverse the list of periods, determine whether the current time in minutes falls between the start and end minutes of any period, and if a match is found, directly call the baseline set temperature parameter (e.g., refrigeration 4°C) and set it as the final target temperature; Optionally, the system can also obtain the current system time and date (distinguishing between weekdays and holidays), and call the high-frequency usage period data for the corresponding date type (the periods for weekdays and holidays may differ); perform precise matching on the current time; if the current time is within 10 minutes before the start of the period, it is determined that "the high-frequency period is about to begin," and the baseline setting temperature preparation is started in advance; if it is within the period, the matching is directly determined to be successful; if the matching is successful, the system checks whether there are other target temperature setting commands (such as pre-cooling compensation commands); if so, the command is terminated, and the baseline setting temperature is set as the final target temperature first. It is understood that time matching can also be achieved through other methods such as timestamp comparison, which is not limited here.

[0057] S107. Set the difference between the reference set temperature and the pre-cooling compensation value as the final target temperature.

[0058] Among them, the pre-cooling time window refers to the preset number of minutes before the start of the high-frequency usage period, which is used to store cold for the freezer in advance; the pre-cooling compensation value is a preset compensation value used by the system to reduce the target temperature during the pre-cooling period, such as 2℃. This value is calibrated based on the freezer's cooling capacity and the heat load during the high-frequency period.

[0059] Specifically, after determining the high-frequency usage period, if the current time is not within a high-frequency period, the system further queries the pre-cooling time window marked in the user usage habit model. This time window corresponds one-to-one with the high-frequency usage period; for example, the first 30 minutes of each high-frequency period is the pre-cooling time window. The current system time is matched with the pre-cooling time window. If the match is successful, the pre-cooling mode is activated. At this time, the system reads the pre-cooling compensation value (e.g., 2℃) from the parameter library, subtracts this compensation value from the baseline setting temperature to obtain the pre-cooling target temperature, and sets it as the final target temperature. For example, if the baseline setting temperature in refrigeration mode is 4℃ and the pre-cooling compensation value is 2℃, then the final target temperature is 2℃. By lowering the target temperature in advance, more cold air accumulates inside the freezer. When the high-frequency usage period arrives and users frequently open the door, causing hot air to rush in, the accumulated cold air can quickly neutralize the heat, maintaining the temperature inside the freezer within a safe range. This prevents the refrigeration system from overloading during high-frequency periods, thus improving energy efficiency.

[0060] S108. Set the energy-saving target temperature, which includes energy-saving compensation, as the final target temperature.

[0061] Among them, energy-saving compensation refers to the system's preset adjustment value, such as 1℃, used to increase the target temperature during low-load periods to achieve energy saving; the energy-saving target temperature refers to the target temperature value including energy-saving compensation, that is, the baseline setting temperature plus the energy-saving compensation value. This temperature can reduce the load on the refrigeration system while ensuring the safety of food storage.

[0062] Specifically, after completing two time checks, if the current time falls within a non-high-frequency, non-pre-cooling idle period, the system activates the energy-saving mode. At this time, the system reads an energy-saving compensation value (e.g., 1°C) from the parameter library. This value has been experimentally verified to ensure the safety of short-term food storage even after raising the target temperature. The system adds the energy-saving compensation value to the baseline set temperature to obtain the energy-saving target temperature, which is then set as the final target temperature. For example, if the baseline temperature for refrigeration mode is 4°C and the energy-saving compensation value is 1°C, then the energy-saving target temperature is 5°C. By raising the target temperature, the temperature difference between the freezer and the external environment decreases, the rate of cold air leakage decreases, and the refrigeration system can maintain the target temperature without frequent starts or high-power operation, thereby reducing compressor operating time and power consumption, achieving energy savings without affecting the quality of food storage.

[0063] S109. Generate a frequency adjustment command to drive the compressor based on the temperature difference between the virtual food temperature and the final target temperature.

[0064] Specifically, the system obtains the current virtual food temperature calculated in step S105 and the final target temperature determined through steps S106, S107, or S108, and calculates the temperature difference between the two. When the virtual food temperature is higher than the final target temperature, the difference is positive, indicating that the temperature inside the cabinet is too high and the cooling power needs to be increased. When the difference is negative, it indicates that the temperature inside the cabinet is lower than the target temperature and the cooling power needs to be reduced or the system needs to be shut down. When the difference is close to zero, it indicates that the temperature meets the standard and the current frequency can be maintained.

[0065] The system then queries a preset temperature difference-compressor frequency mapping table. This table is calibrated based on the freezer's refrigeration characteristics to determine the target operating frequency of the compressor for different temperature differences. For example, a difference of 3°C corresponds to a frequency of 50Hz, and a difference of -1°C corresponds to a frequency of 20Hz. Based on the retrieved target frequency, the system generates a corresponding frequency adjustment command and sends it to the compressor controller. Upon receiving the command, the controller adjusts the compressor's power supply frequency, thereby changing the refrigeration power and gradually bringing the virtual food temperature closer to the final target temperature.

[0066] In the above embodiment, the system captures the opening and closing actions of the refrigerator door in real time and dynamically adjusts the thermal inertia filter coefficient accordingly. This coefficient is used to convert drastically fluctuating air temperature into a gradually changing virtual food temperature. Combined with a pre-cooling strategy based on user habit prediction, the system no longer controls the compressor solely based on instantaneous air temperature fluctuations, but rather adjusts it based on a virtual temperature that better reflects the true thermal state of the food. This method, to a certain extent, shields the control logic from the interference of hot air rushing in due to door opening and closing, reduces the frequent start-stop of the compressor due to sudden increases in air temperature, or unnecessary high-frequency cooling, thus lowering the overall energy consumption of the equipment.

[0067] The following provides a more detailed description of the process of the method provided in this implementation. Please refer to [link / reference]. Figure 2 This is a schematic diagram of a process for defrosting control in the intelligent freezer energy-saving control system of this application embodiment.

[0068] It should be noted that steps S201 to S2010 are applicable to intelligent freezer energy-saving control systems that include variable frequency compressors, evaporator fans and phase change energy storage modules.

[0069] S201, The switch status signal indicates that the switch has changed from the on state to the off state.

[0070] This step has already been explained in S101 above, and will not be repeated here.

[0071] S202. Collect dynamic response data of temperature inside the smart freezer to cooling power.

[0072] Specifically, the system activates the cooling power monitoring module to collect the compressor's current operating power in real time, including parameters such as the compressor's power supply frequency and input current. Using a preset power conversion model, these parameters are converted into corresponding cooling power data to ensure the accuracy of cooling power monitoring. Simultaneously, multiple temperature sensors within the compartment (including the center of the compartment, near the evaporator, and areas with densely packed food) are activated to collect temperature data at a high frequency of 100ms / time (the specific frequency is not limited), avoiding the limitations of single-point temperature collection. During the data collection process, the system records the time point of each cooling power adjustment and the corresponding temperature change data, forming a three-dimensional dynamic response dataset of time-cooling power-temperature. This dataset can comprehensively reflect the impact of cooling power changes on temperature under the current food loading level in the cabinet. For example, when the loading level is high, the temperature response to cooling power is slower, and the dataset will show a slower rate of temperature decrease.

[0073] S203. Calculate the ratio of temperature change rate to input power based on dynamic response data to obtain the load thermal inertia coefficient characterizing the current cargo loading state.

[0074] Specifically, the system removes abnormal data from the initial 10 seconds of data acquisition due to sensor instability, selecting only the stable 2 minutes of data for analysis. Then, the temperature change rate is calculated. Optionally, the average temperature within each window is calculated using 10-second intervals. The difference between the average temperatures of adjacent windows is divided by the time interval (10 seconds) to obtain multiple temperature change rate data. The average of these data is then taken as the final temperature change rate.

[0075] Simultaneously, input power data for the corresponding time period is extracted from the dataset, and the average input power is calculated. Finally, the calculated average temperature change rate is divided by the average input power, and the resulting ratio is the load thermal inertia coefficient. This coefficient accurately reflects the current cargo loading status because the greater the cargo loading volume, the greater its thermal inertia. Under the same input power, the smaller the temperature change rate, the greater the load thermal inertia coefficient; conversely, the smaller the loading volume, the smaller the load thermal inertia coefficient.

[0076] S204. Based on the preset load-flow resistance mapping function, convert the load thermal inertia coefficient into a flow resistance compensation factor.

[0077] The preset load-flow resistance mapping function refers to a mathematical function pre-set by the system to establish the correspondence between the load thermal inertia coefficient and the flow resistance compensation factor. This function is obtained through experimental data calibration. The flow resistance compensation factor is a parameter used to characterize the degree of gain of cargo accumulation on the air duct circulation resistance. The larger the value, the greater the air duct resistance caused by cargo accumulation.

[0078] Specifically, the system retrieves a preset load-flow resistance mapping function from memory. This function is constructed based on duct resistance test data under different cargo loading volumes (corresponding to different load thermal inertia coefficients). The function's input is the load thermal inertia coefficient, and its output is the flow resistance compensation factor. The two are positively correlated—the larger the load thermal inertia coefficient, the more cargo is loaded, the greater the resistance of cargo accumulation to duct circulation, and the larger the corresponding flow resistance compensation factor.

[0079] The system substitutes the load thermal inertia coefficient calculated in step S203 into the mapping function and obtains the corresponding flow resistance compensation factor through function calculation. For example, when the load thermal inertia coefficient is small (small cargo load), the flow resistance compensation factor obtained after substituting into the function is close to 1, indicating that the cargo has little impact on the air duct resistance; when the load thermal inertia coefficient is large (large cargo load), the flow resistance compensation factor obtained is greater than 1, indicating that the cargo has a significant increase in the air duct resistance.

[0080] In some embodiments, the system can call a preset segmented load-flow resistance mapping function, which divides the load thermal inertia coefficient into multiple intervals (such as 0-0.3, 0.3-0.6, 0.6-1.0, and above 1.0), with each interval corresponding to a basic flow resistance compensation factor (such as 1.0, 1.2, 1.5, and 1.8). The system determines the interval to which the load thermal inertia coefficient calculated in S203 belongs. If the coefficient falls exactly at the endpoint of the interval, the corresponding basic compensation factor is directly taken. If the coefficient falls within the interval, the flow resistance compensation factor is calculated using linear interpolation. For example, if the coefficient is 0.4 (in the 0.3-0.6 interval, with a basic compensation factor of 1.2-1.5), the compensation factor is calculated to be 1.3 using "1.2+(0.4-0.3) / (0.6-0.3)×(1.5-1.2)".

[0081] Optionally, the system can also call a preset nonlinear load-flow resistance mapping model (such as a quadratic function model). This model is obtained by fitting a large amount of experimental data, with the load thermal inertia coefficient as input and the initial flow resistance compensation factor as output. It collects the current airflow speed data of the smart freezer's duct. If the airflow speed is lower than 80% of the preset benchmark airflow speed, the initial flow resistance compensation factor is corrected (multiplied by a correction factor of 1.1); if the airflow speed is higher than 120% of the benchmark airflow speed, it is multiplied by a correction factor of 0.9. The corrected value is used as the final flow resistance compensation factor, ensuring that the compensation factor can more accurately reflect the actual impact of goods accumulation on duct resistance. It is understood that other types of mapping relationships, such as polynomial mapping functions, can also be used to achieve this step; this is not limited here.

[0082] S205. The rated current benchmark of the evaporator is weighted and calculated based on the flow resistance compensation factor to obtain the dynamic current threshold under the current load condition.

[0083] Among them, the evaporator fan refers to the fan used in the smart freezer to promote air circulation and accelerate heat exchange in the room; the rated current benchmark refers to the standard rated current value of the evaporator fan when there are no goods obstructing it and the air duct is unobstructed, which is the basic parameter for judging the operating status of the fan; the dynamic current threshold refers to the fan current judgment threshold adapted to the current loading status of the goods (air duct resistance).

[0084] Specifically, the system reads the rated current reference of the evaporator fan from the memory, which is the standard parameter calibrated when the evaporator fan leaves the factory, corresponding to the ideal state of unobstructed airflow and zero cargo load. Then, the flow resistance compensation factor obtained in S204 is used as a weighting coefficient to perform a weighted calculation on the rated current reference.

[0085] Since the flow resistance compensation factor characterizes the degree to which cargo accumulation increases the duct resistance, when there is a large amount of cargo accumulation (flow resistance compensation factor greater than 1), the duct resistance increases, and the fan needs to output more power to maintain normal airflow. The corresponding current threshold should be higher than the rated current reference. When there is a small amount of cargo accumulation (flow resistance compensation factor close to 1), the duct resistance is close to the ideal state, and the current threshold is close to the rated current reference. Through this weighted calculation, the obtained dynamic current threshold can accurately adapt to the current cargo loading status, avoiding subsequent misinterpretation of fan current changes caused by cargo accumulation as current changes caused by evaporator frost blockage.

[0086] S206. Divide the absolute value of the difference between the fan stator current and the dynamic current threshold by the dynamic current threshold to obtain the evaporator blockage index.

[0087] Among them, the stator current of the fan refers to the current passing through the stator winding of the evaporator fan when it is running, which is used to reflect the actual operating load of the fan; the evaporator blockage index is a parameter used to quantitatively characterize the rate of change of wind resistance caused only by the accumulation of frost. The larger the value, the thicker the frost accumulation and the more serious the evaporator blockage.

[0088] Specifically, the system activates the current acquisition module, which collects real-time stator current data from current sensors deployed in the stator winding circuit of the evaporator fan. Then, it retrieves the dynamic current threshold calculated by S205—this threshold is adapted to the duct resistance under the current cargo loading condition, eliminating interference from cargo accumulation on the current. Next, it calculates the difference between the two and takes the absolute value of the difference to avoid negative values ​​caused by the stator current being slightly lower than the threshold affecting the result.

[0089] Finally, the absolute value of the difference is divided by the dynamic current threshold and normalized to obtain the evaporator blockage index. For example, if the dynamic current threshold is 0.6A and the actual stator current is 0.9A, the absolute value of the difference is 0.3A, and the normalized blockage index is 0.5, indicating that frost accumulation leads to increased air resistance, causing the fan current to rise by 50%.

[0090] S207. Predict the duration required for defrosting based on the evaporator blockage index, and calculate the required cooling capacity compensation value for the smart freezer to maintain the duration.

[0091] The defrosting duration refers to the estimated time required to remove the frost layer on the evaporator surface through heating or other means, and is positively correlated with the frost thickness. The cold energy compensation requirement refers to the amount of cold energy that the smart freezer needs to supplement during the defrosting duration to maintain a stable room temperature and prevent food from overheating.

[0092] Specifically, the system calls a preset blocking index-defrost time mapping table. This table is built based on frost thickness test and defrost experiment data under different blocking indices. For example, a blocking index of 0.3 corresponds to a defrost time of 5 minutes, 0.6 corresponds to 8 minutes, and 1.0 corresponds to 12 minutes. Based on the blocking index calculated in S206, the corresponding defrost duration is matched from the table to complete the time prediction.

[0093] The system then calculates the required cooling capacity: It collects the current compartment temperature (virtual food temperature) and a preset safe temperature threshold (e.g., 8°C in refrigeration mode) to determine the maximum allowable temperature rise. Based on the compartment volume, air specific heat capacity, food equivalent specific heat capacity, and estimated defrosting time, the system uses a heat calculation formula (considering the heat input to the compartment from the defrosting heater) to calculate the required cooling capacity to keep the room temperature rise within a safe range during the defrosting duration. For example, if the defrosting time is 8 minutes and the compartment is estimated to absorb 200kJ of heat from the heater, 200kJ of cooling capacity needs to be added to maintain a stable temperature; therefore, the required cooling capacity compensation is 200kJ.

[0094] S208. When the current latent heat stored in the phase change energy storage module is greater than the required value for cooling compensation, a defrosting and cooling release control command is generated.

[0095] Among them, the phase change energy storage module refers to an energy storage component with built-in phase change materials (such as certain salts and paraffins), which can store latent heat (absorb cold energy) at low temperatures and release latent heat (release cold energy) at high temperatures; the current latent heat storage refers to the value of the releaseable cold energy currently stored in the phase change energy storage module, and the unit is consistent with the cold energy compensation requirement value (such as kJ); the defrost-coordinated cold release control command refers to the control signal used to control the phase change energy storage module to release cold energy synchronously during defrost to compensate for the cold energy loss.

[0096] Specifically, the system activates the latent heat monitoring module of the phase change energy storage module. Through temperature and level sensors (or mass sensors) deployed inside the module, it collects real-time data on the current temperature and phase change state of the phase change material (e.g., whether it is completely solid or partially liquid). Combined with the latent heat parameters of the phase change material (e.g., 200 kJ / kg), the current latent heat is calculated. For example, if the total mass of the phase change material is 5 kg, and 3 kg is currently in a release-cooling state, the latent heat is 3 × 200 = 600 kJ.

[0097] Subsequently, the current latent heat storage is compared with the cooling capacity compensation requirement value calculated by S207 (e.g., 200kJ). If the latent heat storage is greater than the requirement value, it indicates that the module has sufficient cooling capacity compensation capability, and the system immediately generates a defrost-coordinated cooling release control command. The command includes parameters such as defrost start time, defrost power, and initial value of cooling release air volume. If the latent heat storage is less than the requirement value, the command is not generated temporarily, and the system will re-evaluate after the module continues to store cooling capacity (e.g., after the compressor has been running for a period of time).

[0098] S209. In response to the defrosting and cooling control command, the airflow system is controlled to enter the thermal shielding defrosting mode.

[0099] Among them, the air circulation system refers to the components used to control air circulation in the smart freezer, including fans, air ducts, and air dampers; the heat shield defrosting mode refers to an operating mode in which defrosting and cold release occur simultaneously, forming a "heat shield" by releasing cold energy to prevent the heat from the defrosting heater from entering the compartment; the defrosting heater refers to the electric heating component used to heat the evaporator and melt the frost layer; the phase change energy storage module releasing cold energy into the refrigerator compartment refers to the process in which the phase change material in the phase change energy storage module changes from solid to liquid (or other phase change processes), releasing the stored cold energy into the refrigerator compartment.

[0100] Specifically, the system receives defrosting and cooling release control commands, parses parameters such as defrosting time and initial cooling air volume from the commands, and then sends mode switching commands to the air duct system. After responding to the commands, the air duct system adjusts the status of the air duct dampers—closing the main air duct damper between the evaporator and the compartment to prevent heat from the defrosting heater from directly entering the compartment; at the same time, it opens the cooling air duct damper between the phase change energy storage module and the compartment to open a channel for the release of cooling capacity.

[0101] Next, the defrost heater is activated, operating at the set power (e.g., 500W) to melt the frost layer on the evaporator surface. Simultaneously, the phase change energy storage module's cooling fan is activated, operating at the initial cooling airflow (e.g., 5m³ / h) to deliver the cooling energy released by the module into the compartment. In this mode, the heat generated by the defrost heater is primarily used to melt the frost layer, while the cooling energy released by the phase change energy storage module creates a "cold atmosphere" within the compartment, preventing the small amount of heat leakage from affecting the food temperature, thus achieving the effect of "defrosting without heating up."

[0102] S210. The cooling air volume of the phase change energy storage module is dynamically adjusted according to the temperature rise rate of the virtual food over a given period of time, so that the virtual food temperature is maintained within a preset safe threshold range. Specifically, the system activates the virtual food temperature monitoring module to collect the current virtual food temperature at a preset frequency (continuously updated based on the calculation logic of S105), and records the time point of each collection. Based on the temperature values ​​and time interval between two adjacent collections, the temperature rise rate of the virtual food is calculated. For example, if the temperature is 4℃ in the first minute and 4.5℃ in the next minute, with a time interval of 1 minute, the temperature rise rate is 0.5℃ / minute.

[0103] Subsequently, the system invokes preset temperature rise rate-airflow adjustment rules. For example, when the temperature rise rate is ≤0.2℃ / min, the current cooling airflow is maintained; when 0.2℃ / min < temperature rise rate ≤0.5℃ / min, the airflow is increased by 20%; when the temperature rise rate >0.5℃ / min, the airflow is increased by 50%. Based on the calculated temperature rise rate, the corresponding airflow adjustment strategy is matched, and an airflow adjustment command is sent to the cooling fan of the phase change energy storage module. If the virtual food temperature is close to the upper limit of the safety threshold (e.g., 7℃), the airflow adjustment range is further increased; if the temperature is below the lower limit of the threshold (e.g., 3℃), the airflow is appropriately reduced to avoid excessive cooling and energy waste, ultimately ensuring that the virtual food temperature remains stable within the safety threshold range.

[0104] The intelligent freezer energy-saving control system of this invention is applied to electronic devices. Figure 3 A schematic diagram of the architecture of an electronic device suitable for implementing embodiments of the present invention is shown.

[0105] It should be noted that, Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0106] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by instructions (computer programs), or by instructions (computer programs) controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. The electronic device of this embodiment includes a storage medium and a processor, wherein the storage medium stores multiple instructions that can be loaded by the processor to execute any step of the method provided in the embodiments of the present invention.

[0107] Specifically, the storage medium and the processor are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more signal lines. The storage medium stores computer-executable instructions that implement data access control methods, including at least one software functional module that can be stored in the storage medium in the form of software or firmware. The processor executes various functional applications and data processing by running the software program and module stored in the storage medium. The storage medium can be, but is not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The storage medium stores the program, and the processor executes the program after receiving the execution instructions.

[0108] Furthermore, the software programs and modules within the aforementioned storage medium may also include an operating system, which may include various software components and / or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.) and can communicate with various hardware or software components to provide an operating environment for other software components. The processor may be an integrated circuit chip with signal processing capabilities. The aforementioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc., which can implement or execute the methods, steps, and logic flowcharts disclosed in this embodiment. The general-purpose processor may be a microprocessor or any conventional processor.

[0109] Since the instructions stored in the storage medium can execute the steps in any of the methods provided in the embodiments of the present invention, the beneficial effects of any of the methods provided in the embodiments of the present invention can be achieved, as detailed in the preceding embodiments, and will not be repeated here.

[0110] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An energy-saving control method for an intelligent freezer, applied to an energy-saving control system for an intelligent freezer, characterized in that, The method includes: The system monitors the opening and closing status signal of the refrigerator door in real time and updates the thermal inertia filter coefficient based on the opening and closing status signal. The thermal inertia filter coefficient is a variable used to adjust the temperature following speed. If the switch status signal indicates an on state, then the thermal inertia filter coefficient is reset to a first preset value; If the switch status signal indicates a change from an on state to a off state, the duration of the current on state is obtained, and the thermal inertia filter coefficient is iteratively converged from the first preset value to the second preset value based on the attenuation step size corresponding to the duration. The real-time air temperature inside the refrigerator compartment and the current thermal inertia filter coefficient are input into a preset temperature filter model to calculate the virtual food temperature at the current moment. If the current system time is within the pre-cooling time window before the high-frequency usage period predicted by the user habit model, the difference between the baseline set temperature and the pre-cooling compensation value is set as the final target temperature. The user habit model is constructed based on the door opening frequency distribution within the historical time window. The frequency adjustment command for driving the compressor is generated based on the temperature difference between the virtual food temperature and the final target temperature.

2. The method according to claim 1, characterized in that, The step of iteratively converging the thermal inertia filter coefficient from the first preset value to the second preset value based on the attenuation step size corresponding to the duration specifically includes: The decay step size is selected from a preset time-step mapping table based on the duration, and the duration is negatively correlated with the decay step size. Within each preset control cycle, the current thermal inertia filter coefficient is updated by decreasing according to the attenuation step size until the thermal inertia filter coefficient is equal to the second preset value.

3. The method according to claim 1, characterized in that, The steps involved in constructing the user habit model based on the door opening frequency distribution within a historical time window specifically include: Record the number of times the door is opened in each time period within a preset number of days in the past, and generate a histogram of door opening frequency; The sliding window algorithm is used to identify continuous high-frequency regions in the door opening frequency histogram, and these continuous high-frequency regions are marked as the high-frequency usage periods. The time period of a preset number of minutes before the start time corresponding to the high-frequency usage period is marked as the pre-cooling time window.

4. The method according to claim 1, characterized in that, After the step of inputting the real-time air temperature inside the refrigerator compartment and the current thermal inertia filter coefficient into a preset temperature filtering model to calculate the virtual food temperature at the current moment, the method further includes: If the current system time is during the high-frequency usage period, the reference set temperature is directly set to the final target temperature; If the current system time is not within the pre-cooling time window and the high-frequency usage period at the same time, then the energy-saving target temperature including energy-saving compensation is set as the final target temperature.

5. The method according to claim 1, characterized in that, After the step of the switch status signal indicating a change from the on state to the off state, the method further includes: Collect dynamic response data of temperature inside the smart freezer to cooling power; The ratio of temperature change rate to input power is calculated based on the dynamic response data to obtain the load thermal inertia coefficient characterizing the current cargo loading state; The rated current reference of the evaporator fan is corrected based on the load thermal inertia coefficient, and the real-time collected stator current of the fan is compared with the corrected rated current reference to generate the evaporator blockage index after removing load interference. The duration required for defrosting is predicted based on the evaporator blockage index, and the required cooling capacity compensation value for the smart freezer to maintain the duration is calculated. When the current latent heat stored in the phase change energy storage module is greater than the required cooling capacity, a defrosting-coordinated cooling release control command is generated.

6. The method according to claim 5, characterized in that, The step of correcting the rated current reference of the evaporator fan based on the load thermal inertia coefficient, and comparing the real-time collected fan stator current with the corrected rated current reference to generate the evaporator blockage index after removing load interference, specifically includes: The load thermal inertia coefficient is converted into a flow resistance compensation factor based on a preset load-flow resistance mapping function. The flow resistance compensation factor is used to characterize the degree of gain of cargo accumulation on the air duct circulation resistance. The rated current reference of the evaporator is weighted and calculated based on the flow resistance compensation factor to obtain the dynamic current threshold under the current load condition; The absolute value of the difference between the stator current of the fan and the dynamic current threshold is calculated, and the absolute value of the difference is divided by the dynamic current threshold for normalization to obtain the evaporator blockage index. The evaporator blockage index is used to quantitatively characterize the rate of change in wind resistance caused only by the accumulation of frost.

7. The method according to claim 5, characterized in that, After the step of generating a defrost-coordinated cooling release control command when the current latent heat stored in the phase change energy storage module is greater than the cooling capacity compensation requirement, the method further includes: In response to the defrosting and cooling control command, the airflow system is controlled to enter the heat shield defrosting mode. In the heat shield defrosting mode, the defrosting heater operates and the phase change energy storage module releases cold to the refrigerator compartment. Calculate the rate of temperature rise of the virtual food during the duration; The cooling air volume of the phase change energy storage module is dynamically adjusted according to the temperature rise rate, so that the temperature of the virtual food is maintained within a preset safe threshold range.

8. An intelligent freezer energy-saving control system, characterized in that, The system includes: one or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the intelligent freezer energy-saving control system, the system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the intelligent freezer energy-saving control system, the system performs the method as described in any one of claims 1-7.

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