A method and system for optimizing defrosting efficiency of a refrigerator

Abstract

The application discloses a refrigerator defrosting efficiency optimization method and system, and relates to the technical field of refrigerator intelligent defrosting optimization, which comprises original refrigerator defrosting time obtained through historical operation data, and temperature of current environmental conditions for static judgment, prediction of next defrosting time and frost layer thickness of the refrigerator; feedback control method is adopted to perform closed loop optimization control on defrosting parameters, energy consumption in the defrosting process is balanced according to the closed loop optimization control; and a dynamic switching mixed control strategy is introduced in the closed loop optimization control process to complete the refrigerator defrosting efficiency optimization process. The application realizes accurate prediction of defrosting time and frost layer thickness, effectively improves defrosting efficiency and reduces energy consumption, improves operation energy efficiency and refrigeration recovery speed of the refrigerator, prolongs the service life of the equipment, improves user experience, and has significant energy-saving benefits and popularization and application value.

Description

Technical Field

[0001] This invention relates to the field of intelligent defrosting optimization technology for freezers, and in particular to a method and system for optimizing the defrosting efficiency of freezers. Background Technology

[0002] With intelligentization, energy conservation, and high efficiency becoming the mainstream trends in the home appliance industry, freezers, as key equipment in food preservation, pharmaceutical storage, and cold chain logistics, are receiving increasing attention for their operational efficiency and energy consumption control. In actual operation, the evaporator surface of freezers is prone to frost formation due to temperature and humidity differences, which not only reduces refrigeration efficiency but also significantly increases energy consumption. This frost problem is particularly pronounced in scenarios involving frequent door openings, high humidity environments, or frequent temperature fluctuations, severely impacting stable equipment operation and user experience. Therefore, there is an urgent need for a technical solution that can accurately predict defrosting timing, intelligently control the defrosting process, and dynamically optimize refrigeration recovery strategies to improve the operating efficiency of freezer systems, reduce energy consumption, and extend equipment lifespan. This invention is proposed against the backdrop of increasingly urgent demands for intelligent upgrades and energy conservation in home appliances, and it possesses significant social application value and industrialization prospects.

[0003] Currently, traditional freezers mostly use timed defrosting or temperature threshold-triggered defrosting, lacking the ability to dynamically sense the actual operating status. This easily leads to over-defrosting or under-defrosting. The former results in energy waste and equipment damage, while the latter leads to decreased refrigeration efficiency and large temperature fluctuations inside the freezer. In addition, existing technologies are relatively crude in terms of refrigeration recovery control after defrosting, lacking a coordinated control mechanism for key components such as fans and compressors, which can easily cause system oscillations and affect the stability of the freezer. At the same time, most freezer control systems do not have the ability to remotely verify and dynamically switch strategies, making it difficult to adapt to complex and changing usage environments and user behavior characteristics. Therefore, existing technologies still have significant shortcomings in defrosting efficiency, energy consumption control, system stability, and intelligence level. There is an urgent need for a new method to optimize freezer defrosting efficiency that integrates multi-source sensing, closed-loop optimization, and remote strategy verification mechanisms. Summary of the Invention

[0004] In view of the problems existing in the current methods and systems for optimizing the defrosting efficiency of freezers, this invention is proposed.

[0005] Therefore, the problem that this invention aims to solve is that the existing technology still has significant shortcomings in terms of defrosting efficiency, energy consumption control, system stability, and intelligence level.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a method for optimizing the defrosting efficiency of a freezer, comprising:

[0008] By integrating multi-source information on freezer defrosting, a defrosting status perception network is constructed. Historical operating data and current environmental conditions are introduced. Static judgments are made based on the original freezer defrosting time obtained from historical operating data and the temperature of the current environmental conditions to predict the timing and frost thickness of the freezer's next defrosting.

[0009] Based on the timing of the next defrost of the freezer and the thickness of the frost layer, a feedback control method is used to optimize the defrost parameters in a closed loop. Based on the closed-loop optimization control, the energy consumption during the defrost process is weighed.

[0010] After defrosting is completed, the operating parameters of the freezer's refrigeration system are automatically adjusted, a remote control strategy verification mechanism is established, and a dynamic switching hybrid control strategy is introduced during the closed-loop optimization control process to complete the freezer defrosting efficiency optimization process.

[0011] As a preferred embodiment of the freezer defrosting efficiency optimization method of the present invention, the fusion of multi-source information for freezer defrosting includes collecting multi-source heterogeneous information during freezer operation. This multi-source heterogeneous information includes the freezer's evaporator surface temperature, internal temperature, ambient temperature and humidity, compressor start / stop status, fan operating frequency, and the number of times the user opens the door. Infrared thermal imaging is introduced to capture the frost distribution on the evaporator surface. Based on the multi-source information and the captured frost distribution, an information processing network containing multiple sensing nodes is established. This information processing network is responsible for parameter analysis tasks related to the multi-source heterogeneous information and the frost distribution on the evaporator surface, including:

[0012] Temperature change rate analysis node;

[0013] User behavior feature extraction node;

[0014] Key points in the projection of frost accumulation trends;

[0015] The introduction of historical operating data and current environmental conditions includes setting initial defrost trigger thresholds for freezer types and usage scenarios based on parameter analysis tasks. A static judgment is made based on the temperature data in the current environmental conditions.

[0016] As a preferred embodiment of the method for optimizing the defrosting efficiency of a freezer according to the present invention, the static judgment includes:

[0017] By analyzing the temperature change rate node, the continuous constant value of the evaporator surface temperature is detected. When the humidity inside the freezer is higher than At this time, it is determined that the frosting stage has begun;

[0018] Based on user behavior characteristics, nodes are extracted to determine the frequency of users frequently opening the door. When the frequency of users frequently opening the door is met... When the load on the freezer increases, the defrosting prediction process is activated.

[0019] As a preferred embodiment of the method for optimizing the defrosting efficiency of a freezer according to the present invention, the step of predicting the timing of the next defrosting of the freezer and the frost layer thickness includes:

[0020] Design a multi-factor interaction method for predicting defrosting timing;

[0021] Frost thickness predicts defrosting needs;

[0022] The design of multi-factor interaction for defrosting timing prediction includes setting rules A, B, and C to determine whether to enter the defrosting preparation stage;

[0023] Rule A includes triggering a defrost warning if the current defrost cycle exceeds the historical average cycle by more than 10% and the evaporator surface temperature drops.

[0024] Rule B includes a rule that if a user opens the door more than a set threshold number of times over three consecutive days... When the ambient humidity rises, the predicted defrosting cycle should be shortened.

[0025] Rule C includes scheduling the next defrost in advance if the previous defrost indicates that the frost layer has not been completely removed;

[0026] The frost thickness prediction for defrosting needs includes analyzing the frost thickness based on changes in evaporator surface temperature, load fluctuations during compressor start-up and shutdown, and the degree of reduction in freezer cooling efficiency.

[0027] The frost thickness includes light, moderate, or heavy grades;

[0028] When the frost layer thickness is at the light level, use the freezer's low power and short defrost time.

[0029] When the frost layer thickness is at a medium or heavy level, the high-power heating of the freezer will be activated and the internal temperature heating time of the freezer will be extended.

[0030] As a preferred embodiment of the method for optimizing the defrosting efficiency of a freezer according to the present invention, the feedback control method includes using the predicted timing of the next defrosting of the freezer and the frost layer thickness as the input basis for the feedback control method, which is used to set the initial value of the defrosting parameters.

[0031] The closed-loop optimization control includes feedback information collected during the defrosting process based on multi-source heterogeneous information, and performing closed-loop optimization control by combining the feedback information with defrosting timing prediction.

[0032] When the surface temperature of the evaporator rises, the heating power of the refrigerator's internal temperature is increased.

[0033] If it is detected that the frost layer scheduled for the next defrost is not completely removed, the heating time for the internal temperature of the freezer will be extended.

[0034] When a user opens the door more than a set threshold for three consecutive days When the ambient humidity drops, the fan is activated to lower the internal temperature of the freezer.

[0035] The energy consumption balance during the defrosting process includes dynamically adjusting the fan speed and classifying the fan operation mode by combining the evaporator surface temperature and the humidity inside the freezer.

[0036] After the first defrosting, the heat-humidity coupling index is calculated based on the evaporator surface temperature and the internal humidity of the freezer during the defrosting recovery process. The fan operation mode is then determined according to the heat-humidity coupling index.

[0037] The specific formula for calculating the thermal-humidity coupling index is as follows:

[0038] ;

[0039] in, Indicates the surface temperature of the evaporator. Indicates the humidity inside the freezer. , and Indicates the weighting coefficient. Indicates the internal temperature of the freezer. Indicates ambient temperature and humidity. Indicates the thermal-humidity coupling index;

[0040] The fan is in a closed or open state based on the thermal-humidity coupling index of 0.4, 0.7 or 1.0. This indicates whether the current air condition inside the freezer is suitable for starting the fan to circulate air, preventing secondary frost formation due to the introduction of humid air, and balancing the energy consumption during the defrosting process.

[0041] As a preferred embodiment of the method for optimizing the defrosting efficiency of a freezer described in this invention, the establishment of a remote control strategy verification mechanism includes establishing a remote control platform architecture, formulating a strategy verification process, and introducing a digital twin verification mechanism.

[0042] The establishment of the remote control platform architecture includes connecting to the remote control platform architecture based on the cloud platform at the end of the first defrosting;

[0043] Once the established remote control platform architecture identifies the end of the first defrosting, a strategy verification process is developed.

[0044] The strategy verification process includes refrigeration recovery time, internal temperature fluctuations of the freezer, compressor start-stop frequency, fan operation status, and energy consumption. A digital twin model of the freezer system is established in the remote control platform architecture to simulate the refrigeration operation status of the freezer after the first defrost.

[0045] As a preferred embodiment of the freezer defrosting efficiency optimization method of the present invention, the dynamic switching hybrid control strategy includes simulating the freezer's refrigeration operation state during the first defrosting cycle after the first defrosting cycle.

[0046] A strategy transition mechanism is introduced to simulate the refrigeration operation state of the freezer after the Nth defrost cycle.

[0047] The strategy transition mechanism includes setting a defrost control strategy and a refrigeration operation strategy. The defrost control strategy is applicable to the defrost phase, and its set of control parameters is as follows:

[0048] ;

[0049] in, This represents the control parameters in the defrosting control strategy. Represents the set of control parameters;

[0050] The refrigeration operation strategy is applicable to the normal operation phase after defrosting, and its set of control parameters is as follows:

[0051] ;

[0052] in, This represents the set of control parameters for the refrigeration operation strategy. This refers to the control parameters in the refrigeration operation strategy;

[0053] The process of optimizing the defrosting efficiency of the freezer includes transitioning the refrigeration operation state of the freezer after defrosting is completed based on a strategy transition mechanism.

[0054] Secondly, embodiments of the present invention provide a freezer defrosting efficiency optimization system, which includes: a static judgment module, which makes a static judgment based on the original freezer defrosting time obtained from historical operating data and the temperature of the current environmental conditions, and predicts the timing and frost thickness of the freezer for the next defrosting.

[0055] The closed-loop optimization control module combines the timing of the next defrost cycle of the freezer with the thickness of the frost layer, and uses a feedback control method to perform closed-loop optimization control on the defrosting parameters. Based on the closed-loop optimization control, the energy consumption during the defrosting process is weighed.

[0056] The freezer defrosting efficiency optimization module introduces a dynamic switching hybrid control strategy during the closed-loop optimization control process to complete the freezer defrosting efficiency optimization process.

[0057] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-described method for optimizing the defrosting efficiency of a freezer.

[0058] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the above-described method for optimizing the defrosting efficiency of a freezer.

[0059] The beneficial effects of this invention are:

[0060] This invention integrates multi-source heterogeneous information from the operation of the freezer, combines historical data with real-time environmental conditions, and constructs a defrosting status perception network to achieve accurate prediction of defrosting timing and frost thickness.

[0061] By employing a feedback control method to perform closed-loop optimization control of defrosting parameters, defrosting efficiency is effectively improved and energy consumption is reduced. The introduction of a dynamic switching hybrid control strategy and a remote strategy verification mechanism enhances the system's adaptability and stability at different operating stages. The overall solution not only improves the freezer's operating energy efficiency and cooling recovery speed but also extends the equipment's service life and enhances the user experience, demonstrating significant energy-saving benefits and promotional application value. Attached Figure Description

[0062] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0063] Figure 1 The flowchart illustrates a method and system for optimizing the defrosting efficiency of a freezer, as provided in one embodiment of the present invention.

[0064] Figure 2 This is a schematic diagram of the defrosting and refrigeration optimization process of a method and system for optimizing the defrosting efficiency of a freezer, provided as an embodiment of the present invention. Detailed Implementation

[0065] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0066] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0067] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0068] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0069] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0070] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0071] Example 1: Refer to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a method for optimizing the defrosting efficiency of a freezer, including:

[0072] S1: Integrate multi-source information on freezer defrosting, construct a defrosting status perception network, introduce historical operating data and current environmental conditions, and make static judgments based on the original freezer defrosting time obtained from historical operating data and the temperature of the current environmental conditions to predict the timing and frost thickness of the freezer's next defrosting.

[0073] The process integrates multi-source information for refrigerator defrosting, including collecting heterogeneous information from various sources during refrigerator operation. This heterogeneous information includes the evaporator surface temperature, internal temperature, ambient temperature and humidity, compressor start / stop status, fan operating frequency, and the number of times the user opens the door. Infrared thermal imaging is used to capture the distribution of frost on the evaporator surface. Based on this multi-source information and the captured frost distribution, an information processing network with multiple sensing nodes is established. This network is responsible for parameter analysis of the heterogeneous information and the frost distribution, including:

[0074] Temperature change rate analysis node;

[0075] User behavior feature extraction node;

[0076] Key points in the projection of frost accumulation trends;

[0077] Incorporating historical operational data and current environmental conditions, including parameter analysis tasks, to set initial defrosting trigger thresholds for freezer types and usage scenarios. A static judgment is made based on the temperature data in the current environmental conditions.

[0078] Furthermore, in step S1 of the present invention, the initial defrost trigger threshold is set according to the type of freezer and typical usage scenario. Specifically, the initial defrost cycle is set to 12 hours for ordinary household freezers and 6 hours for commercial freezers or freezers used in high humidity environments.

[0079] The threshold for the rate of change of evaporator surface temperature is set to ±0.5℃ / min to determine whether the stable frosting stage has been entered; the threshold for the frequency of frequent door opening by users is set to more than 5 times per hour as an auxiliary judgment condition for triggering the defrosting prediction process.

[0080] The frost accumulation trend prediction node sets the frost thickness judgment threshold. For example, when the evaporator surface temperature deviates from the set temperature by more than 3°C, it is judged as light frost, more than 5°C as moderate frost, and more than 7°C as heavy frost.

[0081] Simultaneously, by combining environmental temperature and humidity data, a relative humidity threshold of 70% is set. When this value is exceeded, the prediction weight of the frost trend is enhanced. The above threshold is dynamically corrected through training with historical operating data and feedback from actual operation to improve the accuracy of defrosting prediction and the adaptability of the system.

[0082] S1.1: Static judgment, including:

[0083] By analyzing the temperature change rate node, the continuous constant value of the evaporator surface temperature is detected. When the humidity inside the freezer is higher than At this point, it is determined that the frosting stage has begun.

[0084] Based on user behavior characteristics, nodes are extracted to determine the frequency of users frequently opening the door. When the frequency of users frequently opening the door is met... When the load on the freezer increases, the defrosting prediction process is activated.

[0085] Furthermore, the threshold settings used for static judgment are as follows: the threshold for judging the continuous constant value of the evaporator surface temperature is set as follows: if the temperature change rate is less than ±0.2℃ / min and the duration exceeds 10 minutes, it indicates that the evaporator has entered a stable state.

[0086] If the humidity inside the freezer is higher than the set frosting trigger humidity threshold, defined as relative humidity RH ≥ 65%, the evaporator surface is deemed to have the conditions for frosting and enters the frosting stage.

[0087] The threshold for determining the frequency of frequent door openings by users is set at more than 5 times per hour. When this frequency is met, the system determines that the user's behavior is frequent and the freezer load fluctuates significantly, thereby triggering the defrost prediction process.

[0088] The aforementioned thresholds are initialized based on actual operating data and typical usage scenarios, and can be adaptively adjusted according to the long-term statistical characteristics of the freezer's usage environment to improve judgment accuracy and system intelligence.

[0089] S1.2: Predict the timing and frost thickness of the next defrost cycle for the freezer, including:

[0090] Design a multi-factor interaction method for predicting defrosting timing;

[0091] Frost thickness predicts defrosting needs;

[0092] The design of multi-factor interaction for defrosting timing prediction includes setting rules A, B and C to determine whether to enter the defrosting preparation stage;

[0093] Rule A includes triggering a defrost warning if the current defrost cycle exceeds the historical average cycle by more than 10% and the evaporator surface temperature drops.

[0094] Rule B includes rules that apply if a user opens the door more than a set threshold number of times over three consecutive days. When the ambient humidity rises, the predicted defrosting cycle should be shortened.

[0095] Rule C includes scheduling the next defrost in advance if the previous defrost indicates that the frost layer has not been completely removed;

[0096] Predicting defrosting demand based on frost thickness involves analyzing frost thickness based on changes in evaporator surface temperature, load fluctuations during compressor start-up and shutdown, and the degree of reduction in freezer cooling efficiency.

[0097] Frost thickness is categorized as light, moderate, or heavy.

[0098] When the frost layer thickness is at the light level, use the freezer's low power and short defrost time.

[0099] When the frost layer thickness is at a medium or heavy level, the high-power heating of the freezer will be activated and the internal temperature heating time of the freezer will be extended.

[0100] Furthermore, in Rule A, if the current defrost cycle exceeds 10% of the historical average cycle (for example, if the historical average cycle is 12 hours and the current cycle exceeds 13.2 hours), and the evaporator surface temperature drops at a rate exceeding 0.5℃ / min for more than 5 minutes, a defrost warning will be triggered.

[0101] In Rule B, if a user opens the door more than 8 times a day for 3 consecutive days (i.e., the average daily door opening frequency is high), and the relative humidity of the environment rises to above 70%RH, the system will determine it as a high-frosting-risk scenario and predict that the defrosting cycle will be shortened by 10% to 20%.

[0102] In Rule C, if the evaporator surface temperature does not rise above 0°C for more than 10 minutes during the previous defrosting process, or if the evaporator temperature drops below -5°C again within 2 hours after the defrosting ends, it indicates that the frost layer has not been completely removed, and the system will schedule the next defrosting ahead of schedule, with the advance time set at 15% to 25% of the original cycle.

[0103] Regarding frost thickness assessment, three levels are set based on the deviation of the evaporator surface temperature from the set value, changes in compressor start-stop intervals, and refrigeration efficiency degradation:

[0104] Light frost: Temperature deviation is 2~3℃, compressor start-stop cycle is extended by no more than 20%, low power defrosting is used (e.g., heating power is 60% of rated power), duration is 10~15 minutes.

[0105] Medium frost layer: Temperature deviation is 3~5℃, compressor start-stop cycle is extended by 20%~40%, use medium to high power defrosting (such as 80% of rated power), duration is 20~25 minutes.

[0106] Heavy frost buildup: Temperature deviation exceeds 5℃, compressor start-stop cycle is extended by more than 40%, high-power defrosting (full power operation) is started for more than 30 minutes, and the internal heating time of the freezer is extended to ensure that the frost is completely removed.

[0107] S2: Combining the timing of the next defrost cycle of the freezer with the thickness of the frost layer, a feedback control method is used to perform closed-loop optimization control of the defrost parameters. Based on the closed-loop optimization control, the energy consumption during the defrost process is weighed.

[0108] The feedback control method includes using the predicted timing of the next defrost cycle of the freezer and the frost thickness as the input basis for setting the initial values ​​of the defrost parameters.

[0109] Closed-loop optimization control includes feedback information collected during the defrosting process based on multi-source heterogeneous information, and then performing closed-loop optimization control by combining the feedback information with defrosting timing prediction.

[0110] When the surface temperature of the evaporator rises, the heating power of the refrigerator's internal temperature is increased.

[0111] If it is detected that the frost layer scheduled for the next defrost is not completely removed, the heating time for the internal temperature of the freezer will be extended.

[0112] When a user opens the door more than a set threshold for three consecutive days When the ambient humidity decreases, the fan is activated to lower the internal temperature of the freezer.

[0113] Balancing energy consumption during defrosting involves dynamically adjusting fan speed and classifying fan operating modes based on evaporator surface temperature and internal humidity of the freezer.

[0114] After the first defrosting, the heat-humidity coupling index is calculated based on the evaporator surface temperature and the internal humidity of the freezer during the defrosting recovery process. The fan operation mode is then determined according to the heat-humidity coupling index.

[0115] The specific formula for calculating the thermal-humidity coupling index is as follows:

[0116] ;

[0117] in, Indicates the surface temperature of the evaporator. Indicates the humidity inside the freezer. ,and Indicates the weighting coefficient. Indicates the internal temperature of the freezer. Indicates ambient temperature and humidity. This represents the thermal-humidity coupling index.

[0118] The fan is in a closed or open state based on the thermal-humidity coupling index of 0.4, 0.7 or 1.0. This indicates whether the current air condition inside the freezer is suitable for starting the fan to circulate air, preventing secondary frost formation due to the introduction of humid air, and balancing the energy consumption during the defrosting process.

[0119] S3: After defrosting, automatically adjust the operating parameters of the freezer's refrigeration system, establish a remote control strategy verification mechanism, introduce a dynamic switching hybrid control strategy during the closed-loop optimization control process, and complete the freezer defrosting efficiency optimization process.

[0120] The establishment of a remote control strategy verification mechanism includes building a remote control platform architecture, formulating a strategy verification process, and introducing a digital twin verification mechanism.

[0121] Establishing a remote control platform architecture includes connecting to the remote control platform architecture based on a cloud platform at the end of the first defrosting.

[0122] Once the established remote control platform architecture identifies the end of the first defrosting operation, a strategy verification process is developed.

[0123] The strategy verification process includes refrigeration recovery time, internal temperature fluctuations of the freezer, compressor start-stop frequency, fan operation status, and energy consumption. A digital twin model of the freezer system is established in the remote control platform architecture to simulate the refrigeration operation status of the freezer after the first defrost.

[0124] S3.1: The dynamic switching hybrid control strategy includes simulating the first refrigerator cooling operation state after the first defrost, and introducing a strategy transition mechanism so that after the Nth defrost, the Nth refrigerator cooling operation state is simulated.

[0125] The strategy transition mechanism includes setting a defrost control strategy and a refrigeration operation strategy. The defrost control strategy is applicable to the defrost phase, and its set of control parameters is as follows:

[0126] ;

[0127] in, This represents the control parameters in the defrosting control strategy. This represents the set of control parameters.

[0128] The refrigeration operation strategy is applicable to the normal operation phase after defrosting, and its set of control parameters is as follows:

[0129] ;

[0130] in, This represents the set of control parameters for the refrigeration operation strategy. This represents the control parameters in the refrigeration operation strategy.

[0131] The process of optimizing the defrosting efficiency of the freezer includes transitioning the freezer's refrigeration operation state after defrosting is completed based on a strategy transition mechanism.

[0132] In a preferred embodiment, a freezer defrosting efficiency optimization system includes a static judgment module, which makes a static judgment based on the original freezer defrosting time obtained from historical operating data and the temperature of the current environmental conditions, and predicts the timing and frost thickness of the freezer's next defrosting.

[0133] The closed-loop optimization control module combines the timing of the next defrost cycle of the freezer with the thickness of the frost layer, and uses a feedback control method to perform closed-loop optimization control on the defrosting parameters. Based on the closed-loop optimization control, the energy consumption during the defrosting process is weighed.

[0134] The refrigerator defrosting efficiency optimization module introduces a dynamic switching hybrid control strategy during the closed-loop optimization control process to complete the refrigerator defrosting efficiency optimization process.

[0135] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0136] The computer device may be a terminal, which includes a processor, memory, communication interface, display screen, and input device connected via a system bus.

[0137] The processor of this computer device provides computing and control capabilities. The memory of this computer device includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media.

[0138] The communication interface of this computer device is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad located on the computer device casing, or an external keyboard, touchpad, or mouse.

[0139] In summary, this invention integrates multi-source heterogeneous information from the freezer's operation process, combines historical data with real-time environmental conditions, and constructs a defrosting status sensing network to achieve accurate prediction of defrosting timing and frost thickness. Furthermore, it employs a feedback control method to perform closed-loop optimization control of defrosting parameters, effectively improving defrosting efficiency and reducing energy consumption.

[0140] By introducing a dynamic switching hybrid control strategy and a remote strategy verification mechanism, the system's adaptability and stability at different operating stages are enhanced. The overall solution not only improves the freezer's operating energy efficiency and cooling recovery speed, but also extends the equipment's service life and enhances the user experience, demonstrating significant energy-saving benefits and application value.

[0141] Example 2: Refer to Figure 1 and Figure 2This is the second embodiment of the present invention, which provides a method for optimizing the defrosting efficiency of a freezer. To verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0142] In the cold chain freezer system of a commercial supermarket, the freezer defrosting efficiency optimization technology of the present invention was applied. The system acquires data such as evaporator surface temperature, internal temperature and humidity of the freezer, compressor start and stop status, fan operating frequency and number of times the user opens the door in real time by deploying a multi-source information acquisition module. Combined with infrared thermal imaging recognition technology, the distribution of frost layer on the evaporator surface is dynamically captured.

[0143] During a certain operation, the system detected through the temperature change rate analysis node that the evaporator temperature change rate was below 0.2℃ / min and remained stable for 10 minutes. At the same time, the humidity inside the freezer reached 68%RH, indicating that it had entered the frosting stage. Furthermore, the user had opened the door more than 8 times per day for three consecutive days.

[0144] The system initiates the defrost prediction process. Based on rule B, the current ambient humidity has risen to 72%RH. The system shortens the original 12-hour defrost cycle to 10 hours. The frost thickness prediction model analyzes the current frost layer and concludes that the current frost layer is moderate (evaporator temperature deviation is about 4℃, and compressor start-stop cycle is extended by 30%).

[0145] Accordingly, the system automatically sets the medium-high power defrosting mode (heating power at 80% of the rated power) for 20 minutes, and extends the heating time to ensure that the frost layer is removed.

[0146] After defrosting, the system adjusts the fan operation status based on the thermal-humidity coupling index (THCI) calculation results to avoid secondary frosting. At the same time, it simulates the freezer's recovery operation status through a remote control platform and dynamically switches to a refrigeration operation strategy to achieve efficient, energy-saving, and stable freezer operation, significantly improving defrosting efficiency and reducing energy consumption.

[0147] The comparison between the present invention and the prior art is shown in Table 1 below:

[0148] Table 1. Comparison of the present invention with the prior art;

[0149] Comparison Dimensions Existing technology Technical solution of the present invention Advantages of this invention Defrosting trigger method Fixed time or single temperature threshold Multi-source data fusion + dynamic prediction More precise, avoiding false triggers and missed triggers. Frost layer recognition capability Indirect judgment cannot identify the distribution of frost layers. Infrared thermal imaging combined with multi-factor analysis to identify frost thickness and distribution. Improve recognition accuracy and support differentiated defrosting strategies Defrosting control mode Fixed power and time Automatically adjusts power and time based on frost level. More efficient and energy-saving, extending equipment lifespan Recovery of control after defrosting Single control strategy Dynamically regulate fan operation using the thermal-humidity coupling index (THCI). Prevents secondary frost formation and improves recovery efficiency System control method No feedback mechanism Closed-loop feedback optimization control Real-time adjustments improve system stability and adaptability. Remote management capabilities Lack of remote verification and control Supports remote policy verification and digital twin simulation Convenient for remote debugging and strategy optimization User behavior adaptability Ignoring user impact Extract user behavior characteristics and dynamically adjust strategies More in line with actual use cases Energy consumption control effect High energy consumption and lack of energy-saving mechanisms Multi-parameter collaborative optimization, dynamic energy saving Significantly reduce defrosting energy consumption System intelligence level Fixed control logic Self-learning and adaptive threshold adjustment More intelligent, suitable for various freezer types and environments

[0150] Table 1 illustrates the core advantages of this invention over existing technologies in optimizing the defrosting efficiency of freezers.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing defrosting efficiency in a freezer, characterized in that: include, By integrating multi-source information on freezer defrosting, a defrosting status perception network is constructed. Historical operating data and current environmental conditions are introduced. Static judgments are made based on the original freezer defrosting time obtained from historical operating data and the temperature of the current environmental conditions to predict the timing and frost thickness of the freezer's next defrosting. Based on the timing of the next defrost of the freezer and the thickness of the frost layer, a feedback control method is used to optimize the defrost parameters in a closed loop. Based on the closed-loop optimization control, the energy consumption during the defrost process is weighed. After defrosting, the operating parameters of the freezer's refrigeration system are automatically adjusted, a remote control strategy verification mechanism is established, and a dynamic switching hybrid control strategy is introduced in the closed-loop optimization control process to complete the freezer defrosting efficiency optimization process. The dynamic switching hybrid control strategy includes simulating the first refrigeration operation state of the freezer after the first defrost. A strategy transition mechanism is introduced to simulate the refrigeration operation state of the freezer after the Nth defrost cycle. The strategy transition mechanism includes setting a defrost control strategy and a refrigeration operation strategy. The defrost control strategy is applicable to the defrost phase, and its set of control parameters is as follows: ； in, This represents the control parameters in the defrosting control strategy. Represents the set of control parameters; The refrigeration operation strategy is applicable to the normal operation phase after defrosting, and its set of control parameters is as follows: ； in, This represents the set of control parameters for the refrigeration operation strategy. This refers to the control parameters in the refrigeration operation strategy; The process of optimizing the defrosting efficiency of the freezer includes transitioning the refrigeration operation state of the freezer after defrosting is completed based on a strategy transition mechanism.

2. The method for optimizing the defrosting efficiency of a freezer as described in claim 1, characterized in that: The integrated multi-source information for refrigerator defrosting includes collecting multi-source heterogeneous information during refrigerator operation. This multi-source heterogeneous information includes the refrigerator's evaporator surface temperature, internal temperature, ambient temperature and humidity, compressor start / stop status, fan operating frequency, and the number of times the user opens the door. Infrared thermal imaging is introduced to capture the frost distribution on the evaporator surface. Based on the multi-source information and the captured frost distribution, an information processing network with multiple sensing nodes is established. This information processing network is responsible for parameter analysis tasks related to the multi-source heterogeneous information and the frost distribution on the evaporator surface, including: Temperature change rate analysis node; User behavior feature extraction node; Key points in the projection of frost accumulation trends; The introduction of historical operating data and current environmental conditions includes setting initial defrost trigger thresholds for freezer types and usage scenarios based on parameter analysis tasks. A static judgment is made based on the temperature data in the current environmental conditions.

3. The method for optimizing the defrosting efficiency of a freezer as described in claim 2, characterized in that: The static judgment includes: By analyzing the temperature change rate node, the continuous constant value of the evaporator surface temperature is detected. When the humidity inside the freezer is higher than At this time, it is determined that the frosting stage has begun; Based on user behavior characteristics, nodes are extracted to determine the frequency of users frequently opening the door. When the frequency of users frequently opening the door is met... When the load on the freezer increases, the defrosting prediction process is activated. The thresholds used for static judgment are set as follows: the threshold for judging the evaporator surface temperature to remain constant is set at a temperature change rate of less than ±0.2℃ / min for more than 10 minutes, indicating that the evaporator has entered a stable state; the internal humidity of the freezer is higher than the set frosting trigger humidity threshold, defined as relative humidity RH ≥ 65%, at which point the evaporator surface is judged to have the conditions for frosting and enter the frosting stage; the threshold for judging the frequency of frequent user door opening is set at more than 5 times per hour. When this frequency is met, the system judges that the user behavior is frequent and the freezer load fluctuation is significantly increased, thereby triggering the defrosting prediction process. The above thresholds are initialized based on actual operating data and typical usage scenarios, and support adaptive adjustment based on the long-term statistical characteristics of the freezer's usage environment to improve the accuracy of judgment and the level of system intelligence.

4. The method for optimizing the defrosting efficiency of a freezer as described in claim 3, characterized in that: The prediction of the timing and frost thickness for the next defrost cycle of the freezer includes: Design a multi-factor interaction method for predicting defrosting timing; Frost thickness predicts defrosting needs; The design of multi-factor interaction for defrosting timing prediction includes setting rules A, B, and C to determine whether to enter the defrosting preparation stage; Rule A includes triggering a defrost warning if the current defrost cycle exceeds the historical average cycle by more than 10% and the evaporator surface temperature drops. Rule B includes a rule that if a user opens the door more than a set threshold number of times over three consecutive days... When the ambient humidity rises, the predicted defrosting cycle should be shortened. Rule C includes scheduling the next defrost in advance if the previous defrost indicates that the frost layer has not been completely removed; The frost thickness prediction for defrosting needs includes analyzing the frost thickness based on changes in evaporator surface temperature, load fluctuations during compressor start-up and shutdown, and the degree of reduction in freezer cooling efficiency. The frost thickness includes light, moderate, or heavy grades; When the frost layer thickness is at the light level, use the freezer's low power and short defrost time. When the frost layer thickness is at a medium or heavy level, the high-power heating of the freezer will be activated and the internal temperature heating time of the freezer will be extended.

5. The method for optimizing the defrosting efficiency of a freezer as described in claim 4, characterized in that: The feedback control method includes using the predicted timing of the next defrost of the freezer and the frost thickness as the input basis for the feedback control method, which is used to set the initial values ​​of the defrost parameters. The closed-loop optimization control includes feedback information collected during the defrosting process based on multi-source heterogeneous information, and performing closed-loop optimization control by combining the feedback information with defrosting timing prediction. When the surface temperature of the evaporator rises, the heating power of the refrigerator's internal temperature is increased. If it is detected that the frost layer scheduled for the next defrost is not completely removed, the heating time for the internal temperature of the freezer will be extended. When a user opens the door more than a set threshold for three consecutive days When the ambient humidity drops, the fan is activated to lower the internal temperature of the freezer. The energy consumption balance during the defrosting process includes dynamically adjusting the fan speed and classifying the fan operation mode by combining the evaporator surface temperature and the humidity inside the freezer. After the first defrosting, the heat-humidity coupling index is calculated based on the evaporator surface temperature and the internal humidity of the freezer during the defrosting recovery process. The fan operation mode is then determined according to the heat-humidity coupling index. The specific formula for calculating the thermal-humidity coupling index is as follows: ； in, Indicates the surface temperature of the evaporator. Indicates the humidity inside the freezer. , and Indicates the weighting coefficient. Indicates the internal temperature of the freezer. Indicates ambient temperature and humidity. Indicates the thermal-humidity coupling index; The fan is in a closed or open state based on the thermal-humidity coupling index of 0.4, 0.7 or 1.

0. This indicates whether the current air condition inside the freezer is suitable for starting the fan to circulate air, preventing secondary frost formation due to the introduction of humid air, and balancing the energy consumption during the defrosting process.

6. The method for optimizing the defrosting efficiency of a freezer as described in claim 5, characterized in that: The establishment of the remote control strategy verification mechanism includes establishing a remote control platform architecture, formulating a strategy verification process, and introducing a digital twin verification mechanism. The establishment of the remote control platform architecture includes connecting to the remote control platform architecture based on the cloud platform at the end of the first defrosting; Once the established remote control platform architecture identifies the end of the first defrosting, a strategy verification process is developed. The strategy verification process includes refrigeration recovery time, internal temperature fluctuations of the freezer, compressor start-stop frequency, fan operation status, and energy consumption. A digital twin model of the freezer system is established in the remote control platform architecture to simulate the refrigeration operation status of the freezer after the first defrost.

7. A system for optimizing the defrosting efficiency of a freezer, based on the method for optimizing the defrosting efficiency of a freezer according to any one of claims 1 to 6, characterized in that: include, The static judgment module uses the original defrosting time of the freezer obtained from historical operating data and the temperature of the current environmental conditions to make static judgments and predict the timing and frost thickness of the freezer's next defrosting. The closed-loop optimization control module combines the timing of the next defrost cycle of the freezer with the thickness of the frost layer, and uses a feedback control method to perform closed-loop optimization control on the defrost parameters. Based on the closed-loop optimization control, the energy consumption during the defrost process is weighed. The freezer defrosting efficiency optimization module introduces a dynamic switching hybrid control strategy during the closed-loop optimization control process to complete the freezer defrosting efficiency optimization process.

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