Intelligent adjusting and dynamic defrosting method and system based on multiple sensors and medium

Abstract

The invention provides an intelligent adjusting and dynamic defrosting method and system based on multiple sensors and a medium The method comprises the steps that firstly, monitoring data are obtained in real time according to the multi-dimensional sensors deployed in a freezer, and a multi-source data matrix is obtained through preset data processing and integration; secondly, dynamically adjusting a frost condition model weight coefficient according to monitoring data, and quantifying a frost condition coefficient based on a frost condition model in combination with a multi-source data matrix; then, executing a three-stage defrosting strategy according to the frost condition coefficient, and respectively adjusting working parameters of defrosting equipment such as a compressor, a condensation fan or a heater; and finally, precise control over the box temperature is achieved through power step soft start of a compressor and PID dynamic adjustment of an electronic expansion valve, and energy efficiency optimization is achieved by automatically optimizing the rotating speed of a condensation fan based on the environment temperature and humidity. The corresponding defrosting operation is triggered through the frost condition coefficient, and the effectiveness of defrosting and the stability of the box temperature are improved; in addition, the adaptability of special scenes is enhanced by dynamically adjusting the weight.

Description

Technical Field

[0001] The present invention relates to the field of refrigerators, and more particularly to a multi-sensor based intelligent adjustment and dynamic defrosting method, system and medium. Background Art

[0002] In fields such as the pharmaceutical cold chain, biological sample storage, and scientific research experiments, the precise temperature control and efficient defrosting capabilities of refrigeration equipment directly impact sample safety and equipment reliability. Traditional freezers generally use timed defrost or single-sensor control modes. These defrost operations are disconnected from actual frost conditions and rely on fixed time periods or evaporator temperature differential thresholds to trigger defrost. This can lead to frequent ineffective defrosts or delayed defrosts that cause frost blockage. Furthermore, the defrost process can experience drastic temperature fluctuations, causing the chamber temperature to rise again, threatening the activity of heat-sensitive materials like vaccines.

[0003] While some research has attempted to introduce intelligent algorithms to optimize defrosting, single infrared sensors cannot quantify the density of the frost layer, temperature-prediction models suffer from a sharp drop in accuracy in ultra-low temperature environments, and variable-frequency control technology fails to dynamically adapt defrosting strategies. Therefore, there is an urgent need to develop a freezer defrosting technology that integrates multi-dimensional state perception, dynamic decision-making, and energy efficiency optimization. Summary of the Invention

[0004] In view of the above problems, the purpose of the present invention is to provide a multi-sensor based intelligent adjustment and dynamic defrosting method, system and medium. First, a multi-source data matrix is ​​obtained based on multi-sensor data monitoring and processing, and then a frost coefficient is obtained based on a preset frost evaluation model; then, the corresponding defrost operation is performed according to the frost coefficient to improve the accuracy and effectiveness of defrosting; finally, through the synergistic energy efficiency optimization model of compressor pulse maintenance and condensing fan speed regulation, the stability of the thermostat temperature is improved and the energy efficiency ratio is increased.

[0005] A first aspect of the present invention provides a multi-sensor based intelligent adjustment and dynamic defrosting method, the method comprising:

[0006] Based on the preset collection period, monitoring data is obtained from the sensors deployed in the freezer;

[0007] Based on the preset monitoring data processing logic, a data matrix is ​​formed according to the monitoring data;

[0008] Based on the preset threshold judgment logic, the weight of the frost model is adjusted according to the monitoring data;

[0009] Based on a preset frost model, a frost coefficient is obtained according to the data matrix and the frost model weights;

[0010] Based on the preset three-level defrost strategy, differentiated defrost instructions are executed according to the frost coefficient to control the working status of the compressor, condensing fan or heater;

[0011] After the defrost command is executed, the box temperature is controlled according to the preset compressor power soft start and PID dynamic adjustment of the electronic expansion valve opening;

[0012] After the box temperature stabilizes, the compressor is switched to a preset pulse maintenance mode, and the condensing fan speed is dynamically adjusted according to the ambient temperature and humidity.

[0013] In this solution, the preset monitoring data processing logic is used to form a data matrix based on the monitoring data, specifically:

[0014] A three-dimensional temperature field model is constructed by distributing a temperature sensor array at the evaporator inlet, evaporator outlet, at least six spatial coordinate points inside the box, and on the condenser surface;

[0015] Scan the evaporator fin surface with an infrared thermal imager to generate a thermal map of frost thickness distribution;

[0016] The dielectric constant of the frost layer is monitored in real time by a capacitive sensor, and the dielectric constant change rate of the frost layer is obtained by combining it with a preset reference value of the dielectric constant of the frost layer.

[0017] The suction pressure, discharge pressure and refrigerant liquid level pressure are continuously collected through the pressure transmitter, and the pressure change rate is calculated based on the preset unit time;

[0018] Based on the three-phase current waveform and vibration spectrum data of the compressor, the electromechanical state data is obtained;

[0019] The temperature field model, frost layer thickness distribution thermal map, frost layer dielectric constant change rate, pressure change rate and electromechanical state data are integrated into a multidimensional data matrix.

[0020] In this solution, the preset threshold judgment logic is used to adjust the frost model weight according to the monitoring data, specifically:

[0021] If the humidity information of the monitoring data exceeds a preset humidity threshold, the frost thickness weight of the frost condition model weight is increased to a preset frost thickness weight reference value;

[0022] If the lowest temperature value of the cabinet in the monitoring data is lower than the preset temperature threshold, the pressure weight of the frost model weight is increased to the preset pressure weight reference value;

[0023] According to the adjusted frost thickness weight or the pressure weight, the frost layer density weight of the frost condition model weight is lowered.

[0024] In this solution, the frost coefficient is obtained based on the preset frost model according to the data matrix and the frost model weights, specifically:

[0025] Obtaining a maximum frost temperature difference according to a frost layer thickness distribution thermodynamic map of the data matrix;

[0026] Calculating the product of the maximum frost temperature difference and the frost thickness weight to obtain a frost thickness factor;

[0027] Calculating the product of the pressure change rate of the data matrix and the pressure weight to obtain a pressure factor;

[0028] Calculating the product of the frost layer dielectric constant change rate of the data matrix and the frost layer density weight to obtain a density factor;

[0029] The sum of the frost thickness factor, the pressure factor, and the density factor is calculated and processed according to a preset normalization mapping to obtain a frost condition coefficient.

[0030] In this solution, the preset three-stage defrost strategy executes differentiated defrost instructions according to the frost coefficient to control the working status of the compressor, condensing fan or heater, specifically including:

[0031] If the frost coefficient is lower than a preset first frost threshold, dynamically adjusting the operating frequency of the compressor according to the box temperature;

[0032] If the frost condition coefficient is between a preset first frost condition threshold and a preset second frost condition threshold, a pre-defrost logic is activated, wherein the pre-defrost logic includes adjusting the condensing fan speed to a preset first speed, turning off the internal circulation fan and opening the four-way valve;

[0033] If the frost coefficient is higher than the preset second frost threshold, the secondary defrost logic is activated, wherein the secondary defrost logic first activates the hot gas bypass circuit, introduces the compressor exhaust into the evaporator through the four-way valve, and starts the heater to implement secondary heating after the basic structure of the frost layer collapses.

[0034] This solution also includes a gradient recovery refrigeration mechanism, specifically:

[0035] After the defrosting operation is completed, the four-way valve is maintained in an open state according to a preset first time length;

[0036] Starting the compressor according to a preset first power;

[0037] According to the preset power adjustment step, the compressor output power is adjusted until the preset second power is reached;

[0038] Calculate the temperature difference between the real-time box temperature and the target box temperature;

[0039] According to the temperature difference information, the opening of the electronic expansion valve is adjusted according to a preset PID control algorithm;

[0040] When the real-time box temperature returns to within the preset deviation range of the target box temperature and the duration exceeds the preset stability time threshold, it is determined to be in a stable box temperature state.

[0041] A second aspect of the present invention provides a multi-sensor based intelligent adjustment and dynamic defrosting system, including a multi-sensor based intelligent adjustment and dynamic defrosting method program, wherein the multi-sensor based intelligent adjustment and dynamic defrosting method program, when executed by the processor, implements the following steps:

[0042] Based on the preset collection period, monitoring data is obtained from the sensors deployed in the freezer;

[0043] Based on the preset monitoring data processing logic, a data matrix is ​​formed according to the monitoring data;

[0044] Based on the preset threshold judgment logic, the weight of the frost model is adjusted according to the monitoring data;

[0045] Based on a preset frost model, a frost coefficient is obtained according to the data matrix and the frost model weights;

[0046] Based on the preset three-level defrost strategy, differentiated defrost instructions are executed according to the frost coefficient to control the working status of the compressor, condensing fan or heater;

[0047] After the defrost command is executed, the box temperature is controlled according to the preset compressor power soft start and PID dynamic adjustment of the electronic expansion valve opening;

[0048] After the box temperature stabilizes, the compressor is switched to a preset pulse maintenance mode, and the condensing fan speed is dynamically adjusted according to the ambient temperature and humidity.

[0049] In this solution, the preset monitoring data processing logic is used to form a data matrix based on the monitoring data, specifically:

[0050] A three-dimensional temperature field model is constructed by distributing a temperature sensor array at the evaporator inlet, evaporator outlet, at least six spatial coordinate points inside the box, and on the condenser surface;

[0051] Scan the evaporator fin surface with an infrared thermal imager to generate a thermal map of frost thickness distribution;

[0052] The dielectric constant of the frost layer is monitored in real time by a capacitive sensor, and the dielectric constant change rate of the frost layer is obtained by combining it with a preset reference value of the dielectric constant of the frost layer.

[0053] The suction pressure, discharge pressure and refrigerant liquid level pressure are continuously collected through the pressure transmitter, and the pressure change rate is calculated based on the preset unit time;

[0054] Based on the three-phase current waveform and vibration spectrum data of the compressor, the electromechanical state data is obtained;

[0055] The temperature field model, frost layer thickness distribution thermal map, frost layer dielectric constant change rate, pressure change rate and electromechanical state data are integrated into a multidimensional data matrix.

[0056] In this solution, the preset threshold judgment logic is used to adjust the frost model weight according to the monitoring data, specifically:

[0057] If the humidity information of the monitoring data exceeds a preset humidity threshold, the frost thickness weight of the frost condition model weight is increased to a preset frost thickness weight reference value;

[0058] If the lowest temperature value of the cabinet in the monitoring data is lower than the preset temperature threshold, the pressure weight of the frost model weight is increased to the preset pressure weight reference value;

[0059] According to the adjusted frost thickness weight or the pressure weight, the frost layer density weight of the frost condition model weight is lowered.

[0060] The third aspect of the present invention provides a computer-readable storage medium, which includes a multi-sensor based intelligent adjustment and dynamic defrosting method program. When the multi-sensor based intelligent adjustment and dynamic defrosting method program is executed by a processor, the steps of the multi-sensor based intelligent adjustment and dynamic defrosting method as described in any one of the above items are implemented.

[0061] The present invention provides a multi-sensor-based intelligent adjustment and dynamic defrosting method, system, and medium. First, monitoring data is obtained in real time based on multi-dimensional sensors deployed in the freezer. After preset data processing, a multi-source data matrix is ​​integrated. Second, the frost model weight coefficient is dynamically adjusted based on the monitoring data. In combination with the multi-source data matrix, the frost coefficient is quantified based on the frost model. Then, a three-level defrosting strategy is implemented based on the frost coefficient, and the operating parameters of defrosting equipment such as the compressor, condensing fan, or heater are adjusted respectively. Finally, the compressor power step soft start and electronic expansion valve PID dynamic adjustment are used to achieve precise control of the box temperature, and the condensing fan speed is automatically optimized based on the ambient temperature and humidity to achieve energy efficiency optimization. The present invention triggers the corresponding defrost operation through the frost coefficient, thereby improving the effectiveness of defrosting and the stability of the box temperature. In addition, by dynamically adjusting the weight, the adaptability to special scenarios is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope.

[0063] Figure 1 A flow chart of a multi-sensor based intelligent adjustment and dynamic defrosting method of the present invention is shown;

[0064] Figure 2 The following is a flowchart of an operation of a monitoring data processing logic provided by an embodiment of the present invention;

[0065] Figure 3 A flow chart showing a dynamic adjustment of frost model weights provided by an embodiment of the present invention is shown;

[0066] Figure 4 A block diagram of a multi-sensor based intelligent regulation and dynamic defrosting system of the present invention is shown. DETAILED DESCRIPTION

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0068] Unless otherwise defined, all terms (including technical and scientific terms) used in the embodiments of the present invention have the same meaning as commonly understood by those skilled in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined in this manner in the embodiments of the present invention.

[0069] The terms "first," "second," and similar words used in the embodiments of the present invention do not indicate any order, quantity, or importance, but are only used to distinguish different components. Terms such as "a," "an," or "the" do not indicate a limit on quantity, but rather indicate the presence of at least one. Similarly, terms such as "include," "comprise," and "comprising" mean that the elements or objects preceding the term include the elements or objects listed after the term and their equivalents, without excluding other elements or objects.

[0070] "Connected" or "connected" and similar terms are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The preceding or subsequent steps of the methods of the embodiments of the present invention do not necessarily need to be performed in exact order. Instead, various steps may be performed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more steps may be removed from these processes.

[0071] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0072] Figure 1 The flowchart of the multi-sensor based intelligent adjustment and dynamic defrosting method of the present invention is shown.

[0073] like Figure 1 As shown, the first aspect of the present invention discloses a multi-sensor based intelligent adjustment and dynamic defrosting method, the method comprising:

[0074] S102, obtaining monitoring data based on sensors deployed in the freezer based on a preset collection period;

[0075] S104, forming a data matrix based on the monitoring data based on a preset monitoring data processing logic;

[0076] S106, adjusting the frost model weights based on the monitoring data based on a preset threshold judgment logic;

[0077] S108, obtaining a frost coefficient based on a preset frost model according to the data matrix and the frost model weights;

[0078] S110, based on a preset three-stage defrost strategy and according to the frost coefficient, executing differentiated defrost instructions to control the working state of the compressor, condensing fan or heater;

[0079] S112, after the defrost command is executed, the box temperature is controlled according to the preset compressor power soft start and PID dynamic adjustment of the electronic expansion valve opening;

[0080] S114, after the box temperature stabilizes, the compressor is switched to a preset pulse maintenance mode, and the condensing fan speed is dynamically adjusted according to the ambient temperature and humidity.

[0081] It should be noted that, as an implementation, after startup, this embodiment collects data from various sensors within the refrigerator according to a preset cycle, including but not limited to evaporator inlet and outlet temperatures, multiple temperature points within the refrigerator, infrared thermal images, pressure parameters, and compressor current and vibration signals. The acquired raw data is integrated according to spatial location and time series to calculate a three-dimensional temperature field model, a frost layer thickness distribution thermodynamic map, the frost layer dielectric constant change rate, the pressure change rate, and electromechanical state data, which are then integrated to form a multidimensional data matrix. Then, based on real-time monitoring of ambient humidity and refrigerator temperature data, the weights of the three parameters of frost thickness, pressure, and density in the frost layer calculation model are automatically adjusted. Based on dynamic weights, the data matrix is ​​input into the frost layer model, and a frost condition coefficient value is normalized and output in the range [0, 1]. This multi-source data analysis improves the accuracy of frost condition determination. When the frost condition coefficient is below a first threshold, the dynamic frequency modulation cooling mode is maintained. When the frost condition coefficient is within the preset threshold range, a pre-defrost process is initiated by reducing the condenser fan speed and activating the preheating pipeline to achieve pre-defrost functionality. When the coefficient exceeds the second threshold, the two-stage defrost logic is triggered, first introducing high-temperature exhaust gas to defrost the core, and then starting the heater to supplement heat. The three-stage defrost strategy avoids ineffective defrosting and prevents frost accumulation caused by insufficient defrosting. After defrosting is completed, the four-way valve is kept open for a preset time to dry the pipeline. The compressor is controlled to increase power soft start in a step-by-step manner, and the opening of the electronic expansion valve is dynamically adjusted through the PID algorithm. After the box temperature stabilizes in the set value range, the compressor is switched to a pulse mode with periodic start and stop, and the condensing fan speed is automatically adjusted to the optimal energy efficiency point according to the ambient temperature and humidity.

[0082] Figure 2 The flowchart of the operation of a monitoring data processing logic provided by an embodiment of the present invention is shown.

[0083] According to an embodiment of the present invention, Figure 2 As shown, the preset monitoring data processing logic forms a data matrix according to the monitoring data, specifically:

[0084] S202, constructing a three-dimensional temperature field model by distributing a temperature sensor array at the evaporator inlet, the evaporator outlet, at least six spatial coordinate points inside the box, and on the condenser surface;

[0085] S204, scanning the evaporator fin surface with an infrared thermal imager to generate a frost layer thickness distribution thermodynamic map;

[0086] S206, monitoring the dielectric constant of the frost layer in real time using a capacitive sensor, and obtaining a dielectric constant change rate of the frost layer in combination with a preset dielectric reference value of the frost layer;

[0087] S208, continuously collecting suction pressure, discharge pressure, and refrigerant liquid level pressure through a pressure transmitter, and calculating a pressure change rate based on a preset unit time;

[0088] S210, obtaining electromechanical state data based on the three-phase current waveform and vibration spectrum data of the compressor;

[0089] S212, integrating the temperature field model, the frost layer thickness distribution thermal map, the frost layer dielectric constant change rate, the pressure change rate and the electromechanical state data into a multidimensional data matrix.

[0090] It should be noted that this embodiment provides a processing logic for monitoring data. As an implementation, temperature probes are installed at the evaporator inlet and outlet, and six temperature sensors are deployed at the centers of six side walls within the enclosure. A three-dimensional thermal field model is constructed based on the temperature values. An infrared thermal imager scans the evaporator fin surface with millimeter-level resolution to generate a thermal map containing the frost thickness gradient. A capacitive sensor monitors capacitance changes in real time to convert the dielectric constant of the frost layer; the rate of change of the dielectric properties is calculated by comparing it to a frost-free baseline. A pressure sensor collects the difference between the suction and discharge pressures, as well as the refrigerant liquid level pressure, and calculates the pressure change trend using a sliding time window. A Hall current sensor captures the compressor's three-phase current waveform, and a piezoelectric vibration sensor records the 0-1kHz vibration spectrum characteristics, thereby generating electromechanical status data for the motor's operating status. A central processing unit (CPU) simultaneously receives the temperature field distribution data, infrared frost layer images, dielectric change rate, pressure dynamic curve, and electromechanical status data, aligns them by timestamp, and packages them into a multidimensional data matrix.

[0091] Figure 3 A flow chart of dynamic adjustment of frost model weights provided by an embodiment of the present invention is shown.

[0092] According to an embodiment of the present invention, Figure 3 As shown, the preset threshold judgment logic is used to adjust the frost model weight according to the monitoring data, specifically:

[0093] S302: if the humidity information of the monitoring data exceeds a preset humidity threshold, adjusting the frost thickness weight of the frost condition model weight to a preset frost thickness weight reference value;

[0094] S304: if the lowest temperature value of the cabinet in the monitoring data is lower than a preset temperature threshold, the pressure weight of the frost model weight is increased to a preset pressure weight reference value;

[0095] S306 , lowering the frost layer density weight of the frost condition model weight according to the adjusted frost thickness weight or the pressure weight.

[0096] It should be noted that this embodiment provides a dynamic adjustment logic for the weights of a frost model to improve the adaptability of the frost model in various environmental scenarios. As an implementation method, when the humidity exceeds the set threshold, the weight of the infrared frost thickness parameter in the frost model is automatically increased to the preset upper limit. At the same time, the data of the lowest temperature point inside the box is monitored. If the temperature is lower than the ultra-low temperature threshold, the weight of the pressure change parameter is increased accordingly. According to the above-mentioned weight adjustment rules, the contribution weight of the frost density parameter is proportionally reduced. The dynamic adjustment mechanism of the weight coefficient is completely based on the autonomous response of the real-time environmental status and does not require manual intervention; the frost thickness monitoring is strengthened in high temperature and high humidity environments, and the pressure parameter analysis is emphasized under ultra-low temperature conditions, so that the frost assessment model always adapts to the current operating conditions.

[0097] According to an embodiment of the present invention, the frost coefficient is obtained based on the preset frost model according to the data matrix and the frost model weights, specifically:

[0098] Obtaining a maximum frost temperature difference according to a frost layer thickness distribution thermodynamic map of the data matrix;

[0099] Calculating the product of the maximum frost temperature difference and the frost thickness weight to obtain a frost thickness factor;

[0100] Calculating the product of the pressure change rate of the data matrix and the pressure weight to obtain a pressure factor;

[0101] Calculating the product of the frost layer dielectric constant change rate of the data matrix and the frost layer density weight to obtain a density factor;

[0102] The sum of the frost thickness factor, the pressure factor, and the density factor is calculated and processed according to a preset normalization mapping to obtain a frost condition coefficient.

[0103] It should be noted that this embodiment obtains the frost coefficient based on a linear weighted algorithm and normalization processing. The maximum temperature difference value on the evaporator surface is extracted from the infrared thermal imaging data as a frost thickness characterization quantity, and the maximum frost temperature difference value is multiplied by the dynamically adjusted frost thickness weight to generate a frost thickness factor. The pressure factor is obtained by multiplying the pressure change rate value calculated by the pressure sensor with the current pressure weight. The dielectric property change rate calculated by the capacitive sensor is read, and the density factor is calculated in combination with the density weight. The frost thickness factor, pressure factor, and density factor are first algebraically added and summed, and the summation result is then input into the normalization processing module and converted into a standard frost coefficient in the [0,1] interval through linear mapping. In this embodiment, the frost coefficient reflects the current severity of frost in real time. The higher the value, the more serious the frost accumulation.

[0104] According to an embodiment of the present invention, the preset three-stage defrost strategy executes differentiated defrost instructions according to the frost coefficient to control the working state of the compressor, condensing fan or heater, specifically including:

[0105] If the frost coefficient is lower than a preset first frost threshold, dynamically adjusting the operating frequency of the compressor according to the box temperature;

[0106] If the frost condition coefficient is between a preset first frost condition threshold and a preset second frost condition threshold, a pre-defrost logic is activated, wherein the pre-defrost logic includes adjusting the condensing fan speed to a preset first speed, turning off the internal circulation fan and opening the four-way valve;

[0107] If the frost coefficient is higher than the preset second frost threshold, the secondary defrost logic is activated, wherein the secondary defrost logic first activates the hot gas bypass circuit, introduces the compressor exhaust into the evaporator through the four-way valve, and starts the heater to implement secondary heating after the basic structure of the frost layer collapses.

[0108] It should be noted that this embodiment provides a defrost operation based on a three-stage defrost strategy. When the frost coefficient is below the first frost threshold, indicating mild frost formation, the compressor operating frequency is dynamically adjusted based on the deviation between the cabinet temperature and the target temperature. When the frost coefficient rises between the first and second frost thresholds, indicating that frost has begun to accumulate, a pre-defrost preparation procedure is initiated. In one embodiment, the pre-defrost preparation procedure reduces the condensing fan speed to a preset low speed to accumulate heat energy; shuts down the in-cabinet recirculation fan to reduce cooling loss; and opens the four-way valve in advance to allow high-temperature refrigerant to flow through the evaporator pipe for preheating. When the defrost coefficient exceeds the second frost threshold, a second-stage defrost operation is triggered. In one embodiment, the second-stage defrost operation includes first activating the hot gas bypass circuit to direct hot gas from the compressor exhaust pipe into the evaporator to melt the frost core; and after the infrared thermal imager confirms the collapse of the frost layer's basic structure, activating the PTC ceramic heater to provide secondary supplemental heating. The timing coordination of the hot gas bypass and electric heating prevents thermal shock damage to the evaporator. In addition, this embodiment uses a pre-defrost mechanism to reduce the speed of frost formation and also enables early preheating, thereby reducing the time required for formal defrosting. During the defrost process, the frost layer melting status is continuously monitored, and the defrost mode is automatically exited when the termination conditions are met.

[0109] According to an embodiment of the present invention, a gradient recovery refrigeration mechanism is also included, specifically:

[0110] After the defrosting operation is completed, the four-way valve is maintained in an open state according to a preset first time length;

[0111] Starting the compressor according to a preset first power;

[0112] According to the preset power adjustment step, the compressor output power is adjusted until the preset second power is reached;

[0113] Calculate the temperature difference between the real-time box temperature and the target box temperature;

[0114] According to the temperature difference information, the opening of the electronic expansion valve is adjusted according to a preset PID control algorithm;

[0115] When the real-time box temperature returns to within the preset deviation range of the target box temperature and the duration exceeds the preset stability time threshold, it is determined to be in a stable box temperature state.

[0116] It should be noted that this embodiment provides a gradient recovery refrigeration mechanism. After the defrost operation is completed, by maintaining the four-way valve in the open state for a preset time, the residual heat of the pipeline is used to evaporate the residual moisture, thereby avoiding secondary frost on the pipeline. The compressor is driven to start in low-power mode, and the output power is increased step by step at fixed time intervals until the set value is reached; a gradual power increase is adopted to prevent compressor liquid hammer. The deviation between the average temperature of multiple points in the box and the target temperature is calculated in real time, and the opening of the electronic expansion valve is dynamically adjusted based on the temperature deviation value through the proportional, integral and differential controllers, wherein the proportional unit responds to the real-time temperature difference, the integral unit eliminates the historical temperature deviation, and the differential unit suppresses the temperature fluctuation; the temperature fluctuation is controlled within the set range through PID dynamic adjustment. When the box temperature continues to be stable within the allowable deviation range for more than the set time, it is determined that the box temperature has entered a stable state.

[0117] It is worth mentioning that after the defrost operation is completed and the box temperature is stable, it also includes an energy consumption optimization mode, specifically:

[0118] Controlling the compressor to operate in a pulse maintenance mode at a preset first frequency, wherein the pulse maintenance mode repeatedly switches the output state of the compressor with a preset first on duration and a preset first off duration as a switching cycle;

[0119] According to the ambient temperature and humidity, the speed of the condensing fan is adjusted based on the preset mapping relationship;

[0120] When it detects that the door is open or the load changes suddenly, it automatically switches to normal cooling mode.

[0121] It should be noted that this embodiment provides an energy consumption optimization mode, which is operated after the defrost operation is completed and when the box temperature is stable, so as to reduce compressor wear and improve energy efficiency. In this embodiment, when the box temperature is detected to be stable, the compressor is switched to the pulse maintenance mode. As an implementation method, the pulse maintenance mode is to alternately start and stop the compressor at a fixed cycle, drive the compressor according to the preset first frequency within the set operating time, and then turn off the compressor to enter the insulation stage; the pulse mode reduces compressor wear. According to the ambient temperature and ambient humidity data output by the ambient temperature and humidity sensor, the preset speed mapping table is queried to obtain the optimal condensing fan speed, thereby maintaining the box temperature stable. At the same time, the box door switch sensor signal and the temperature change rate are continuously monitored. When it is detected that the box door is open or the box temperature suddenly changes and exceeds the threshold, the energy efficiency optimization mode is immediately exited and switched back to the normal refrigeration process to avoid temperature out of control. The pulse operation strategy is automatically enabled again after the system stabilizes again.

[0122] It is worth mentioning that it also includes:

[0123] When the infrared thermal imager or capacitive sensor fails, the pressure sensor data and historical frost model are used to predict the frost state and execute the timed defrost program;

[0124] When a single defrost lasts longer than the preset first duration, the defrost process is forced to terminate and an audible and visual alarm is triggered, and the system self-check program is started to diagnose the cause of the fault;

[0125] If the box temperature rise rate exceeds the preset temperature rise rate threshold during the defrost process, the heater will be immediately turned off and the backup refrigeration unit will be started.

[0126] It should be noted that this embodiment provides an exception handling mechanism. When the system diagnoses that the infrared thermal imager or the capacitive sensor has failed, it automatically calls the historical frost database and uses the pressure sensor data to predict the frost status; at the same time, a degraded execution timer defrost program is used to ensure basic operation and maintain basic defrost functions. During the defrost execution process, if the duration of a single defrost exceeds the safety threshold, the power supply of the four-way valve and the heater is immediately cut off, and the sound and light alarm device is triggered to prevent the equipment from overheating and damage through a double protection mechanism; at the same time, the fault self-check program is started to analyze the cause of the timeout. If the box temperature recovery rate increases abnormally during the defrost period, the system immediately turns off the heater and starts the backup refrigeration unit to suppress the temperature to ensure the safety of ultra-low temperature samples in the event of a failure. All abnormal events generate encrypted logs and upload them to the cloud operation and maintenance platform.

[0127] It is worth mentioning that it also includes:

[0128] If the pre-defrost logic is executed, the opening time of the four-way valve is extended according to the preset proportional threshold;

[0129] If the secondary defrost logic is executed, the heater is prohibited from turning on and defrosting is only done through the hot gas bypass circuit;

[0130] If it is in the gradient recovery refrigerator stage, the pulse maintenance mode is operated at the preset second frequency and the power regulation step is lowered.

[0131] It should be noted that this embodiment provides a method for applying to ultra-low temperature defrosting scenarios. As an implementation method, when operating in an ultra-low temperature scenario, the preheating time of the four-way valve is automatically extended to 150% of the normal value in the pre-defrost stage to avoid cold brittle cracking of the pipeline. After entering the secondary defrost stage, the PTC heater is forcibly disabled, and only a single hot gas bypass path is used to complete the defrosting operation to prevent a sudden rise in ambient temperature. In the gradient recovery stage, the power adjustment step of the compressor is reduced, thereby extending the time for the power to be adjusted to the set value and reducing the mechanical load under ultra-low temperature conditions; at the same time, the compressor is operated in low-frequency pulse mode to extend the proportion of shutdown insulation time and ensure the operational stability of the ultra-low temperature compressor. All parameter adjustments are automatically triggered by the ambient temperature to ensure the safe and stable operation of ultra-low temperature equipment.

[0132] Figure 4 A block diagram of a multi-sensor based intelligent regulation and dynamic defrosting system of the present invention is shown.

[0133] like Figure 4 As shown, the second aspect of the present invention discloses a multi-sensor based intelligent adjustment and dynamic defrosting system 4, comprising a memory 41 and a processor 42. The memory includes a multi-sensor based intelligent adjustment and dynamic defrosting method program. When the multi-sensor based intelligent adjustment and dynamic defrosting method program is executed by the processor, the following steps are implemented:

[0134] Based on the preset collection period, monitoring data is obtained from the sensors deployed in the freezer;

[0135] Based on the preset monitoring data processing logic, a data matrix is ​​formed according to the monitoring data;

[0136] Based on the preset threshold judgment logic, the weight of the frost model is adjusted according to the monitoring data;

[0137] Based on a preset frost model, a frost coefficient is obtained according to the data matrix and the frost model weights;

[0138] Based on the preset three-level defrost strategy, differentiated defrost instructions are executed according to the frost coefficient to control the working status of the compressor, condensing fan or heater;

[0139] After the defrost command is executed, the box temperature is controlled according to the preset compressor power soft start and PID dynamic adjustment of the electronic expansion valve opening;

[0140] After the box temperature stabilizes, the compressor is switched to a preset pulse maintenance mode, and the condensing fan speed is dynamically adjusted according to the ambient temperature and humidity.

[0141] It should be noted that, as an implementation, after startup, this embodiment collects data from various sensors within the refrigerator according to a preset cycle, including but not limited to evaporator inlet and outlet temperatures, multiple temperature points within the refrigerator, infrared thermal images, pressure parameters, and compressor current and vibration signals. The acquired raw data is integrated according to spatial location and time series to calculate a three-dimensional temperature field model, a frost layer thickness distribution thermodynamic map, the frost layer dielectric constant change rate, the pressure change rate, and electromechanical state data, which are then integrated to form a multidimensional data matrix. Then, based on real-time monitoring of ambient humidity and refrigerator temperature data, the weights of the three parameters of frost thickness, pressure, and density in the frost layer calculation model are automatically adjusted. Based on dynamic weights, the data matrix is ​​input into the frost layer model, and a frost condition coefficient value is normalized and output in the range [0, 1]. This multi-source data analysis improves the accuracy of frost condition determination. When the frost condition coefficient is below a first threshold, the dynamic frequency modulation cooling mode is maintained. When the frost condition coefficient is within the preset threshold range, a pre-defrost process is initiated by reducing the condenser fan speed and activating the preheating pipeline to achieve pre-defrost functionality. When the coefficient exceeds the second threshold, the two-stage defrost logic is triggered, first introducing high-temperature exhaust gas to defrost the core, and then starting the heater to supplement heat. The three-stage defrost strategy avoids ineffective defrosting and prevents frost accumulation caused by insufficient defrosting. After defrosting is completed, the four-way valve is kept open for a preset time to dry the pipeline. The compressor is controlled to increase power soft start in a step-by-step manner, and the opening of the electronic expansion valve is dynamically adjusted through the PID algorithm. After the box temperature stabilizes in the set value range, the compressor is switched to a pulse mode with periodic start and stop, and the condensing fan speed is automatically adjusted to the optimal energy efficiency point according to the ambient temperature and humidity.

[0142] According to an embodiment of the present invention, the preset monitoring data processing logic is used to form a data matrix according to the monitoring data, specifically:

[0143] A three-dimensional temperature field model is constructed by distributing a temperature sensor array at the evaporator inlet, evaporator outlet, at least six spatial coordinate points inside the box, and on the condenser surface;

[0144] Scan the evaporator fin surface with an infrared thermal imager to generate a thermal map of frost thickness distribution;

[0145] The dielectric constant of the frost layer is monitored in real time by a capacitive sensor, and the dielectric constant change rate of the frost layer is obtained by combining it with a preset reference value of the dielectric constant of the frost layer.

[0146] The suction pressure, discharge pressure and refrigerant liquid level pressure are continuously collected through the pressure transmitter, and the pressure change rate is calculated based on the preset unit time;

[0147] Based on the three-phase current waveform and vibration spectrum data of the compressor, the electromechanical state data is obtained;

[0148] The temperature field model, frost layer thickness distribution thermal map, frost layer dielectric constant change rate, pressure change rate and electromechanical state data are integrated into a multidimensional data matrix.

[0149] It should be noted that this embodiment provides a processing logic for monitoring data. As an implementation, temperature probes are installed at the evaporator inlet and outlet, and six temperature sensors are deployed at the centers of six side walls within the enclosure. A three-dimensional thermal field model is constructed based on the temperature values. An infrared thermal imager scans the evaporator fin surface with millimeter-level resolution to generate a thermal map containing the frost thickness gradient. A capacitive sensor monitors capacitance changes in real time to convert the dielectric constant of the frost layer; the rate of change of the dielectric properties is calculated by comparing it to a frost-free baseline. A pressure sensor collects the difference between the suction and discharge pressures, as well as the refrigerant liquid level pressure, and calculates the pressure change trend using a sliding time window. A Hall current sensor captures the compressor's three-phase current waveform, and a piezoelectric vibration sensor records the 0-1kHz vibration spectrum characteristics, thereby generating electromechanical status data for the motor's operating status. A central processing unit (CPU) simultaneously receives the temperature field distribution data, infrared frost layer images, dielectric change rate, pressure dynamic curve, and electromechanical status data, aligns them by timestamp, and packages them into a multidimensional data matrix.

[0150] According to an embodiment of the present invention, the preset threshold judgment logic is used to adjust the frost model weight according to the monitoring data, specifically:

[0151] If the humidity information of the monitoring data exceeds a preset humidity threshold, the frost thickness weight of the frost condition model weight is increased to a preset frost thickness weight reference value;

[0152] If the lowest temperature value of the cabinet in the monitoring data is lower than the preset temperature threshold, the pressure weight of the frost model weight is increased to the preset pressure weight reference value;

[0153] According to the adjusted frost thickness weight or the pressure weight, the frost layer density weight of the frost condition model weight is lowered.

[0154] It should be noted that this embodiment provides a dynamic adjustment logic for the weights of a frost model to improve the adaptability of the frost model in various environmental scenarios. As an implementation method, when the humidity exceeds the set threshold, the weight of the infrared frost thickness parameter in the frost model is automatically increased to the preset upper limit. At the same time, the data of the lowest temperature point inside the box is monitored. If the temperature is lower than the ultra-low temperature threshold, the weight of the pressure change parameter is increased accordingly. According to the above-mentioned weight adjustment rules, the contribution weight of the frost density parameter is proportionally reduced. The dynamic adjustment mechanism of the weight coefficient is completely based on the autonomous response of the real-time environmental status and does not require manual intervention; the frost thickness monitoring is strengthened in high temperature and high humidity environments, and the pressure parameter analysis is emphasized under ultra-low temperature conditions, so that the frost assessment model always adapts to the current operating conditions.

[0155] According to an embodiment of the present invention, the frost coefficient is obtained based on the preset frost model according to the data matrix and the frost model weights, specifically:

[0156] Obtaining a maximum frost temperature difference according to a frost layer thickness distribution thermodynamic map of the data matrix;

[0157] Calculating the product of the maximum frost temperature difference and the frost thickness weight to obtain a frost thickness factor;

[0158] Calculating the product of the pressure change rate of the data matrix and the pressure weight to obtain a pressure factor;

[0159] Calculating the product of the frost layer dielectric constant change rate of the data matrix and the frost layer density weight to obtain a density factor;

[0160] The sum of the frost thickness factor, the pressure factor, and the density factor is calculated and processed according to a preset normalization mapping to obtain a frost condition coefficient.

[0161] It should be noted that this embodiment obtains the frost coefficient based on a linear weighted algorithm and normalization processing. The maximum temperature difference value on the evaporator surface is extracted from the infrared thermal imaging data as a frost thickness characterization quantity, and the maximum frost temperature difference value is multiplied by the dynamically adjusted frost thickness weight to generate a frost thickness factor. The pressure factor is obtained by multiplying the pressure change rate value calculated by the pressure sensor with the current pressure weight. The dielectric property change rate calculated by the capacitive sensor is read, and the density factor is calculated in combination with the density weight. The frost thickness factor, pressure factor, and density factor are first algebraically added and summed, and the summation result is then input into the normalization processing module and converted into a standard frost coefficient in the [0,1] interval through linear mapping. In this embodiment, the frost coefficient reflects the current severity of frost in real time. The higher the value, the more serious the frost accumulation.

[0162] According to an embodiment of the present invention, the preset three-stage defrost strategy executes differentiated defrost instructions according to the frost coefficient to control the working state of the compressor, condensing fan or heater, specifically including:

[0163] If the frost coefficient is lower than a preset first frost threshold, dynamically adjusting the operating frequency of the compressor according to the box temperature;

[0164] If the frost condition coefficient is between a preset first frost condition threshold and a preset second frost condition threshold, a pre-defrost logic is activated, wherein the pre-defrost logic includes adjusting the condensing fan speed to a preset first speed, turning off the internal circulation fan and opening the four-way valve;

[0165] If the frost coefficient is higher than the preset second frost threshold, the secondary defrost logic is activated, wherein the secondary defrost logic first activates the hot gas bypass circuit, introduces the compressor exhaust into the evaporator through the four-way valve, and starts the heater to implement secondary heating after the basic structure of the frost layer collapses.

[0166] It should be noted that this embodiment provides a defrost operation based on a three-stage defrost strategy. When the frost coefficient is below the first frost threshold, indicating mild frost formation, the compressor operating frequency is dynamically adjusted based on the deviation between the cabinet temperature and the target temperature. When the frost coefficient rises between the first and second frost thresholds, indicating that frost has begun to accumulate, a pre-defrost preparation procedure is initiated. In one embodiment, the pre-defrost preparation procedure reduces the condensing fan speed to a preset low speed to accumulate heat energy; shuts down the in-cabinet recirculation fan to reduce cooling loss; and opens the four-way valve in advance to allow high-temperature refrigerant to flow through the evaporator pipe for preheating. When the defrost coefficient exceeds the second frost threshold, a second-stage defrost operation is triggered. In one embodiment, the second-stage defrost operation includes first activating the hot gas bypass circuit to direct hot gas from the compressor exhaust pipe into the evaporator to melt the frost core; and after the infrared thermal imager confirms the collapse of the frost layer's basic structure, activating the PTC ceramic heater to provide secondary supplemental heating. The timing coordination of the hot gas bypass and electric heating prevents thermal shock damage to the evaporator. In addition, this embodiment uses a pre-defrost mechanism to reduce the speed of frost formation and also enables early preheating, thereby reducing the time required for formal defrosting. During the defrost process, the frost layer melting status is continuously monitored, and the defrost mode is automatically exited when the termination conditions are met.

[0167] According to an embodiment of the present invention, a gradient recovery refrigeration mechanism is also included, specifically:

[0168] After the defrosting operation is completed, the four-way valve is maintained in an open state according to a preset first time length;

[0169] Starting the compressor according to a preset first power;

[0170] According to the preset power adjustment step, the compressor output power is adjusted until the preset second power is reached;

[0171] Calculate the temperature difference between the real-time box temperature and the target box temperature;

[0172] According to the temperature difference information, the opening of the electronic expansion valve is adjusted according to a preset PID control algorithm;

[0173] When the real-time box temperature returns to within the preset deviation range of the target box temperature and the duration exceeds the preset stability time threshold, it is determined to be in a stable box temperature state.

[0174] It should be noted that this embodiment provides a gradient recovery refrigeration mechanism. After the defrost operation is completed, by maintaining the four-way valve in the open state for a preset time, the residual heat of the pipeline is used to evaporate the residual moisture, thereby avoiding secondary frost on the pipeline. The compressor is driven to start in low-power mode, and the output power is increased step by step at fixed time intervals until the set value is reached; a gradual power increase is adopted to prevent compressor liquid hammer. The deviation between the average temperature of multiple points in the box and the target temperature is calculated in real time, and the opening of the electronic expansion valve is dynamically adjusted based on the temperature deviation value through the proportional, integral and differential controllers, wherein the proportional unit responds to the real-time temperature difference, the integral unit eliminates the historical temperature deviation, and the differential unit suppresses the temperature fluctuation; the temperature fluctuation is controlled within the set range through PID dynamic adjustment. When the box temperature continues to be stable within the allowable deviation range for more than the set time, it is determined that the box temperature has entered a stable state.

[0175] It is worth mentioning that after the defrost operation is completed and the box temperature is stable, it also includes an energy consumption optimization mode, specifically:

[0176] Controlling the compressor to operate in a pulse maintenance mode at a preset first frequency, wherein the pulse maintenance mode repeatedly switches the output state of the compressor with a preset first on duration and a preset first off duration as a switching cycle;

[0177] According to the ambient temperature and humidity, the speed of the condensing fan is adjusted based on the preset mapping relationship;

[0178] When it detects that the door is open or the load changes suddenly, it automatically switches to normal cooling mode.

[0179] It should be noted that this embodiment provides an energy consumption optimization mode, which is operated after the defrost operation is completed and when the box temperature is stable, so as to reduce compressor wear and improve energy efficiency. In this embodiment, when the box temperature is detected to be stable, the compressor is switched to the pulse maintenance mode. As an implementation method, the pulse maintenance mode is to alternately start and stop the compressor at a fixed cycle, drive the compressor according to the preset first frequency within the set operating time, and then turn off the compressor to enter the insulation stage; the pulse mode reduces compressor wear. According to the ambient temperature and ambient humidity data output by the ambient temperature and humidity sensor, the preset speed mapping table is queried to obtain the optimal condensing fan speed, thereby maintaining the box temperature stable. At the same time, the box door switch sensor signal and the temperature change rate are continuously monitored. When it is detected that the box door is open or the box temperature suddenly changes and exceeds the threshold, the energy efficiency optimization mode is immediately exited and switched back to the normal refrigeration process to avoid temperature out of control. The pulse operation strategy is automatically enabled again after the system stabilizes again.

[0180] It is worth mentioning that it also includes:

[0181] When the infrared thermal imager or capacitive sensor fails, the pressure sensor data and historical frost model are used to predict the frost state and execute the timed defrost program;

[0182] When a single defrost lasts longer than the preset first duration, the defrost process is forced to terminate and an audible and visual alarm is triggered, and the system self-check program is started to diagnose the cause of the fault;

[0183] If the box temperature rise rate exceeds the preset temperature rise rate threshold during the defrost process, the heater will be immediately turned off and the backup refrigeration unit will be started.

[0184] It should be noted that this embodiment provides an exception handling mechanism. When the system diagnoses that the infrared thermal imager or the capacitive sensor has failed, it automatically calls the historical frost database and uses the pressure sensor data to predict the frost status; at the same time, a degraded execution timer defrost program is used to ensure basic operation and maintain basic defrost functions. During the defrost execution process, if the duration of a single defrost exceeds the safety threshold, the power supply of the four-way valve and the heater is immediately cut off, and the sound and light alarm device is triggered to prevent the equipment from overheating and damage through a double protection mechanism; at the same time, the fault self-check program is started to analyze the cause of the timeout. If the box temperature recovery rate increases abnormally during the defrost period, the system immediately turns off the heater and starts the backup refrigeration unit to suppress the temperature to ensure the safety of ultra-low temperature samples in the event of a failure. All abnormal events generate encrypted logs and upload them to the cloud operation and maintenance platform.

[0185] It is worth mentioning that it also includes:

[0186] If the pre-defrost logic is executed, the opening time of the four-way valve is extended according to the preset proportional threshold;

[0187] If the secondary defrost logic is executed, the heater is prohibited from turning on and defrosting is only done through the hot gas bypass circuit;

[0188] If it is in the gradient recovery refrigerator stage, the pulse maintenance mode is operated at the preset second frequency and the power regulation step is lowered.

[0189] It should be noted that this embodiment provides a method for applying to ultra-low temperature defrosting scenarios. As an implementation method, when operating in an ultra-low temperature scenario, the preheating time of the four-way valve is automatically extended to 150% of the normal value in the pre-defrost stage to avoid cold brittle cracking of the pipeline. After entering the secondary defrost stage, the PTC heater is forcibly disabled, and only a single hot gas bypass path is used to complete the defrosting operation to prevent a sudden rise in ambient temperature. In the gradient recovery stage, the power adjustment step of the compressor is reduced, thereby extending the time for the power to be adjusted to the set value and reducing the mechanical load under ultra-low temperature conditions; at the same time, the compressor is operated in low-frequency pulse mode to extend the proportion of shutdown insulation time and ensure the operational stability of the ultra-low temperature compressor. All parameter adjustments are automatically triggered by the ambient temperature to ensure the safe and stable operation of ultra-low temperature equipment.

[0190] The third aspect of the present invention provides a computer-readable storage medium, which includes a multi-sensor based intelligent adjustment and dynamic defrosting method program. When the multi-sensor based intelligent adjustment and dynamic defrosting method program is executed by a processor, the steps of the multi-sensor based intelligent adjustment and dynamic defrosting method as described in any one of the above items are implemented.

[0191] In summary, the present invention provides a method, system and medium for intelligent adjustment and dynamic defrosting based on multiple sensors. First, monitoring data is obtained in real time based on the multi-dimensional sensors deployed in the freezer. After preset data processing, a multi-source data matrix is ​​integrated; secondly, the weight coefficient of the frost model is dynamically adjusted according to the monitoring data, and the frost coefficient is quantified based on the frost model in combination with the multi-source data matrix; then, a three-level defrosting strategy is executed according to the frost coefficient, and the working parameters of the defrosting equipment such as the compressor, condensing fan or heater are adjusted respectively; finally, the compressor power step soft start and the electronic expansion valve PID dynamic adjustment are used to achieve precise control of the box temperature, and the condensing fan speed is automatically optimized based on the ambient temperature and humidity to achieve energy efficiency optimization. The present invention triggers the corresponding defrost operation through the frost coefficient, thereby improving the effectiveness of defrosting and the stability of the box temperature; and by dynamically adjusting the weight, the adaptability to special scenarios is enhanced.

[0192] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0193] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A multi-sensor based intelligent adjustment and dynamic defrosting method, characterized in that: The method comprises: Based on the preset collection period, monitoring data is obtained from the sensors deployed in the freezer; Based on the preset monitoring data processing logic, a data matrix is ​​formed according to the monitoring data; Based on the preset threshold judgment logic, the weight of the frost model is adjusted according to the monitoring data; Based on a preset frost model, a frost coefficient is obtained according to the data matrix and the frost model weights; Based on the preset three-level defrost strategy, differentiated defrost instructions are executed according to the frost coefficient to control the working status of the compressor, condensing fan or heater; After the defrost command is executed, the box temperature is controlled according to the preset compressor power soft start and PID dynamic adjustment of the electronic expansion valve opening; After the box temperature stabilizes, the compressor is switched to a preset pulse maintenance mode, and the condensing fan speed is dynamically adjusted according to the ambient temperature and humidity.

2. The multi-sensor based intelligent adjustment and dynamic defrosting method according to claim 1, characterized in that: The preset monitoring data processing logic is used to form a data matrix based on the monitoring data, specifically: A three-dimensional temperature field model is constructed by distributing a temperature sensor array at the evaporator inlet, evaporator outlet, at least six spatial coordinate points inside the box, and on the condenser surface; Scan the evaporator fin surface with an infrared thermal imager to generate a thermal map of frost thickness distribution; The dielectric constant of the frost layer is monitored in real time by a capacitive sensor, and the dielectric constant change rate of the frost layer is obtained by combining it with a preset reference value of the dielectric constant of the frost layer. The suction pressure, discharge pressure and refrigerant liquid level pressure are continuously collected through the pressure transmitter, and the pressure change rate is calculated based on the preset unit time; Based on the three-phase current waveform and vibration spectrum data of the compressor, the electromechanical state data is obtained; The temperature field model, frost layer thickness distribution thermal map, frost layer dielectric constant change rate, pressure change rate and electromechanical state data are integrated into a multidimensional data matrix.

3. The multi-sensor based intelligent adjustment and dynamic defrosting method according to claim 2, characterized in that: The preset threshold judgment logic is based on the monitoring data, and the frost model weight is adjusted, specifically: If the humidity information of the monitoring data exceeds a preset humidity threshold, the frost thickness weight of the frost condition model weight is increased to a preset frost thickness weight reference value; If the lowest temperature value of the cabinet in the monitoring data is lower than the preset temperature threshold, the pressure weight of the frost model weight is increased to the preset pressure weight reference value; According to the adjusted frost thickness weight or the pressure weight, the frost layer density weight of the frost condition model weight is lowered.

4. The multi-sensor based intelligent adjustment and dynamic defrosting method according to claim 3, characterized in that: The frost coefficient is obtained based on the preset frost model according to the data matrix and the frost model weight, specifically: Obtaining a maximum frost temperature difference according to a frost layer thickness distribution thermodynamic map of the data matrix; Calculating the product of the maximum frost temperature difference and the frost thickness weight to obtain a frost thickness factor; Calculating the product of the pressure change rate of the data matrix and the pressure weight to obtain a pressure factor; Calculating the product of the frost layer dielectric constant change rate of the data matrix and the frost layer density weight to obtain a density factor; The sum of the frost thickness factor, the pressure factor, and the density factor is calculated and processed according to a preset normalization mapping to obtain a frost condition coefficient.

5. The multi-sensor based intelligent adjustment and dynamic defrosting method according to claim 1, characterized in that: The three-stage defrost strategy based on the preset setting executes differentiated defrost instructions according to the frost coefficient to control the working state of the compressor, condensing fan or heater, specifically including: If the frost coefficient is lower than a preset first frost threshold, dynamically adjusting the operating frequency of the compressor according to the box temperature; If the frost condition coefficient is between a preset first frost condition threshold and a preset second frost condition threshold, a pre-defrost logic is activated, wherein the pre-defrost logic includes adjusting the condensing fan speed to a preset first speed, turning off the internal circulation fan and opening the four-way valve; If the frost coefficient is higher than the preset second frost threshold, the secondary defrost logic is activated, wherein the secondary defrost logic first activates the hot gas bypass circuit, introduces the compressor exhaust into the evaporator through the four-way valve, and starts the heater to implement secondary heating after the basic structure of the frost layer collapses.

6. The multi-sensor based intelligent adjustment and dynamic defrosting method according to claim 1, characterized in that: It also includes a gradient recovery refrigeration mechanism, specifically: After the defrosting operation is completed, the four-way valve is maintained in an open state according to a preset first time length; Starting the compressor according to a preset first power; According to the preset power adjustment step, the compressor output power is adjusted until the preset second power is reached; Calculate the temperature difference between the real-time box temperature and the target box temperature; According to the temperature difference information, the opening of the electronic expansion valve is adjusted according to a preset PID control algorithm; When the real-time box temperature returns to within the preset deviation range of the target box temperature and the duration exceeds the preset stability time threshold, it is determined to be in a stable box temperature state.

7. An intelligent adjustment and dynamic defrosting system based on multiple sensors, characterized in that: The system includes a memory and a processor. The memory includes a multi-sensor based intelligent adjustment and dynamic defrosting method program. When the multi-sensor based intelligent adjustment and dynamic defrosting method program is executed by the processor, the following steps are implemented: Based on the preset collection period, monitoring data is obtained from the sensors deployed in the freezer; Based on the preset monitoring data processing logic, a data matrix is ​​formed according to the monitoring data; Based on the preset threshold judgment logic, the weight of the frost model is adjusted according to the monitoring data; Based on a preset frost model, a frost coefficient is obtained according to the data matrix and the frost model weights; Based on the preset three-level defrost strategy, differentiated defrost instructions are executed according to the frost coefficient to control the working status of the compressor, condensing fan or heater; After the defrost command is executed, the box temperature is controlled according to the preset compressor power soft start and PID dynamic adjustment of the electronic expansion valve opening; After the box temperature stabilizes, the compressor is switched to a preset pulse maintenance mode, and the condensing fan speed is dynamically adjusted according to the ambient temperature and humidity.

8. The multi-sensor based intelligent adjustment and dynamic defrosting system according to claim 7, characterized in that: The preset monitoring data processing logic is used to form a data matrix based on the monitoring data, specifically: A three-dimensional temperature field model is constructed by distributing a temperature sensor array at the evaporator inlet, evaporator outlet, at least six spatial coordinate points inside the box, and on the condenser surface; Scan the evaporator fin surface with an infrared thermal imager to generate a thermal map of frost thickness distribution; The dielectric constant of the frost layer is monitored in real time by a capacitive sensor, and the dielectric constant change rate of the frost layer is obtained by combining it with a preset reference value of the dielectric constant of the frost layer. The suction pressure, discharge pressure and refrigerant liquid level pressure are continuously collected through the pressure transmitter, and the pressure change rate is calculated based on the preset unit time; Based on the three-phase current waveform and vibration spectrum data of the compressor, the electromechanical state data is obtained; The temperature field model, frost layer thickness distribution thermal map, frost layer dielectric constant change rate, pressure change rate and electromechanical state data are integrated into a multidimensional data matrix.

9. The multi-sensor based intelligent adjustment and dynamic defrosting system according to claim 8, characterized in that: The preset threshold judgment logic is based on the monitoring data, and the frost model weight is adjusted, specifically: If the humidity information of the monitoring data exceeds a preset humidity threshold, the frost thickness weight of the frost condition model weight is increased to a preset frost thickness weight reference value; If the lowest temperature value of the cabinet in the monitoring data is lower than the preset temperature threshold, the pressure weight of the frost model weight is increased to the preset pressure weight reference value; According to the adjusted frost thickness weight or the pressure weight, the frost layer density weight of the frost condition model weight is lowered.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer-readable storage medium includes a multi-sensor-based intelligent adjustment and dynamic defrosting method program. When the multi-sensor-based intelligent adjustment and dynamic defrosting method program is executed by a processor, the steps of the multi-sensor-based intelligent adjustment and dynamic defrosting method as described in any one of claims 1 to 6 are implemented.

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