A multi-parameter collaborative sensing and resonance intelligent wake-up control method for refrigerators

Abstract

The present invention relates to the field of intelligent control and discloses a multi-parameter collaborative perception and resonant intelligent wake-up control method for refrigerators. The method comprises the following steps: dynamically acquiring temperature, humidity, and door status signals and adjusting their weights; extracting the resonant frequency offset in combination with the compressor vibration spectrum; performing intelligent wake-up and refrigeration duration prediction based on this feature; and introducing credibility assessment, a self-learning mapping model, and an interference filtering mechanism. Compared to traditional single-parameter or fixed-threshold control methods, this solution drives the control logic through changes in physical quantities. When triggered by a door status event, it forms a three-dimensional linkage relationship between environmental status, physical signals, and control decisions. This allows for timely perception and accurate response to changes in thermal characteristics within the refrigerator, overcoming the challenges of data fragmentation and hysteresis control.

Description

Technical Field

[0001] The present invention relates to a multi-parameter collaborative sensing and resonance intelligent awakening control method for a refrigerator, belonging to the technical field of intelligent control. Background Art

[0002] Freezers are widely used in homes, businesses, and industries, primarily for storing items such as food and medicine that require a low-temperature environment. Traditional freezer control systems rely on simple feedback control based on individual sensors collecting data such as temperature, humidity, and door status. However, with changing usage needs and complex environmental conditions, these traditional control methods have limitations in improving freezer energy efficiency, stability, and intelligence.

[0003] In the prior art, refrigerator control systems typically use fixed thresholds or single sensing signals to trigger the cooling process. For example, in solutions that trigger cooling based on temperature thresholds, when the refrigerator door is opened, the temperature changes rapidly, often resulting in a delayed response of the control system to the temperature change, making the timing of cooling inaccurate and causing unnecessary energy waste. In addition, existing refrigerator control methods fail to effectively consider the complex interactions between door state changes, humidity fluctuations, and the cooling process, which leads to delayed control instructions and insufficient energy efficiency optimization. Specifically, these existing solutions have the following problems:

[0004] 1. Fragmented perception of multiple parameters: Current refrigerator control systems often rely on independent sensors to monitor parameters such as temperature, humidity, and door status. This approach fails to effectively analyze the dynamic coupling between these parameters. In particular, the interaction between humidity surges and temperature fluctuations at the moment of door opening cannot be captured and analyzed in real time, resulting in slow control system response and reduced energy efficiency.

[0005] 2. Energy efficiency conflicts in the wake-up mechanism: Traditional freezer control solutions often use fixed temperature thresholds (e.g., starting refrigeration when the temperature exceeds 5°C). However, this approach cannot dynamically adjust the wake-up strategy based on the actual thermal characteristics of the materials inside the freezer (e.g., heat capacity, moisture content, etc.). This not only leads to frequent starts and stops, but can also cause over-cooling, further wasting energy.

[0006] 3. Insufficient handling of external environmental interference: Existing technologies usually rely solely on temperature sensor feedback and lack adaptive response to external environmental changes (such as high humidity and high temperature weather) and their impact on the internal state of the refrigerator, which can easily cause control system instability or misjudgment.

[0007] To address these issues, the industry typically reduces errors by improving sensor accuracy and introducing filtering algorithms. However, these improvements still have drawbacks: improved sensor accuracy often increases costs, while filtering algorithms often cannot fully adapt to complex operating conditions when dealing with signal interference, still creating the risk of false wakeups and unstable control. Summary of the Invention

[0008] The present invention provides a method for multi-parameter collaborative sensing and resonant intelligent wake-up control of a refrigerator, the main purpose of which is to solve the problems of instability, low energy efficiency and insufficient system robustness of multi-parameter collaborative sensing and intelligent wake-up control.

[0009] To achieve the above objectives, the present invention provides a refrigerator multi-parameter collaborative sensing and resonant intelligent wake-up control method, comprising the following steps:

[0010] Step 1: Perform multi-parameter collaborative sensing, including real-time acquisition of the temperature signal, humidity signal, and door status signal inside the refrigerator, and dynamically assign weights of the temperature signal, humidity signal, and door status signal in subsequent control decisions according to the current operating status of the refrigerator. When the refrigerator door is detected to be open, the weight of the humidity signal W is H The door opening time t is adjusted according to the following formula:

[0011] W H (t) = W H_initial ×e -λt ,

[0012] Among them, W H_initial is the initial humidity weight when the door is opened, and λ is the preset humidity weight attenuation coefficient;

[0013] Step 2: Extract resonance features based on the vibration signal, including real-time acquisition of the vibration signal of the refrigerator compressor during operation, and performing spectrum analysis on the vibration signal to obtain a resonance frequency offset Δf that characterizes changes in the thermal characteristics of the material inside the refrigerator. The resonance frequency offset Δf is obtained by comparing the currently detected resonance frequency of the compressor with a preset reference resonance frequency.

[0014] Step 3: Make an intelligent wake-up decision based on the resonant frequency offset, including: when the absolute value of the detected resonant frequency offset Δf is greater than the preset offset threshold Δf threshold When the refrigerator is triggered to enter the cooling operation state, the duration required for this cooling is predicted based on the statistical relationship between the resonant frequency offset and the cooling time under different load conditions in the historical operation data;

[0015] Step 4: Identify and filter environmental interference, including analyzing the temporal correlation between the door status signal and the temperature and humidity change data of the external environment, and determining whether the fluctuation of the internal parameters of the refrigerator is caused by actual load changes or interference from external environmental factors. When the external environmental humidity is detected to increase by more than a preset amplitude within a preset time interval while the refrigerator door remains closed, it is determined to be interference from external environmental factors, and the parameter weight is adjusted or the triggering of the cooling operation is delayed.

[0016] Step 5: Build and dynamically update a multi-parameter coupling mapping model. The model records the correlation data between the temperature, humidity, and compressor resonant frequency when the refrigerator is operating in different geographical locations, and updates it based on the real-time collected data. In the subsequent control process, the model is used to adjust the operating mode and control parameters of the refrigerator according to the current geographical location and perception parameters.

[0017] Preferably, in step 3, the time T required for the current cooling is predicted. cool Calculated according to the following formula: T cool =k×|Δf|, where k is a load-related proportional coefficient determined based on historical operating data, and |Δf| is the absolute value of the resonant frequency offset.

[0018] Preferably, in step 2, the preset reference resonant frequency is obtained based on a spectrum analysis of a compressor vibration signal of the refrigerator during no-load steady-state operation.

[0019] Preferably, in step 3, the offset threshold Δf threshold The value range is 2Hz to 5Hz.

[0020] Preferably, in step 4, the preset time interval is 10 seconds to 30 seconds, and the preset amplitude is 5% RH to 15% RH.

[0021] Preferably, in step 5, the control parameters of the refrigerator are adaptively optimized and adjusted in combination with the geographical location information of the refrigerator, specifically including: when the refrigerator is located in a high altitude area, adjusting the mapping relationship between temperature and compressor starting frequency in the multi-parameter coupling mapping model.

[0022] Preferably, after step 2, the step of performing a credibility assessment on the resonant frequency offset Δf is further included, specifically comprising: comparing the currently acquired resonant frequency offset Δf with the fluctuation range of the resonant frequency offset under the same load state in the historical operation database; if it exceeds the fluctuation range, reducing the weight of the resonant frequency offset Δf in the intelligent wake-up decision.

[0023] Preferably, when the credibility of the resonant frequency offset Δf is lower than a preset credibility threshold, the intelligent wake-up decision step switches to a backup strategy that relies only on the temperature signal and the door status signal for wake-up judgment.

[0024] Preferably, it also includes a refrigerator group collaborative control step based on geographic location information, specifically including: forming a group of multiple refrigerators within a preset geographical range, sharing their respective operating status data and environmental perception data between refrigerators in the group, and collaboratively adjusting their respective wake-up thresholds and refrigeration time prediction parameters according to the overall change trend of the regional environment.

[0025] Preferably, when the credibility of the resonant frequency offset Δf is continuously lower than a preset credibility threshold for a preset number of times, a self-check procedure of the refrigerator is triggered and an alarm message of sensor abnormality is issued.

[0026] Compared with the problems described in the background technology, the beneficial effects of the present invention are:

[0027] 1. Through dynamic detection of the resonant frequency offset Δf and a weighted distribution mechanism triggered by door status, the hysteresis of traditional temperature threshold control is overcome, achieving real-time perception of the thermal characteristics of materials inside the refrigerator and optimizing energy efficiency. The compressor vibration signal is correlated with the ice crystal formation rate. The physical meaning of Δf is extracted through spectrum analysis, directly reflecting the material phase change process. This avoids the error accumulation of traditional solutions that rely on temperature gradient inference. The door status event triggers real-time adjustment of the temperature and vibration signal acquisition frequency. The exponential decay function suppresses interference from sudden humidity changes, forming a three-dimensional collaborative logic of physical signal, environmental status, and control weight, solving the problem of traditional multi-sensor data fragmentation.

[0028] 2. A self-comparison mechanism between the resonant frequency credibility coefficient and historical data is introduced to build an anti-interference self-healing system, significantly improving the robustness under complex working conditions. For example, when the Δf deviation exceeds the historical benchmark threshold, the decision weight of low-credibility data is dynamically reduced. Combined with the switching of the backup wake-up strategy, the false triggering problem caused by the superposition of sensor drift and environmental noise is solved. The inherent frequency response of the cavity is detected by the swept frequency excitation signal to achieve online compensation for sensor performance degradation.

[0029] 3. The data sharing of geographically adjacent refrigerators and the temperature-resonance frequency correlation matrix are used to achieve global optimization and dynamic expansion of the cooling strategy. For example, when frequent door openings of adjacent refrigerators are detected, the cooling threshold is adjusted in advance to avoid power grid shock caused by simultaneous high-load operation of multiple devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of the architecture of the refrigerator multi-parameter collaborative sensing and resonant intelligent wake-up control method of the present invention.

[0031] Figure 2 This is a flow chart of the intelligent wake-up decision-making process based on vibration signals of the present invention.

[0032] Figure 3 This is a structural block diagram of the refrigerator control system of the present invention.

[0033] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0034] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0035] The present invention provides a method for cooperatively sensing and intelligently waking up refrigerators using multiple parameters, including the following steps:

[0036] Step 1: Perform multi-parameter collaborative sensing, including real-time acquisition of the temperature signal, humidity signal, and door status signal inside the refrigerator, and dynamically assign weights of the temperature signal, humidity signal, and door status signal in subsequent control decisions according to the current operating status of the refrigerator. When the refrigerator door is detected to be open, the weight of the humidity signal W is H The door opening time t is adjusted according to the following formula:

[0037] W H (t) = W H_initial ×e -λt ,

[0038] Among them, W H_initial is the initial humidity weight when the door is opened, and λ is the preset humidity weight attenuation coefficient;

[0039] Step 2: Extract resonance features based on the vibration signal, including real-time acquisition of the vibration signal of the refrigerator compressor during operation, and performing spectrum analysis on the vibration signal to obtain a resonance frequency offset Δf that characterizes changes in the thermal characteristics of the material inside the refrigerator. The resonance frequency offset Δf is obtained by comparing the currently detected resonance frequency of the compressor with a preset reference resonance frequency.

[0040] Step 3: Make an intelligent wake-up decision based on the resonant frequency offset, including: when the absolute value of the detected resonant frequency offset Δf is greater than the preset offset threshold Δf threshold When the refrigerator is triggered to enter the cooling operation state, the duration required for this cooling is predicted based on the statistical relationship between the resonant frequency offset and the cooling time under different load conditions in the historical operation data;

[0041] Step 4: Identify and filter environmental interference, including analyzing the temporal correlation between the door status signal and the temperature and humidity change data of the external environment, and determining whether the fluctuation of the internal parameters of the refrigerator is caused by actual load changes or interference from external environmental factors. When the external environmental humidity is detected to increase by more than a preset amplitude within a preset time interval while the refrigerator door remains closed, it is determined to be interference from external environmental factors, and the parameter weight is adjusted or the triggering of the cooling operation is delayed.

[0042] Step 5: Build and dynamically update a multi-parameter coupling mapping model. The model records the correlation data between the temperature, humidity, and compressor resonant frequency when the refrigerator is operating in different geographical locations, and updates it based on the real-time collected data. In the subsequent control process, the model is used to adjust the operating mode and control parameters of the refrigerator according to the current geographical location and perception parameters.

[0043] Preferably, in step 3, the time T required for the current cooling is predicted. cool Calculated according to the following formula: T cool =k×|Δf|, where k is a load-related proportional coefficient determined based on historical operating data, and |Δf| is the absolute value of the resonant frequency offset.

[0044] Preferably, in step 2, the preset reference resonant frequency is obtained based on a spectrum analysis of a compressor vibration signal of the refrigerator during no-load steady-state operation.

[0045] Preferably, in step 3, the offset threshold Δf threshold The value range is 2Hz to 5Hz.

[0046] Preferably, in step 4, the preset time interval is 10 seconds to 30 seconds, and the preset amplitude is 5% RH to 15% RH.

[0047] Preferably, in step 5, the control parameters of the refrigerator are adaptively optimized and adjusted in combination with the geographical location information of the refrigerator, specifically including: when the refrigerator is located in a high altitude area, adjusting the mapping relationship between temperature and compressor starting frequency in the multi-parameter coupling mapping model.

[0048] Preferably, after step 2, the step of performing a credibility assessment on the resonant frequency offset Δf is further included, specifically comprising: comparing the currently acquired resonant frequency offset Δf with the fluctuation range of the resonant frequency offset under the same load state in the historical operation database; if it exceeds the fluctuation range, reducing the weight of the resonant frequency offset Δf in the intelligent wake-up decision.

[0049] Preferably, when the credibility of the resonant frequency offset Δf is lower than a preset credibility threshold, the intelligent wake-up decision step switches to a backup strategy that relies only on the temperature signal and the door status signal for wake-up judgment.

[0050] Preferably, it also includes a refrigerator group collaborative control step based on geographic location information, specifically including: forming a group of multiple refrigerators within a preset geographical range, sharing their respective operating status data and environmental perception data between refrigerators in the group, and collaboratively adjusting their respective wake-up thresholds and refrigeration time prediction parameters according to the overall change trend of the regional environment.

[0051] Preferably, when the credibility of the resonant frequency offset Δf is continuously lower than a preset credibility threshold for a preset number of times, a self-check procedure of the refrigerator is triggered and an alarm message of sensor abnormality is issued.

[0052] Example 1: This example describes in detail the specific steps of realizing intelligent wake-up control of a refrigerator by optimizing vibration spectrum analysis and data processing of environmental sensors.

[0053] Step 1: Multi-parameter collaborative sensing. First, the system collects real-time temperature, humidity, and door status signals from the refrigerator. After acquiring these signals, the system dynamically adjusts the weight of each signal in subsequent control decisions based on the refrigerator's current operating status. In particular, when the refrigerator door is open, the weight of the humidity signal is adjusted based on the length of time the door has been open. This adjustment process is performed using the following formula:

[0054] W H (t) = W Hinitial ×e -λt ,

[0055] Among them, W H (t) is the humidity signal weight at the time the door is opened, W Hinitial is the initial humidity weight when the door is opened, λ is the preset humidity attenuation coefficient, and t is the door opening time (in seconds). The core function of this formula is to dynamically adjust the impact of humidity on cooling startup, thereby avoiding control lag caused by sudden humidity changes and ensuring real-time response of the control strategy.

[0056] In this formula, the variables λ and W HinitialThe value range of λ should be determined through experimental data. In practical applications, the selection of λ takes into account the rate of humidity change after the door is opened, ensuring that the impact of humidity on the refrigerator control is not excessive, thereby avoiding unnecessary energy consumption. Specifically, the humidity attenuation coefficient λ is a parameter used to adjust the weight of the humidity signal as the door is opened. Its purpose is to ensure that humidity changes do not significantly affect the timing of cooling start, thereby avoiding unnecessary energy waste. To accurately determine the value of the humidity attenuation coefficient λ, the following experimental process was used. Experimental data collection: The humidity change after multiple refrigerator doors are opened was tested, and the impact of the humidity signal on cooling start was monitored in real time under different attenuation coefficients. The experiment controlled the duration of the refrigerator door opening to collect humidity change data and record the response time of cooling start timing. Data analysis: Through comparative analysis of experimental data, different attenuation coefficient λ values ​​were selected to observe the impact of humidity changes on cooling start timing. To avoid control lag caused by sudden changes in humidity, the attenuation coefficient λ should be between 0.01 and 0.1. The specific value was determined through experimentation. Selection of the humidity attenuation coefficient: Experimental results show that the attenuation coefficient λ should be determined based on the rate of humidity change after the door is opened. A lower attenuation coefficient is suitable for situations where humidity changes slowly, while a higher attenuation coefficient is suitable for environments where humidity changes rapidly. In practical applications, the attenuation coefficient is usually between 0.01 and 0.1 and needs to be adjusted according to actual environmental conditions (such as the rate of change of air humidity, the type of refrigerator, etc.); Dynamic adjustment: In order to adapt to humidity changes under different working conditions, the attenuation coefficient λ can be dynamically adjusted based on the real-time monitoring of humidity changes. For example, in an environment with rapid humidity changes, the attenuation coefficient λ can be appropriately increased to ensure that the impact of sudden humidity changes on the control system is effectively suppressed. These are all extended implementation methods known to ordinary technicians in this field.

[0057] Step 2: Extract the resonance characteristics of the vibration signal. During operation, the refrigerator compressor generates a certain vibration signal. Spectral analysis of these vibration signals can be used to extract the resonant frequency offset Δf, a key indicator of changes in the thermal properties of the material inside the refrigerator. Specifically, the offset Δf is determined by comparing the currently detected compressor vibration frequency with a pre-set baseline resonant frequency.

[0058] To further improve data processing accuracy, a filtering mechanism is implemented to mitigate the influence of external environmental interference. Whenever the freezer door is detected to be closed, any change in external humidity exceeding a preset threshold is considered environmental interference, and the vibration signal weighting is adjusted accordingly.

[0059] Step 3: Intelligent wake-up decision. In this embodiment, the refrigerator's intelligent wake-up decision determines whether to start cooling based on the extracted resonant frequency offset Δf. When the absolute value of the offset |Δf| is greater than a preset threshold, the system triggers cooling operation. The cooling duration prediction is based on the statistical relationship between the resonant frequency offset and cooling duration under different load conditions in historical operating data. The specific cooling duration prediction formula is:

[0060] T c =k×|Δf|,

[0061] Among them, T c is the predicted cooling time, k is the proportional coefficient, and |Δf| is the absolute value of the current frequency offset. The proportional coefficient k is obtained through learning from historical data, and reflects the impact of frequency offset on cooling time under different load conditions. It should be emphasized that the value of variable k should be determined according to the specific load state of the refrigerator, the type of refrigerator and the application environment, and avoid using fixed values. The reasonable setting of these values ​​is the key to ensuring that the cooling strategy can adapt to different working conditions. Specifically, the proportional coefficient k is used to predict the cooling time, and its value is closely related to the load state of the refrigerator and the relationship between the frequency offset and the cooling time in the historical operating data. Specifically, the proportional coefficient k is obtained through the following experimental process, such as experimental data collection: First, under different load conditions (such as no load, light load, heavy load), multiple refrigerators are operated under a standard environment, and the operating data of each refrigerator is recorded, including the vibration frequency offset Δf and the corresponding cooling time T c . Data processing: Through statistical analysis methods (such as regression analysis, least squares method), the relationship between the frequency offset and the refrigeration time under each load state is calculated, and the value of the proportional coefficient k is fitted according to the experimental data. Applicable range of the proportional coefficient: According to experimental results, the value range of the proportional coefficient k is usually between 0.1 and 1, and the specific value depends on factors such as the load state of the refrigerator, the type of compressor, and environmental conditions. For example, under heavier loads, the value of the proportional coefficient k may be higher because the refrigeration time is more affected by load changes; while under light loads, the value of the proportional coefficient is lower. At the same time, in order to adapt to different environmental conditions and load states, the proportional coefficient kk can be dynamically adjusted according to real-time operating data to ensure the accuracy of the refrigeration time prediction. For example, if the load state of the refrigerator changes, the system will update the proportional coefficient in real time to maintain the accuracy of the prediction, which are all extended implementation methods known to ordinary technicians in this field.

[0062] Step 4: Identify and filter environmental interference. During the operation of the refrigerator, external environmental factors such as humidity changes may interfere with internal sensor data, thereby affecting control decisions. To address this issue, this embodiment proposes an environmental interference identification and filtering mechanism. By analyzing the temporal correlation between the door status signal and the external temperature and humidity change data, it is determined whether the fluctuation of the internal parameters of the refrigerator is caused by load changes or external interference. If the external humidity changes by more than a preset amplitude while the refrigerator door remains closed, the system will determine that it is an external environmental factor interference and adjust the control strategy.

[0063] Step 5: Construction and update of a multi-parameter coupling mapping model. To achieve adaptive control of the refrigerator in different environments, this embodiment further constructs and dynamically updates a multi-parameter coupling mapping model. The model records the temperature, humidity, and resonant frequency data of the refrigerator during operation in different geographical locations and establishes a correlation between them. Based on the real-time collected data, the model will update the operating mode and control parameters of the refrigerator. When the refrigerator is located in different geographical locations, the model will adjust the mapping relationship between temperature and compressor starting frequency based on the geographical location information to ensure the optimal energy efficiency of the refrigerator in different environments.

[0064] If the reliability of the resonant frequency offset Δf is low, the system compares and evaluates historical data with the current data. If it exceeds the fluctuation range, the weight of this data in the intelligent wake-up decision is reduced. If the reliability falls below a preset threshold, the refrigerator switches to a backup strategy that relies solely on temperature and door status signals to ensure control stability and reliability. These are all extended implementations known to those skilled in the art.

[0065] Example 2: Figure 1As shown, the system architecture of the multi-parameter collaborative perception and resonant intelligent awakening control method for refrigerators of the present invention is mainly composed of a data acquisition layer, a data processing layer, a decision execution layer, and a geographic adaptation module. In the data acquisition layer, various parameters of the refrigerator during operation are collected in real time through temperature sensors, humidity sensors, vibration sensors, and door status switches, and these signals are input into the collaborative perception unit for preliminary processing. The output of the collaborative perception unit enters the data processing layer. First, the weights of each parameter are adjusted according to the current state of the refrigerator through the dynamic weight distributor. The vibration signal also needs to pass through the spectrum analysis engine to extract the resonant frequency offset Δf, and the credibility of the offset is evaluated by the credibility assessment module. The geographic adaptation module uses the GPS positioning module to obtain geographic location information, and adjusts the compressor frequency in high altitude mode in combination with the multi-parameter mapping library. In the decision execution layer, the system judges the result based on the threshold. If the condition is met, the refrigeration time is predicted and the compressor controller is controlled to start refrigeration; if the condition is not met, interference filtering analysis is performed, and parameter weight correction may be performed, thereby affecting the parameter setting of the dynamic weight distributor. As Figure 2 As shown in the figure, the intelligent wake-up decision process based on vibration signals starts by detecting the vibration signal and then obtaining the resonant frequency offset. The system then determines whether the offset is greater than the threshold. If the judgment result is yes, cooling is triggered and the process ends. If the judgment result is no, the system enters the waiting state and the process ends. Figure 3 The block diagram of the freezer control system (see Figure 1) illustrates the main components of the freezer control system, including temperature and humidity sensors, door status sensors, and vibration signal sensors as input terminals. These sensors transmit the collected information to the freezer controller. Based on this input information, the freezer controller makes intelligent wake-up decisions and ultimately drives the refrigeration system.

[0066] Example 3: In this example, real-time temperature, humidity, and door status signals from the refrigerator are collected to ensure that the acquisition and dynamic adjustment of these signals accurately reflect the refrigerator's current operating status. By setting dynamic weight allocation rules, the weights of different sensor signals can be effectively adjusted in real time based on actual operating conditions.

[0067] For example, when the refrigerator door is opened, the weight of the humidity signal is adjusted according to the door opening time. This adjustment is achieved by the following formula:

[0068] W H (t) = W H,initial ·e -λt ,

[0069] Where: W H (t) is the humidity signal weight at the door opening moment; W H,initialis the initial humidity weight when the door is opened; λ is the preset humidity weight attenuation coefficient; t is the door opening time (seconds), W H (t) represents the humidity signal weight, reflecting the impact of humidity on refrigeration control; W H,initial is the initial humidity weight, representing the immediate impact of humidity on cooling when the freezer door is opened. λ is the attenuation coefficient, typically ranging from 0.01 to 0.1, determined based on experimental results to accommodate varying rates of humidity change. This gradually reduces the impact of humidity on cooling startup, preventing sudden humidity changes from undesirably affecting the control system.

[0070] During the operation of the refrigerator, the resonance frequency offset Δf is extracted by performing spectrum analysis on the compressor vibration signal, which serves as an important indicator of the change in the thermal characteristics of the materials inside the refrigerator. The resonance frequency offset Δf is calculated by comparing the currently detected compressor vibration frequency with the pre-set reference resonance frequency. The formula is as follows:

[0071] Δf=f current -f baseline ,

[0072] Where: f current is the currently detected vibration frequency of the compressor; f baseline is the baseline resonant frequency of the freezer when operating unloaded and in steady state. This formula can be used to determine the actual thermal property changes of the material inside the freezer. To improve accuracy, a credibility assessment of the resonant frequency offset is also required to ensure data accuracy.

[0073] When the absolute value of the detected resonant frequency offset Δf is greater than the preset offset threshold Δf threshold When , the refrigerator is triggered to enter the cooling operation state. The prediction of cooling time is based on the statistical relationship between the resonant frequency offset and cooling time under different load conditions in the historical operation data. The specific formula is:

[0074] T c =k·|Δf|,

[0075] Where: T c is the predicted cooling time; k is the load-related proportionality factor; |Δf| is the absolute value of the resonant frequency offset. In this formula, k is a coefficient statistically derived from historical data, reflecting the impact of frequency offset on cooling time under different load conditions. This proportionality factor, k, is determined by analyzing the relationship between resonant frequency offset and actual cooling time under different loads. Its value typically ranges from 0.1 to 1, depending on the specific refrigerator type and operating conditions.

[0076] To effectively filter out external environmental interference, this embodiment monitors humidity changes and analyzes them in conjunction with door status signals. Specifically, when the refrigerator door is closed, if the external humidity exceeds a preset range within a preset time interval, it is considered an external environmental interference factor. In this case, the control strategy needs to be adjusted to delay or adjust the cooling trigger timing. The preset range for determining the humidity change range is 5% RH to 15% RH to accommodate most normal environmental fluctuations.

[0077] By recording the operating data of the refrigerator at different geographical locations, this embodiment constructs and dynamically updates a multi-parameter coupling mapping model. The model can optimize the operating mode and control parameters of the refrigerator based on the geographical location and the temperature, humidity and compressor resonant frequency data collected in real time. For example, in high-altitude areas, the mapping relationship between the refrigerator's compressor start-up frequency and temperature may change, so it is necessary to adjust the control strategy in the mapping model based on the geographical information. In this way, the refrigerator can automatically adjust its operating parameters according to different environmental conditions to ensure optimal energy efficiency and cooling effect, which are all extended implementation methods that can be known to ordinary technicians in this field.

[0078] Example 4: When the refrigerator door is open, the weight of the humidity signal is adjusted according to the door opening time. The formula is as follows:

[0079] W H (t) = W Hinitial ×e -λt ,

[0080] Among them, W H (t) is the humidity signal weight at the time the door is opened, W Hinitial is the initial humidity weight when the door is opened, λ is the preset humidity weight attenuation coefficient, and t is the door opening duration (in seconds). This formula dynamically adjusts the impact of humidity on refrigeration startup, thereby avoiding control lag caused by sudden humidity changes and ensuring real-time system response. The value of λ ranges from 0.01 to 0.1. This range was determined through actual experiments. Specifically, the experiment simulated the humidity changes after the refrigerator door was opened, set different attenuation coefficients λ, and measured the impact of humidity changes on refrigeration startup timing. Analysis of the experimental data found that when λ is set between 0.01 and 0.1, humidity fluctuations can be minimized while ensuring a rapid response, while avoiding interference from humidity fluctuations on the control system. Therefore, this range was selected as the applicable attenuation coefficient to ensure stable system operation in practical applications. The attenuation coefficient λ is used to control the impact of humidity on refrigeration system startup, ensuring that humidity changes during the door opening process do not significantly affect the refrigerator startup timing.

[0081] When Δf=f current -fbaseline In, f current is the currently detected vibration frequency of the compressor, f baseline is the reference vibration frequency in the no-load steady state. According to this formula, the calculated Δf represents an important indicator of the change in the thermal characteristics inside the refrigerator. To ensure the accuracy of the calculation method, this embodiment adds an evaluation mechanism for the credibility of the resonant frequency offset. The specific steps are as follows: Each time Δf is detected, the system will compare the current value with the offset range in the historical operating data. If it exceeds this range, the credibility of the data is low; when the credibility of Δf is lower than the preset threshold, the intelligent wake-up decision switches to the backup strategy, relying only on the temperature and door status signals for judgment; this measure effectively avoids false triggering due to sensor drift or environmental noise, and improves the robustness and stability of the system.

[0082] The prediction formula for cooling time T c =k×|Δf|, T c is the predicted cooling time, k is the proportional coefficient related to the load, and |Δf| is the absolute value of the resonant frequency offset. To ensure the applicability of this formula, this embodiment further clarifies the setting process of the proportional coefficient k. Through statistical analysis of historical data, k depends on the load condition of the refrigerator and the relationship between the frequency offset and the cooling time in the historical operation data. In this embodiment, the value of the proportional coefficient k is obtained by statistical analysis of the historical operation data of the refrigerator. Specifically, k reflects the relationship between the load state of the refrigerator and the frequency offset of the vibration signal. In the experiment, the vibration frequency data of the refrigerator under different load conditions were collected, and the corresponding cooling time was compared. Through regression analysis method, it was determined that the resonant frequency offset Δf and the cooling time T under different load conditions are related. c The statistical relationship between them is used to derive a proportional coefficient k suitable for a specific freezer type and load state. This proportional coefficient k will be dynamically adjusted according to the actual operating data of the freezer to improve the accuracy of the refrigeration time prediction and ensure that the system can operate stably under different load and environmental conditions. And in order to ensure that the proportional coefficient can flexibly adapt to different freezer types and usage environments, this embodiment adds an adaptive adjustment mechanism based on real-time data when calculating k. Specifically, the load state and environmental conditions (such as temperature, humidity, etc.) of the freezer will be fed back to the control system in real time, and the value range of k will be dynamically adjusted according to these data to further improve the accuracy of the refrigeration time prediction.

[0083] During the operation of the refrigerator, changes in the external environment (such as humidity fluctuations) may interfere with the internal sensor data and affect the control decision. In order to deal with this problem, the present embodiment adds a more detailed interference identification and filtering mechanism. The specific operation process is as follows: First, monitor the switch status of the refrigerator door. When the door remains closed, the system will analyze the changes in the external environmental humidity. When the external humidity exceeds the set range (such as 5% RH to 15% RH) within a preset time interval, the system will determine it as external interference. For the identified interference, the system will automatically adjust the weight of the humidity signal or delay the triggering of the refrigeration operation to avoid false triggering caused by external environmental fluctuations. This improvement measure further enhances the stability of the refrigerator system and avoids energy efficiency loss due to environmental fluctuations.

[0084] To further enhance the refrigerator's intelligence, this embodiment adds an adaptive optimization function based on geographic location information. By using a GPS module to obtain the refrigerator's geographic location information in real time and combining it with a multi-parameter mapping model, the refrigerator's control strategy can be optimized based on the local geographic environment. For example, when the refrigerator is located at a high altitude, the relationship between the refrigerator's compressor startup frequency and temperature may change. In this case, the system automatically adjusts the control parameters to better adapt to the special high-altitude environment. The introduction of this function not only improves the refrigerator's energy efficiency in different environments but also enhances the system's adaptability, ensuring stable operation under various complex operating conditions. Its specific implementation steps include: data acquisition: real-time collection of relevant data about the refrigerator's internal and external environments through temperature sensors, humidity sensors, vibration sensors, and door status switches; signal preprocessing: dynamic weighting of different signals is adjusted through a dynamic weight allocator, particularly dynamically adjusting the weight of the humidity signal when the door is open; spectrum analysis: spectrum analysis of the vibration signal, extraction of the resonant frequency offset, and credibility assessment; intelligent decision-making: based on the assessment results, if the resonant frequency offset exceeds a preset threshold, the refrigeration system is activated and the cooling time is predicted. If the credibility is low, it switches to a backup strategy, relying only on temperature and door status signals; environmental interference processing: real-time monitoring of external humidity changes, when interference is detected, adjust the control strategy to ensure stable operation of the system; geographic adaptive optimization: automatically adjust control parameters according to the geographical location of the refrigerator, and improve the working efficiency of the refrigerator in different environments. These are all extended implementation methods known to ordinary technicians in this field.

[0085] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-parameter collaborative sensing and resonance intelligent wake-up control method for a refrigerator, characterized in that: The following steps are involved: Step 1: Perform multi-parameter collaborative sensing, including real-time acquisition of the temperature signal, humidity signal, and door status signal inside the refrigerator, and dynamically assign weights of the temperature signal, humidity signal, and door status signal in subsequent control decisions according to the current operating status of the refrigerator. When the refrigerator door is detected to be open, the weight of the humidity signal W is H The door opening time t is adjusted according to the following formula: W H (t)=W H_initial ×e -λt , Among them, W H_initial is the initial humidity weight when the door is opened, and λ is the preset humidity weight attenuation coefficient; Step 2: Extract resonance features based on the vibration signal, including real-time acquisition of the vibration signal of the refrigerator compressor during operation, and performing spectrum analysis on the vibration signal to obtain a resonance frequency offset Δf that characterizes changes in the thermal characteristics of the material inside the refrigerator. The resonance frequency offset Δf is obtained by comparing the currently detected resonance frequency of the compressor with a preset reference resonance frequency. Step 3: Make an intelligent wake-up decision based on the resonant frequency offset, including: when the absolute value of the detected resonant frequency offset Δf is greater than the preset offset threshold Δf threshold When the refrigerator is triggered to enter the cooling operation state, the duration required for this cooling is predicted based on the statistical relationship between the resonant frequency offset and the cooling time under different load conditions in the historical operation data; Step 4: Identify and filter environmental interference, including analyzing the temporal correlation between the door status signal and the temperature and humidity change data of the external environment, and determining whether the fluctuation of the internal parameters of the refrigerator is caused by actual load changes or interference from external environmental factors. When the external environmental humidity is detected to increase by more than a preset amplitude within a preset time interval while the refrigerator door remains closed, it is determined to be interference from external environmental factors, and the parameter weight is adjusted or the triggering of the cooling operation is delayed. Step 5: Build and dynamically update a multi-parameter coupling mapping model. The model records the correlation data between the temperature, humidity, and compressor resonant frequency when the refrigerator is operating in different geographical locations, and updates it based on the real-time collected data. In the subsequent control process, the model is used to adjust the operating mode and control parameters of the refrigerator according to the current geographical location and perception parameters.

2. The refrigerator multi-parameter collaborative sensing and resonance intelligent wake-up control method according to claim 1 is characterized in that: In step 3, the time required for the current cooling is predicted to be T cool Calculated according to the following formula: T cool =k× | Δf|, where k is a load-related proportional coefficient determined based on historical operating data, and |Δf| is the absolute value of the resonant frequency offset.

3. The refrigerator multi-parameter collaborative sensing and resonance intelligent wake-up control method according to claim 1 is characterized in that: In step 2, the preset reference resonant frequency is obtained based on a spectrum analysis of a compressor vibration signal when the refrigerator is operating in a no-load steady state.

4. The refrigerator multi-parameter collaborative sensing and resonance intelligent wake-up control method according to claim 1 is characterized in that: In step 3, the offset threshold Δf threshold The value range is 2Hz to 5Hz.

5. The refrigerator multi-parameter collaborative sensing and resonance intelligent wake-up control method according to claim 1 is characterized in that: In step 4, the preset time interval is 10 seconds to 30 seconds, and the preset amplitude is 5% RH to 15% RH.

6. The refrigerator multi-parameter collaborative sensing and resonance intelligent wake-up control method according to claim 1 is characterized in that: In step 5, the control parameters of the refrigerator are adaptively optimized and adjusted in combination with the geographical location information of the refrigerator, specifically including: when the refrigerator is located in a high altitude area, adjusting the mapping relationship between temperature and compressor starting frequency in the multi-parameter coupling mapping model.

7. The refrigerator multi-parameter collaborative sensing and resonance intelligent wake-up control method according to claim 1 is characterized in that: After step 2, the step of performing a credibility assessment on the resonant frequency offset Δf is also included, specifically comprising: comparing the currently acquired resonant frequency offset Δf with the fluctuation range of the resonant frequency offset under the same load state in the historical operation database; if the fluctuation range is exceeded, reducing the weight of the resonant frequency offset Δf in the intelligent wake-up decision.

8. The refrigerator multi-parameter collaborative sensing and resonance intelligent wake-up control method according to claim 7 is characterized in that: When the credibility of the resonant frequency offset Δf is lower than a preset credibility threshold, the intelligent wake-up decision step switches to a backup strategy that relies only on the temperature signal and the door status signal for wake-up determination.

9. The refrigerator multi-parameter collaborative sensing and resonance intelligent wake-up control method according to claim 1 is characterized in that: It also includes a collaborative control step for refrigerator groups based on geographic location information, specifically including: forming a group of multiple refrigerators within a preset geographic range, sharing each other's operating status data and environmental perception data among the refrigerators in the group, and collaboratively adjusting their respective wake-up thresholds and refrigeration time prediction parameters according to the overall change trend of the regional environment.

10. The refrigerator multi-parameter collaborative sensing and resonance intelligent wake-up control method according to claim 7, characterized in that: When the credibility of the resonant frequency offset Δf is continuously lower than a preset credibility threshold for a preset number of times, a self-check program of the refrigerator is triggered, and an alarm message of sensor abnormality is issued.

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