Refrigerator multi-parameter collaborative awareness and resonance intelligent awakening control method

Abstract

The invention relates to the field of intelligent control, and discloses a refrigerator multi-parameter collaborative awareness and resonance intelligent wakeup control method, which comprises the following steps: dynamically acquiring temperature, humidity and door state signals and adjusting the weights of the signals, extracting the resonant frequency offset by combining the vibration spectrum of a compressor, and performing intelligent wakeup and refrigeration duration prediction based on the characteristics. And credibility evaluation, a self-learning mapping model and an interference filtering mechanism are introduced. Compared with a traditional single-parameter or fixed threshold value control mode, the control logic is driven through physical quantity changes, under triggering of a door state event, a three-dimensional linkage relation of an environment state, a physical signal and a control decision is formed, timely sensing and accurate response to changes of the internal thermal characteristics of the refrigerator are achieved, and the problems of data splitting and lag control are solved.

Description

Technical Field

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

[0002] As a refrigeration device widely used in the fields of home, commerce and industry, a refrigerator is mainly used to store items such as food and medicine that require a low-temperature environment. The control systems of traditional refrigerators mostly rely on sensors such as temperature, humidity, and door status to collect data separately and perform simple feedback control. However, with the changes in usage requirements and the complexity of environmental conditions, these traditional control methods have certain limitations in improving the energy efficiency, stability and intelligence level of refrigerators.

[0003] In the prior art, the refrigerator control system usually uses a fixed threshold or a single sensing signal to trigger the refrigeration process. For example, in the scheme of triggering refrigeration based on a temperature threshold, when the refrigerator door is opened, the temperature changes rapidly, often resulting in a lag response of the control system to the temperature change, making the refrigeration start timing inaccurate and causing unnecessary energy waste. In addition, the existing control methods for refrigerators fail to effectively consider the complex interaction between door state changes, humidity fluctuations and the refrigeration process, which leads to the lag of control instructions and the insufficiency of energy efficiency optimization. Specifically, the existing schemes have the following problems:

[0004] 1. Problem of split sensing of multi-source parameters: The current refrigerator control system mostly relies on independent sensors to monitor parameters such as temperature, humidity and door status respectively. This method fails to effectively analyze the dynamic coupling relationship between various parameters. Especially at the moment when the door is opened, the interaction between the sudden increase in humidity and the temperature fluctuation cannot be captured and analyzed in real time, resulting in a slow response of the control system and a decrease in energy efficiency.

[0005] 2. Energy efficiency contradiction of the wake-up mechanism: Traditional refrigerator control schemes often use a fixed temperature threshold (such as starting refrigeration when the temperature exceeds 5°C), but this method cannot dynamically adjust the wake-up strategy according to the actual thermal characteristics (such as heat capacity, moisture content, etc.) of the materials inside the refrigerator. This not only leads to frequent start and stop, but also may cause over-refrigeration, further wasting energy.

[0006] 3. Insufficient handling of external environmental interference: The prior art usually only relies on the feedback of temperature sensors, lacking an 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 is prone to cause instability or misjudgment of the control system.

[0007] To address these issues, the industry typically reduces errors by improving sensor accuracy, introducing filtering algorithms, etc. However, these improvement methods still have drawbacks: improving sensor accuracy usually leads to an increase in cost, and when dealing with signal interference, filtering algorithms often cannot fully adapt to complex working condition changes, and there is still a risk of false wake-up and unstable control. Summary of the Invention

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

[0009] To achieve the above object, a multi-parameter collaborative perception and resonant intelligent wake-up control method for a refrigerator provided by the present invention includes the following steps:

[0010] Step 1, perform multi-parameter collaborative perception, including real-time obtaining of temperature signals, humidity signals, and door status signals inside the refrigerator, and dynamically allocating weights of the temperature signals, humidity signals, and door status signals in subsequent control decisions according to the current operating state of the refrigerator. Among them, when it is detected that the refrigerator door is opened, the weight W H of the humidity signal is adjusted according to the following formula with the door opening duration t:

[0011] W H (t) = W H_initial ×e -λt ,

[0012] where W H_initial is the initial humidity weight when the door is opened, and λ is a preset humidity weight decay coefficient;

[0013] Step 2, perform resonant feature extraction based on vibration signals, including real-time collection of vibration signals during the operation of the refrigerator compressor, and obtaining the resonant frequency offset Δf characterizing the change in the thermal characteristics of the materials inside the refrigerator through spectral analysis of the vibration signals. The resonant frequency offset Δf is obtained by comparing the currently detected compressor resonant frequency with a preset reference resonant frequency;

[0014] Step 3, perform 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 a preset offset threshold Δf threshold , triggering the refrigerator to enter the refrigeration operation state, and predicting the required refrigeration duration based on the statistical relationship between the resonant frequency offset and the refrigeration duration under different load states in the historical operation data;

[0015] Step 4: Conduct environmental interference identification and filtering, including analyzing the time 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 freezer is caused by actual load changes or external environmental factor interference. Among them, when it is detected that the external environmental humidity increases by more than a preset amplitude within a preset time interval while the freezer door remains closed, it is determined as external environmental factor interference, and the parameter weight is adjusted or the refrigeration operation is delayed.

[0016] Step 5: Construct and dynamically update a multi-parameter coupling mapping model. The model records the correlation data between the temperature, humidity, and compressor resonance frequency during the operation of the freezer at different geographical locations and is updated based on the real-time collected data. It is used to adjust the operation mode and control parameters of the freezer according to the current geographical location and sensed parameters during the subsequent control process.

[0017] Preferably, in step 3, the predicted duration T for this refrigeration cool is calculated according to the following formula: T cool = k × |Δf|, where k is a load-related proportional coefficient determined according to historical operation data, and |Δf| is the absolute value of the resonance frequency offset.

[0018] Preferably, in step 2, the preset reference resonance frequency is obtained through spectral analysis of the compressor vibration signal during the no-load steady-state operation of the freezer.

[0019] Preferably, in step 3, the offset threshold Δf threshold has a value range of 2 Hz to 5 Hz.

[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 freezer are adaptively optimized and adjusted in combination with the geographical location information of the freezer. Specifically, when the freezer is located in a high-altitude area, the mapping relationship between temperature and the compressor startup frequency in the multi-parameter coupling mapping model is adjusted.

[0022] Preferably, after step 2, there is also a step of evaluating the credibility of the resonance frequency offset Δf, specifically including: comparing the currently obtained resonance frequency offset Δf with the fluctuation range of the resonance frequency offset under the same load state in the historical operation database. If it exceeds the fluctuation range, the weight of this resonance frequency offset Δf in the intelligent wake-up decision is reduced.

[0023] Preferably, when the confidence level of the resonance frequency offset Δf is lower than a preset confidence threshold, the intelligent wake-up decision-making 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 further includes a collaborative control step for the freezer group based on geographical location information, specifically including: forming a group of multiple freezers within a preset geographical range, and the freezers within the group share their respective operating status data and environmental perception data, and collaboratively adjust their respective wake-up thresholds and refrigeration duration prediction parameters according to the overall change trend of the regional environment.

[0025] Preferably, when the confidence level of the resonance frequency offset Δf continuously remains lower than the preset confidence threshold for a preset number of times, a self-check program of the freezer is triggered, and an alarm message of sensor abnormality is sent.

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

[0027] 1. Through the dynamic detection of the resonance frequency offset Δf and the weight distribution mechanism triggered by the door status, the lag of traditional temperature threshold control is broken through, the real-time perception of the thermal characteristics of the materials inside the freezer and the energy efficiency optimization are realized, and the compressor vibration signal is correlated with the ice crystal generation rate. The physical meaning of Δf is extracted through spectrum analysis, which directly reflects the material phase change process, avoiding the error accumulation of traditional solutions relying on temperature gradient calculation, and the real-time adjustment of the temperature triggered by the door status event and the vibration signal acquisition frequency. The humidity mutation interference is suppressed through the exponential decay function, forming a three-dimensional collaborative logic of physical signal - environmental state - control weight, and solving the problem of data fragmentation of traditional multi-sensors.

[0028] 2. The resonance frequency confidence coefficient and the historical data self-comparison mechanism are introduced to construct an anti-interference self-healing system, significantly improving the robustness under complex working conditions. For example, when the deviation of Δf exceeds the historical reference threshold, the decision weight of low-confidence data is dynamically reduced, combined with the switching of the backup wake-up strategy, to solve the false triggering problem caused by the superposition of sensor drift and environmental noise, and the online compensation of sensor performance degradation is realized through the sweep frequency excitation signal to detect the cavity natural frequency response.

[0029] 3. The data sharing and temperature - resonance frequency correlation matrix of adjacent freezers based on geographical location are adopted to realize the global optimization and dynamic expansion of the refrigeration strategy. For example, when it is detected that an adjacent freezer has a high-frequency door opening, the refrigeration threshold is adjusted in advance to avoid the power grid impact caused by multiple devices running at high load simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is the architecture flowchart of the multi-parameter collaborative perception and resonance intelligent wake-up control method for the freezer of the present invention.

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

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

[0033] The implementation, functional features, and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with embodiments. Detailed implementation manners

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

[0035] The embodiments of the present application provide a multi-parameter collaborative perception and resonant intelligent wake-up control method for a refrigerator, including the following steps:

[0036] Step 1, perform multi-parameter collaborative perception, including real-time acquisition of the temperature signal, humidity signal, and door status signal inside the refrigerator, and dynamically allocate the weights of the temperature signal, humidity signal, and door status signal in subsequent control decisions according to the current operating state of the refrigerator. Among them, when it is detected that the refrigerator door is opened, the weight W H of the humidity signal is adjusted according to the following formula as the door opening duration t:

[0037] W H (t) = W H_initial × e -λt ,

[0038] where W H_initial is the initial humidity weight when the door is opened, and λ is a preset humidity weight decay coefficient;

[0039] Step 2, perform resonant feature extraction based on vibration signals, including real-time collection of the vibration signal when the refrigerator compressor is running, and obtaining the resonant frequency offset Δf characterizing the change in the thermal characteristics of the materials inside the refrigerator through spectral analysis of the vibration signal. The resonant frequency offset Δf is obtained by comparing the currently detected compressor resonant frequency with a preset reference resonant frequency;

[0040] Step 3, perform 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 a preset offset threshold Δf threshold , trigger the refrigerator to enter the refrigeration operation state, and predict the duration required for this refrigeration based on the statistical relationship between the resonant frequency offset and the refrigeration duration under different load states in the historical operation data;

[0041] Step 4, perform environmental interference identification and filtering, including analyzing the time 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 freezer is caused by actual load changes or external environmental factor interference. Among them, when it is detected that the external environmental humidity increases by more than a preset amplitude within a preset time interval with the freezer door kept closed, it is determined as external environmental factor interference, and the parameter weight is adjusted or the refrigeration operation is delayed.

[0042] Step 5, construct and dynamically update a multi-parameter coupling mapping model. The model records the correlation data between the temperature, humidity, and compressor resonance frequency during the operation of the freezer at different geographical locations and is updated based on the real-time collected data. It is used in the subsequent control process to adjust the operation mode and control parameters of the freezer according to the current geographical location and sensed parameters.

[0043] Preferably, in the said Step 3, the predicted duration T for this refrigeration cool is calculated according to the following formula: T cool = k × |Δf|, where k is a load-related proportionality coefficient determined according to historical operation data, and |Δf| is the absolute value of the resonance frequency offset.

[0044] Preferably, in the said Step 2, the preset reference resonance frequency is obtained through spectral analysis of the compressor vibration signal during the no-load steady-state operation of the freezer.

[0045] Preferably, in the said Step 3, the offset threshold Δf threshold has a value range of 2 Hz to 5 Hz.

[0046] Preferably, in the said 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 the said Step 5, the control parameters of the freezer are adaptively optimized and adjusted in combination with the geographical location information where the freezer is located. Specifically, when the freezer is located in a high-altitude area, the mapping relationship between temperature and compressor start frequency in the multi-parameter coupling mapping model is adjusted.

[0048] Preferably, after the said Step 2, there is also a step of evaluating the credibility of the resonance frequency offset Δf, specifically including: comparing the currently obtained resonance frequency offset Δf with the fluctuation range of the resonance frequency offset under the same load state in the historical operation database. If it exceeds the fluctuation range, the weight of this resonance frequency offset Δf in the intelligent wake-up decision is reduced.

[0049] Preferably, when the confidence level of the resonance frequency offset Δf is lower than a preset confidence threshold, the intelligent wake-up decision-making 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 further includes a collaborative control step for a group of refrigerators based on geographical 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 among the refrigerators in the group, and collaboratively adjusting their respective wake-up thresholds and refrigeration duration prediction parameters according to the overall change trend of the regional environment.

[0051] Preferably, when the confidence level of the resonance frequency offset Δf continuously remains lower than the preset confidence threshold for a preset number of times, a self-check program of the refrigerator is triggered, and an alarm message indicating a sensor abnormality is sent.

[0052] Embodiment 1: This embodiment details the specific steps for realizing intelligent wake-up control of a refrigerator by optimizing the vibration spectrum analysis and data processing of environmental sensors.

[0053] Step 1: Multi-parameter collaborative perception. First, the system continuously collects the temperature signal, humidity signal, and door status signal inside the refrigerator. After obtaining these signals, the system dynamically adjusts the weights of each signal in subsequent control decisions according to the current operating status of the refrigerator. In particular, when the refrigerator door is opened, the weight of the humidity signal will be adjusted according to the door opening duration. This adjustment process is carried out through the following formula:

[0054] W H (t) = W Hinitial ×e -λt ,

[0055] where, W H (t) is the weight of the humidity signal at the moment when the door is opened, W Hinitial is the initial humidity weight when the door is opened, λ is a preset humidity decay coefficient, and t is the door opening duration (seconds). The core function of this formula is to dynamically adjust the influence of humidity on refrigeration startup, thereby avoiding control lag caused by sudden changes in humidity and ensuring the 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 cold cabinet control is not too large to avoid unnecessary energy consumption. Specifically, the humidity decay coefficient λ is a parameter used to adjust the weight of the humidity signal as the door opening duration changes. Its purpose is to ensure that the impact of humidity change on the refrigeration start timing is not too large, thus avoiding unnecessary energy waste. To accurately obtain the value of the humidity decay coefficient λ, the following experimental process is adopted. Experimental data collection: Through the humidity change tests after multiple cold cabinet doors are opened, the impact of humidity signals on refrigeration start under different decay coefficients is monitored in real time. The experiment controls the opening time of the cold cabinet door, collects humidity change data, and records the response time of the refrigeration start timing; Data analysis: Through the comparative analysis of experimental data, different values of the decay coefficient λ are selected to observe the impact of humidity change on the refrigeration start timing. To avoid control lag caused by sudden humidity changes, the value of the decay coefficient λ needs to be between 0.01 and 0.1, and the specific value is determined through experiments; Selection of humidity decay coefficient: Experiments show that the value of the decay coefficient λ should be determined according to the rate of humidity change after the door is opened. A lower decay coefficient is suitable for situations with slow humidity changes, while a higher decay coefficient is suitable for environments with fast humidity changes. In practical applications, the decay coefficient is usually between 0.01 and 0.1 and needs to be adjusted according to actual environmental conditions (such as the rate of air humidity change, cold cabinet type, etc.); Dynamic adjustment: To adapt to humidity changes under different working conditions, the decay coefficient λ can be dynamically adjusted according to the real-time monitored humidity change situation. For example, in an environment with fast humidity changes, the decay coefficient λ can be appropriately increased to ensure that the impact of sudden humidity changes on the control system is effectively suppressed, which all belong to the extended implementation methods known to those of ordinary skill in the art.

[0057] Step 2: Resonant feature extraction based on vibration signals. The cold cabinet compressor generates certain vibration signals during operation. By performing spectral analysis on these vibration signals, the offset Δf of the resonant frequency can be extracted, and this frequency offset is an important indicator of the thermal characteristics change of the materials inside the cold cabinet. Specifically, the offset Δf is obtained by comparing the currently detected compressor vibration frequency with a pre-set reference resonant frequency.

[0058] To further improve the accuracy of data processing, a filtering mechanism is adopted to avoid the influence of external environmental interference. Whenever it is detected that the cold cabinet door remains closed, if the external humidity change exceeds the preset threshold, it will be determined as environmental interference and the weight of the vibration signal will be adjusted accordingly.

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

[0060] T c = k × |Δf|,

[0061] where T c is the predicted refrigeration duration, k is the proportionality coefficient, and |Δf| is the absolute value of the current frequency offset. The proportionality coefficient k is obtained through learning from historical data and reflects the influence of the frequency offset on the refrigeration duration under different load states. It should be emphasized that the value of the 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 a fixed value. The reasonable setting of these values is the key to ensuring that the refrigeration strategy can adapt to different working conditions. Specifically, the proportionality coefficient k is used for predicting the refrigeration duration, and its value is closely related to the load state of the refrigerator and the relationship between the frequency offset and the refrigeration duration in the historical operation data. Specifically, the proportionality coefficient k is obtained through the following experimental process, such as experimental data collection: First, for different load conditions (such as no load, light load, heavy load), run multiple refrigerators in a standard environment and record the operation data of each refrigerator, including the vibration frequency offset Δf and the corresponding refrigeration duration T c . Data processing: Through statistical analysis methods (such as regression analysis, least squares method), calculate the relationship between the frequency offset and the refrigeration duration under each load state, and fit the value of the proportionality coefficient k according to the experimental data. Applicable range of the proportionality coefficient: According to the experimental results, the value range of the proportionality 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 the environmental conditions. For example, under a heavier load, the value of the proportionality coefficient k may be higher because the refrigeration duration is more affected by the load change; while in the case of a light load, the value of the proportionality coefficient is lower. At the same time, in order to adapt to different environmental conditions and load states, the proportionality coefficient k can be dynamically adjusted according to the real-time operation data to ensure the accuracy of the refrigeration duration prediction. For example, if the load state of the refrigerator changes, the system will update the proportionality coefficient in real time to maintain the prediction accuracy, which all belong to the extended implementation methods known to those of ordinary skill in the art.

[0062] Step 4: Environmental interference identification and filtering. During the operation of the freezer, external environmental factors such as humidity changes may interfere with the internal sensor data, thus affecting the control decision-making. To solve this problem, 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 internal parameter fluctuation of the freezer is caused by load changes or external interference. When the external humidity change exceeds the preset amplitude while the freezer door remains closed, the system will determine it as an external environmental factor interference and adjust the control strategy.

[0063] Step 5: Construction and update of the multi-parameter coupling mapping model. To achieve the adaptive control of the freezer in different environments, this embodiment further constructs and dynamically updates a multi-parameter coupling mapping model. This model establishes the correlation between them by recording the temperature, humidity, and resonance frequency data during the operation of the freezer at different geographical locations. Through the real-time collected data, the model will update the operation mode and control parameters of the freezer. When the freezer is located at different geographical locations, the model will adjust the mapping relationship between the temperature and the compressor startup frequency according to the geographical location information to ensure the optimal energy efficiency of the freezer in different environments.

[0064] When the credibility of the resonance frequency offset Δf is low, the system will compare and evaluate the historical data with the currently obtained data. If it exceeds the fluctuation range, the weight of this data in the intelligent wake-up decision-making will be reduced. If the credibility is lower than the preset threshold, the freezer will switch to a backup strategy that only depends on the temperature and the door status signal to ensure the stability and reliability of the control, which all belong to the extended implementation methods known to those of ordinary skill in the art.

[0065] Example 2: As Figure 1As shown in the figure, the system architecture of the multi-parameter collaborative perception and resonant intelligent wake-up control method for the refrigerator of the present invention mainly consists of a data acquisition layer, a data processing layer, a decision execution layer, and a geographical adaptation module. In the data acquisition layer, various parameters during the operation of the refrigerator are collected in real time through a temperature sensor, a humidity sensor, a vibration sensor, and a door status switch, 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 dynamic weight allocator adjusts the weights of each parameter according to the current state of the refrigerator. The vibration signal also needs to pass through the spectrum analysis engine to extract the resonant frequency offset Δf, and the credibility evaluation module evaluates the credibility of this offset. The geographical adaptation module uses the GPS positioning module to obtain geographical location information and adjusts the compressor frequency in the high-altitude mode in combination with the multi-parameter mapping library. In the decision execution layer, according to the result of the threshold judgment, if the condition is met, the refrigeration duration 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 carried out, thereby affecting the parameter settings of the dynamic weight allocator. As Figure 2 shown, after the intelligent wake-up decision-making process based on the vibration signal starts, the vibration signal is detected, and then the resonant frequency offset is obtained. The system then determines whether the offset is greater than the threshold? If the judgment result is yes, refrigeration is triggered, and then the process ends. If the judgment result is no, the system enters the waiting state, and then the process ends. As Figure 3 shown, the block diagram of the refrigerator control system shows the main components of the refrigerator control system, including the temperature and humidity sensor, the door status sensor, and the vibration signal sensor as the input ends. These sensors transmit the collected information to the refrigerator controller. The refrigerator controller makes an intelligent wake-up decision based on these input information and finally drives the refrigeration system to work.

[0066] Embodiment 3: In this embodiment, the temperature, humidity, and door status signals inside the refrigerator are collected in real time first to ensure that the acquisition and dynamic adjustment of these signals can accurately reflect the current operating state of the refrigerator. By setting the dynamic weight allocation rule, the weights of different perception signals can be adjusted in real time effectively according to the actual working 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 through the following formula:

[0068] W H (t) = W H,initial ·e -λt ,

[0069] where: W H (t) is the weight of the humidity signal at the moment when the door is opened; 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 duration (seconds), and W H (t) represents the humidity signal weight, reflecting the influence of humidity on refrigeration control; W H,initial is the initial humidity weight, used to represent the immediate influence of humidity on refrigeration at the moment when the cold cabinet door is opened; λ is the attenuation coefficient, and its value range is usually between 0.01 and 0.1, determined according to experimental results to adapt to different humidity change rates. In this way, the influence of humidity on refrigeration startup gradually weakens, thus avoiding unnecessary influence on the control system caused by humidity mutation.

[0070] During the operation of the cold cabinet, by performing spectral analysis on the vibration signal of the compressor, the resonant frequency offset Δf is extracted, which is used as an important indicator reflecting the change of the thermal characteristics of the materials inside the cold cabinet. The resonant frequency offset Δf is calculated by comparing the currently detected compressor vibration frequency with the preset reference resonant frequency. The formula is as follows:

[0071] Δf = f current - f baseline ,

[0072] where: f current is the currently detected compressor vibration frequency; f baseline is the reference resonant frequency when the cold cabinet is operating stably without load. Through this formula, the actual value of the change of the thermal characteristics of the materials inside the cold cabinet can be obtained. To improve the accuracy, it is also necessary to evaluate the credibility of the resonant frequency offset to ensure the accuracy of the data.

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

[0074] T c = k·|Δf|,

[0075] where: T c is the predicted refrigeration duration; k is the proportionality coefficient related to the load; |Δf| is the absolute value of the resonant frequency offset. In the formula, k is a coefficient obtained through statistical methods based on historical data, reflecting the influence of the frequency offset on the refrigeration duration under different load states. The determination of this proportionality coefficient k is obtained by analyzing the relationship between the resonant frequency offset and the actual refrigeration duration under different loads, and its value range is usually 0.1 to 1, depending on the specific cold cabinet type and operating conditions.

[0076] To effectively filter out external environmental interference, in this embodiment, the humidity change is monitored and the external interference is analyzed in combination with the door status signal. Specifically, when the refrigerator door is in the closed state, if the external humidity exceeds the preset amplitude range within the preset time interval, it is regarded as external environmental factor interference. At this time, it is necessary to adjust the control strategy and postpone or adjust the refrigeration trigger timing. For the judgment of the humidity change amplitude, the preset amplitude range is 5%RH to 15%RH to cope with most normal environmental changes.

[0077] By recording the operation data of the refrigerator at different geographical locations, this embodiment constructs and dynamically updates a multi-parameter coupling mapping model. This model can optimize the operation mode and control parameters of the refrigerator according to geographical location and the temperature, humidity, and compressor resonance frequency data collected in real time. For example, in high-altitude areas, the mapping relationship between the compressor startup frequency and temperature of the refrigerator may change, so it is necessary to adjust the control strategy in the mapping model according to the geographical information. In this way, the refrigerator can automatically adjust the working parameters according to different environmental conditions to ensure the best energy efficiency and refrigeration effect, which all belong to the extended implementation methods that those of ordinary skill in the art can know.

[0078] Embodiment 4: When the refrigerator door is opened, 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] where W H (t) is the humidity signal weight at the moment when the door is opened, W Hinitial is the initial humidity weight when the door is opened, λ is the preset humidity weight decay coefficient, and t is the door opening duration (in seconds). The influence of humidity on refrigeration startup is dynamically adjusted through this formula, so as to avoid control lag caused by sudden humidity changes and ensure the real-time response of the system. The value range of λ is 0.01 to 0.1, and this range is obtained through actual experiments. Specifically, in the experiment, by simulating the humidity change process after the refrigerator door is opened, different decay coefficients λ are set respectively, and the influence of humidity change on the refrigeration startup timing is measured. Through the analysis of the experimental data, it is found that when the value of λ is between 0.01 and 0.1, it can avoid the interference of humidity fluctuation on the control system on the premise of ensuring that the influence of humidity on the refrigerator startup timing is not significant and the response is rapid. Therefore, this range is selected as the applicable range of the decay coefficient to ensure the stable operation of the system in actual application. The decay coefficient λ is used to control the influence of humidity on the startup of the refrigeration system to ensure that during the door opening process, the change of humidity will not have too much influence on the startup timing of the refrigerator.

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

[0082] In the prediction formula for the refrigeration duration T c = k×|Δf|, T c is the predicted refrigeration duration, k is a proportionality coefficient related to the load, and |Δf| is the absolute value of the resonance frequency offset. To ensure the applicability of this formula, this embodiment further clarifies the setting process of the proportionality 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 refrigeration duration in the historical operation data. In this embodiment, the value of the proportionality coefficient k is obtained through 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 refrigeration durations were compared. Through the regression analysis method, the statistical relationship between the resonance frequency offset Δf and the refrigeration duration T c was determined under different load conditions, so as to obtain the proportionality coefficient k applicable to a specific refrigerator type and load state. This proportionality coefficient k will be dynamically adjusted according to the actual operation data of the refrigerator to improve the accuracy of the refrigeration duration prediction and ensure the stable operation of the system under different loads and environmental conditions. And to ensure that this proportionality coefficient can flexibly adapt to different refrigerator 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 refrigerator 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 duration 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 control decisions. To address this issue, this embodiment adds a more refined interference recognition and filtering mechanism. The specific operation process is as follows: First, monitor the opening and closing state of the refrigerator door. When the door remains closed, the system analyzes the change 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 determines it as an external interference. For the identified interference, the system automatically adjusts the weight of the humidity signal or delays the triggering of the refrigeration operation, thus avoiding false triggering caused by external environmental fluctuations. This improvement further enhances the stability of the refrigerator system and avoids energy efficiency losses caused by environmental fluctuations.

[0084] To further improve the intelligence level of the refrigerator, this embodiment adds an adaptive optimization function based on geographical location information. By using a GPS module to obtain the geographical location information of the refrigerator in real time and combining with a multi-parameter mapping model, the control strategy of the refrigerator can be optimized and adjusted according to the geographical environment where it is located. For example, when the refrigerator is located in a high-altitude area, the relationship between the starting frequency of the refrigerator compressor and the temperature may change. In this case, the system automatically adjusts the control parameters to make it more adaptable to the special environment of the high-altitude area. The introduction of this function can not only improve the energy efficiency performance of the refrigerator in different environments, but also enhance the adaptive ability of the system, ensuring that the refrigerator can maintain stable operation under various complex working conditions. Its specific implementation steps include data collection: collect relevant data of the internal and external environments of the refrigerator in real time through temperature sensors, humidity sensors, vibration sensors, and door status switches; signal preprocessing: adjust the weights of different signals through a dynamic weight allocator, especially dynamically adjusting the weight of the humidity signal when the door is opened; spectrum analysis: perform spectrum analysis on the vibration signal, extract the resonant frequency offset, and conduct credibility evaluation; intelligent decision-making: according to the evaluation results, if the resonant frequency offset exceeds the preset threshold, start the refrigeration system and predict the refrigeration duration. If the credibility is low, switch to the backup strategy and only rely on the temperature and door status signals; environmental interference handling: monitor the change in external humidity in real time, and when interference is detected, adjust the control strategy to ensure the stable operation of the system; geographical adaptive optimization: automatically adjust the control parameters according to the geographical location of the refrigerator to improve the working efficiency of the refrigerator in different environments, all of which belong to the extended implementation methods known to those of ordinary skill in the art.

[0085] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

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

Claims

1. A multi-parameter collaborative sensing and resonant intelligent wake-up control method for a freezer, characterized in that It includes the following steps: Step 1, perform multi-parameter collaborative perception, including obtaining the temperature signal, humidity signal, and door status signal inside the refrigerator in real time, and dynamically allocating the weights of the temperature signal, humidity signal, and door status signal in subsequent control decisions according to the current operating state of the refrigerator. Among them, when the refrigerator door is detected to be opened, the weight W H of the humidity signal is adjusted according to the following formula with the door opening duration t: W H W(t) = 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 the resonance characteristics based on the vibration signal, including real-time collection of the vibration signal during the operation of the freezer compressor, obtaining the resonance frequency offset Δf characterizing the change in the thermal characteristics of the materials inside the freezer through spectral analysis of the vibration signal, and the resonance frequency offset Δf is obtained by comparing the currently detected compressor resonance frequency with a pre-set reference resonance frequency; Step 3, perform 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 trigger the refrigerator to enter the refrigeration operation state, and predict the duration required for this refrigeration based on the statistical relationship between the resonant frequency offset and the refrigeration duration under different load states in the historical operation data; Step 4: Identify and filter environmental interference, including analyzing the time correlation between the door state signal and the temperature and humidity change data of the external environment, and judging whether the fluctuation of the internal parameters of the freezer is caused by the change of the actual load or the interference of external environmental factors. Among them, when it is detected that the external environmental humidity rises by more than a preset amplitude within a preset time interval with the freezer door kept closed, it is determined as the interference of external environmental factors, and the parameter weight is adjusted or the refrigeration operation is triggered with a delay; Step 5: Construct and dynamically update the multi-parameter coupling mapping model. The model records the correlation data between the temperature, humidity and compressor resonance frequency during the operation of the freezer at different geographical locations and is updated based on the real-time collected data, and is used to adjust the operation mode and control parameters of the freezer according to the current geographical location and sensed parameters during the subsequent control process.

2. The multi-parameter collaborative perception and resonant intelligent wake-up control method for a refrigerator according to claim 1, wherein In the said step 3, predicting the duration T required for this refrigeration cool is calculated according to the following formula: T cool = k × | |Δf|, where k is a load-related proportionality coefficient determined according to historical operation data, and |Δf| is the absolute value of the resonance frequency offset.

3. The multi-parameter collaborative perception and resonant intelligent wake-up control method for a refrigerator according to claim 1, wherein, In Step 2, the pre-set reference resonance frequency is obtained through spectral analysis of the compressor vibration signal when the freezer is operating in an unloaded steady state.

4. The multi-parameter collaborative perception and resonant intelligent wake-up control method for a refrigerator according to claim 1, wherein In step 3, the offset threshold Δf threshold has a value range of 2 Hz to 5 Hz.

5. The multi-parameter collaborative perception and resonant intelligent wake-up control method for a refrigerator according to claim 1, wherein, 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 multi-parameter collaborative perception and resonant intelligent wake-up control method for a refrigerator according to claim 1, characterized in that In Step 5, the control parameters of the freezer are adaptively optimized and adjusted in combination with the geographical location information of the freezer. Specifically, when the freezer is located in a high-altitude area, the mapping relationship between the temperature and the compressor starting frequency in the multi-parameter coupling mapping model is adjusted.

7. The multi-parameter collaborative perception and resonant intelligent wake-up control method for a refrigerator according to claim 1, characterized in that After Step 2, it also includes a step of evaluating the credibility of the resonance frequency offset Δf. Specifically, the currently obtained resonance frequency offset Δf is compared with the fluctuation range of the resonance frequency offset under the same load state in the historical operation database. If it exceeds the fluctuation range, the weight of this resonance frequency offset Δf in the intelligent wake-up decision is reduced.

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

9. The multi-parameter collaborative perception and resonant intelligent wake-up control method for a refrigerator according to claim 1, characterized in that It also includes a step of collaborative control of the freezer group based on geographical location information. Specifically, a plurality of freezers within a preset geographical range are grouped together, and the freezers within the group share their respective operation state data and environmental perception data, and adjust their respective wake-up thresholds and refrigeration duration prediction parameters collaboratively according to the overall change trend of the regional environment.

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

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