A method and system for monitoring the status of a refrigerator
By dynamically adjusting the stability index of the model parameters, a load heat capacity and leakage coefficient model based on the recursive least squares method is constructed, which solves the problem of misjudgment of load and sealing performance in refrigerator condition monitoring and achieves high-precision and robust condition monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- DA PAN ELECTRIC APPLIANCE IND CO LTD
- Filing Date
- 2026-02-10
- Publication Date
- 2026-05-12
AI Technical Summary
Existing refrigerator condition monitoring technologies struggle to accurately distinguish between temperature changes caused by increased load and cold air leakage caused by decreased sealing, resulting in a high rate of misjudgment in monitoring results. Furthermore, fixed-parameter algorithms struggle to balance convergence speed and anti-interference capabilities when faced with complex refrigerator operating conditions.
By introducing a stability index to dynamically adjust the forgetting factor of the load heat capacity model and the weighting coefficient of the heat leakage coefficient model, a load heat capacity and heat leakage coefficient model based on recursive least squares method is constructed, eliminating load heat flow and achieving independent and accurate monitoring of load and sealing performance.
It significantly improves the accuracy and anti-interference capability of refrigerator status monitoring, can suppress data fluctuation interference and quickly track load changes in steady state, and independently decouples load and sealing assessment to reduce the false judgment rate.
Smart Images
Figure CN121720265B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of automatic control technology. More specifically, this invention relates to a method and system for monitoring the status of a refrigerator. Background Technology
[0002] As an essential household appliance, the refrigerator's operating status directly affects the quality of food preservation and the equipment's energy consumption. During actual operation, the amount of food stored inside (load capacity) and the aging of the door seals or whether the door is closed tightly (sealing performance) are two key factors influencing refrigerator performance. Accurately monitoring these two conditions is of significant practical importance for optimizing refrigeration control strategies, reminding users to perform equipment maintenance, and reducing operating energy consumption.
[0003] Existing refrigerator condition monitoring technologies typically rely on simple temperature threshold judgments or static thermodynamic model calculations. In real-world usage, the interior of a refrigerator is a complex dynamic thermodynamic system, with temperature affected by various factors such as compressor start-up and shutdown, defrosting cycles, and user door opening and closing. Traditional static models struggle to distinguish whether temperature changes are caused by increased load or by cold air leakage due to decreased sealing, leading to a high rate of misjudgment. When a large amount of room-temperature food is placed inside, causing a temperature rise, traditional methods are highly likely to misidentify this as a sealing failure.
[0004] When using parameter identification algorithms to estimate refrigerator parameters, the frequent switching between steady-state and transient operating states makes it difficult for algorithms with fixed parameters to simultaneously balance convergence speed and anti-interference capability. If the algorithm reacts too slowly to new data, it cannot capture sudden load changes in time; if it reacts too quickly, it is easily affected by temperature fluctuations under steady-state conditions, leading to oscillations in the estimated values. There is an urgent need for a method that can dynamically adjust model parameters according to the real-time operating status of the refrigerator and effectively decouple the mutual influence between load and sealing performance to solve the problem of inaccurate monitoring in existing technologies. Summary of the Invention
[0005] The purpose of this invention is to propose a method and system for monitoring the status of a refrigerator, in order to solve the problem of insufficient accuracy in monitoring the operating status of refrigerators in the prior art.
[0006] In a first aspect, the present invention provides a method for state monitoring of a refrigerator, comprising: acquiring internal and external temperature data of the refrigerator and cooling capacity data of the refrigeration system; calculating the fluctuation characteristics of the internal temperature data of the refrigerator within a preset time period to obtain a stability index; constructing a load heat capacity model based on recursive least squares method according to the cooling capacity data and the temperature change rate, dynamically adjusting the forgetting factor of the load heat capacity model according to the stability index, and obtaining the estimated load heat capacity value at the current moment using the adjusted load heat capacity model; constructing a heat leakage coefficient model based on recursive least squares method according to the net heat flow and the temperature difference between the inside and outside of the refrigerator, and adjusting the weighting coefficient of the heat leakage coefficient model according to the stability index; calculating the load heat flow using the estimated load heat capacity value combined with the temperature data, subtracting the load heat flow from the cooling capacity data to obtain the net heat flow, and inputting the net heat flow into the adjusted heat leakage coefficient model to obtain the estimated heat leakage coefficient value; determining the load of the refrigerator based on the estimated load heat capacity value, and determining the airtightness of the refrigerator based on the estimated heat leakage coefficient value.
[0007] This invention utilizes a stability index to dynamically adjust the iterative parameters of the load heat capacity model and employs a strategy of removing load heat flow from the cooling capacity to construct a heat leakage model that only reflects the characteristics of the refrigerator body. This concept based on dynamic identification and heat flow decoupling effectively eliminates the interference of load fluctuations on the sealing performance assessment, enabling independent and accurate monitoring of the refrigerator's load and sealing performance, and significantly improving the accuracy of the monitoring results.
[0008] Optionally, the calculation process of the stability index includes: calculating the temperature change rate sequence of the internal temperature data of the refrigerator within the preset time period; calculating the variance of the temperature change rate sequence as a fluctuation feature value; normalizing the fluctuation feature value to obtain the stability index; wherein the stability index is negatively correlated with the fluctuation feature value and is used to characterize the stability of the internal temperature state of the refrigerator.
[0009] By calculating and normalizing the variance of the temperature change rate sequence, a stability index is obtained, which can sensitively capture the subtle fluctuations in the internal temperature of the refrigerator. Variance, as a characteristic value of fluctuation, can accurately quantify the dispersion of data from a statistical perspective, providing a reliable quantitative basis for the dynamic adjustment of subsequent model parameters, namely the forgetting factor and weighting coefficients, thus ensuring the objectivity and accuracy of system state assessment.
[0010] Optionally, the calculation process of the forgetting factor includes: establishing a positive correlation mapping function between the stability index and the forgetting factor; calculating the difference between the stability index at the current time and a preset threshold to obtain a deviation value; substituting the deviation value into the positive correlation mapping function to obtain a correction amount for the current forgetting factor; adding the correction amount to the current forgetting factor to obtain an adjusted forgetting factor; and performing boundary limiting processing on the adjusted forgetting factor: if the adjusted forgetting factor exceeds a preset upper limit value, then assigning it the preset upper limit value; if the adjusted forgetting factor is lower than a preset lower limit value, then assigning it the preset lower limit value.
[0011] A positive correlation between the stability index and the forgetting factor was established, optimizing the algorithm's performance under different operating conditions. When the refrigerator is in a steady state (i.e., the stability index is high), increasing the forgetting factor and utilizing more historical data for smooth calculation improves the algorithm's robustness to interference and the stability of parameter estimation. When the refrigerator is in a transient state (i.e., the stability index is low), decreasing the forgetting factor and assigning greater weight to new data enables the algorithm to quickly capture load changes, resolving the error problem caused by the difficulty in balancing tracking speed and steady-state accuracy in traditional fixed-forgetting-factor algorithms.
[0012] Optionally, the process of constructing the load heat capacity model includes: establishing a parameter estimation equation describing the linear relationship between cooling capacity data and temperature change rate, wherein the load heat capacity is the parameter to be estimated, the temperature change rate is the input variable, and the cooling capacity data is the observed value; solving the parameter estimation equation using a recursive least squares algorithm, and updating the covariance matrix using the adjusted forgetting factor in each iteration, thereby updating the estimated value of the load heat capacity.
[0013] Optionally, the calculation process of the net heat flow includes: calculating the product of the estimated load heat capacity and the current temperature change rate to obtain the load heat flow; acquiring cooling capacity data for constructing a heat leakage coefficient model; and subtracting the load heat flow from the cooling capacity data to obtain the net heat flow.
[0014] By first estimating the load heat capacity, then calculating the heat flow absorbed or released by the load, and subtracting this heat from the total cooling capacity, the net heat flow related only to heat leakage from the cabinet is obtained. This step eliminates the interference of load thermal inertia on the calculation of the heat leakage coefficient, enabling accurate assessment of the refrigerator's sealing performance even during load changes. This avoids misjudging door seal leakage due to increased heat load, further improving the accuracy of sealing performance monitoring.
[0015] Optionally, the process of constructing the heat leakage coefficient model includes: establishing a linear relationship between net heat flow and the temperature difference between the inside and outside of the refrigerator; converting the linear relationship into a parameter estimation equation, wherein the heat leakage coefficient is set as the parameter to be estimated, the net heat flow is set as the observed value, and the temperature difference between the inside and outside of the refrigerator is set as the input variable; solving the parameter estimation equation using a recursive least squares algorithm, wherein in each recursive iteration step, the reciprocal of the weighting coefficient is introduced into the denominator of the gain coefficient calculation formula of the recursive least squares algorithm, thereby updating the estimated value of the heat leakage coefficient.
[0016] A piecewise weighted strategy based on the stability index was adopted for the heat leakage coefficient model. The heat leakage coefficient mainly reflects the thermal insulation performance of the enclosure and is a slowly varying parameter. The heat leakage value calculated when the temperature fluctuates drastically is often unreliable. By assigning high weights to high stability and low weights to low stability, the influence of instantaneous heat flow fluctuations caused by door opening and closing or defrosting on the heat leakage coefficient estimation can be effectively filtered out, thereby improving the robustness and accuracy of sealing monitoring under complex operating conditions.
[0017] Optionally, the weighting coefficient includes: dividing the range of the stability index into two intervals and establishing a positive correspondence between the weighting coefficient and the stability index; when the stability index is in a first preset interval, setting the weighting coefficient to a first preset value; when the stability index is in a second preset interval, setting the weighting coefficient to a second preset value; wherein the value in the first preset interval is greater than the value in the second preset interval, and the first preset value is greater than the second preset value.
[0018] Optionally, determining the refrigerator's load based on the estimated load heat capacity includes: presetting multiple consecutive load heat capacity intervals and assigning a corresponding load level to each interval; determining the load heat capacity interval to which the estimated load heat capacity belongs; and outputting the load level corresponding to the interval as the refrigerator's load determination result.
[0019] Optionally, determining the refrigerator's sealing performance based on the estimated heat leakage coefficient includes: setting a preset sealing performance abnormality threshold; comparing the estimated heat leakage coefficient with the sealing performance abnormality threshold; and generating a refrigerator sealing performance abnormality signal if the estimated heat leakage coefficient is greater than the sealing performance abnormality threshold.
[0020] In a second aspect, a condition monitoring system for a refrigerator includes:
[0021] processor;
[0022] The memory stores computer instructions for a method of monitoring the state of a refrigerator, which, when executed by the processor, cause the system to perform the aforementioned method of monitoring the state of a refrigerator.
[0023] The beneficial effects of this invention are as follows: This invention introduces a stability index to characterize the current operating state of the refrigerator and dynamically adjusts the forgetting factor of the load heat capacity model accordingly. This mechanism enables the model to quickly track load changes when the system fluctuates greatly, and effectively suppresses data fluctuation interference when the system is stable, thereby obtaining an accurate estimate of the load heat capacity. More importantly, this invention calculates the load heat flow and removes it from the total cooling capacity to obtain the net heat flow used to evaluate the sealing performance. This completely eliminates the interference of load changes on the sealing performance evaluation from a physical mechanism perspective, achieving independent and accurate decoupling of load determination and sealing performance determination, and solving the problem of misjudgment caused by the inability to distinguish between the two in existing technologies. Attached Figure Description
[0024] Figure 1 This is a flowchart of a method for monitoring the status of a refrigerator according to an embodiment of the present invention.
[0025] Figure 2 This is a comparison diagram of the effects of a status monitoring method for a refrigerator according to an embodiment of the present invention.
[0026] Figure 3 This is a state change curve diagram of a state monitoring method for a refrigerator according to an embodiment of the present invention.
[0027] Figure 4 This is a structural block diagram of a condition monitoring system for a refrigerator according to an embodiment of the present invention. Detailed Implementation
[0028] The technical solutions of the embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. Figure 1 The diagram shown is a flowchart of a method for monitoring the status of a refrigerator according to an embodiment of the present invention.
[0029] It should be noted that the standard recursive least squares method is an adaptive algorithm based on a linear regression model that recursively estimates unknown parameters by minimizing the weighted sum of squared errors. The core of the standard recursive least squares method lies in correcting the parameter estimates through recursive iteration of the gain vector and covariance matrix. The covariance matrix mathematically represents the dispersion of the estimation error; in this invention, the covariance matrix corresponds to the covariance matrix in the standard recursive least squares method. The forgetting factor in the standard algorithm is used to adjust the weighting of historical data; in the load heat capacity model, the forgetting factor is dynamically adjusted to directly intervene in the update rate of the covariance matrix. The weighting coefficient introduced in the heat leakage coefficient model is equivalent to a measure of the reliability of the observed data. It participates in the gain calculation as a denominator term, essentially adjusting the update magnitude of the covariance matrix. This ensures that the contribution weight of this iteration to the final estimate of the heat leakage coefficient is reduced when temperature fluctuations are severe, thereby achieving decoupling and accurate identification of the two types of physical parameters under different operating conditions.
[0030] S1: Obtain the internal temperature data of the refrigerator and the cooling capacity data of the refrigeration system, and calculate the stability index by analyzing the fluctuation characteristics of the temperature data within a preset time period.
[0031] First, acquire the internal and external temperature data of the refrigerator, as well as the cooling capacity data of the refrigeration system. The temperature data is collected by a temperature sensor installed in the refrigerator compartment or freezer compartment, with a sampling period set to 30 seconds. The cooling capacity data is calculated based on the compressor power, operating time, and refrigerant flow rate, and is expressed in watts.
[0032] After acquiring the raw temperature data, a stability index is obtained by calculating the fluctuation characteristics of the temperature data within a preset time period. The preset time period is set to 10 minutes and includes 20 consecutive temperature sampling points. First, the temperature change rate sequence of the temperature data within the preset time period is calculated. The temperature change rate is calculated by dividing the temperature difference between two adjacent sampling points by the time interval, with the unit being degrees Celsius per second.
[0033] Secondly, the variance of the temperature change rate sequence is calculated as a fluctuation characteristic value. The variance is calculated using standard statistical methods: the sum of the squares of all temperature change rates within the time window is divided by the sample size, and then the square of the mean temperature change rate is subtracted. A larger variance indicates more drastic temperature fluctuations.
[0034] Finally, the fluctuation characteristic values are normalized to obtain the stability index. The normalization process uses a max-min normalization method, mapping the variance value to the range of 0 to 1. The stability index is negatively correlated with the fluctuation characteristic values, and is calculated by subtracting the normalized variance value from 1. A higher stability index value indicates a more stable internal temperature in the refrigerator; a lower value indicates more drastic temperature fluctuations, suggesting the refrigerator may be under transient conditions such as load changes or door opening / closing.
[0035] S2: Construct a load heat capacity model based on the least squares method, dynamically adjust the forgetting factor according to the stability index, and iteratively update the estimated load heat capacity value.
[0036] In this embodiment, a load heat capacity model based on the least squares method is constructed to accurately estimate the heat capacity of the internal load of the refrigerator. The load heat capacity reflects the ability of the items stored in the refrigerator to absorb or release heat; a larger value indicates a greater load.
[0037] First, a parameter estimation equation is established to describe the linear relationship between cooling capacity data and the rate of temperature change. According to the first law of thermodynamics, the energy balance inside the refrigerator can be expressed as: cooling capacity equals the product of load heat capacity and the rate of temperature change, plus heat leakage. In this step, the load heat capacity is set as the parameter to be estimated, the rate of temperature change is set as the input variable, and the cooling capacity data is set as the observed value.
[0038] Secondly, the parameter estimation equation is solved using the recursive least squares algorithm. The recursive least squares algorithm is an online parameter identification method that continuously updates parameter estimates as new data arrives. In each iteration, the covariance matrix is updated using an adjusted forgetting factor, thereby updating the estimated load heat capacity. The specific equation of the load heat capacity model is as follows:
[0039] ;
[0040] Where C(k) is the estimated load heat capacity at the k-th iteration, in joules per degree Celsius; C(k 1) is the estimated load heat capacity from the previous iteration, in joules per degree Celsius; ( Let be the gain coefficient of the k-th iteration, dimensionless; Q(k) be the cooling capacity data at time k, in watts; and r(k) be the absolute value of the temperature change rate at time k, in degrees Celsius per second. This formula continuously corrects the estimated load heat capacity recursively. The gain coefficient K(k) is determined by the covariance matrix and the forgetting factor, as shown in the formula:
[0041] ;
[0042] Where K(k) represents the gain coefficient at the k-th iteration; This represents the covariance matrix updated in the previous time step; This represents the input variable vector at time k, which corresponds to the rate of temperature change in this embodiment; This represents the transpose of the input variable vector; The forgetting factor at time k is represented by the value of the forgetting factor. Iterative correction and boundary limiting are applied by calculating the deviation between the current stability index and a preset threshold, causing the forgetting factor to decrease as the stability index decreases and increase as the stability index increases. The forgetting factor is dynamically adjusted according to the stability index, enabling the model to perform smooth estimation in steady state and rapid tracking in transient states.
[0043] The dynamic adjustment of the forgetting factor is a key technical feature of this invention. First, a range of values for the forgetting factor is defined, defined between a preset upper limit and a preset lower limit. In this embodiment, the preset upper limit is set to 0.99, and the preset lower limit is set to 0.90. Second, a positive correlation mapping function between the stability index and the forgetting factor is established. The mapping function uses a linear relationship; when the stability index is 1, the forgetting factor takes the upper limit value, and when the stability index is 0, the forgetting factor takes the lower limit value.
[0044] When the stability index is higher than a preset threshold, the value of the forgetting factor is increased through the mapping function, up to a preset upper limit of the range. In this embodiment, the preset threshold is set to 0.7. A high stability index indicates that the refrigerator is operating in a steady state. In this case, increasing the forgetting factor allows the algorithm to utilize historical data more for smooth calculations, improving the stability and anti-interference ability of parameter estimation.
[0045] When the stability index falls below a preset threshold, the forgetting factor is reduced using the mapping function, down to a preset lower limit within its range. A low stability index indicates the refrigerator is in a transient state, possibly due to the user recently placing a large amount of room-temperature food inside or frequent door opening and closing. In this case, reducing the forgetting factor and assigning greater weight to new data allows the algorithm to quickly track load fluctuations and update the estimated load heat capacity in a timely manner.
[0046] Through the aforementioned dynamic adjustment mechanism, the load heat capacity model can adaptively balance tracking speed and estimation accuracy under different operating conditions, thus overcoming the limitations of traditional fixed parameter algorithms when facing the complex operating conditions of refrigerators.
[0047] S3: Construct a heat leakage coefficient model based on the least squares method, calculate the load heat flow using the load heat capacity estimate and subtract it from the cooling capacity data to obtain the net heat flow, determine the weighting coefficient based on the stability index and update the heat leakage coefficient estimate.
[0048] After obtaining the estimated load heat capacity, the load heat flow is calculated to achieve heat flow decoupling. The calculation process of the load heat flow includes: calculating the product of the estimated load heat capacity and the current rate of temperature change to obtain the load heat flow.
[0049] Next, the cooling capacity data used to construct the heat leakage coefficient model is obtained. The load heat flow is subtracted from the cooling capacity data to obtain the net heat flow. The net heat flow eliminates the influence of load thermal inertia and only reflects the heat leaked through the cabinet insulation layer and door seal, providing a reliable basis for accurately assessing the refrigerator's sealing performance.
[0050] Then, a heat leakage coefficient model based on the least squares method is constructed. First, a linear relationship between net heat flow and the temperature difference between the inside and outside of the refrigerator is established. According to the principles of heat transfer, the heat leakage through the refrigerator is proportional to the temperature difference, and the proportionality coefficient is the heat leakage coefficient. Second, the linear relationship is converted into a parameter estimation equation, where the heat leakage coefficient is set as the parameter to be estimated, the net heat flow is set as the observed value, and the temperature difference between the inside and outside of the refrigerator is set as the input variable. The temperature difference between the inside and outside of the refrigerator is calculated by the difference between the readings of the internal temperature sensor and the ambient temperature sensor.
[0051] The parameter estimation equation is solved using a recursive least squares algorithm. In each recursive iteration, the update weights of the covariance matrix in the algorithm are adjusted using weighted coefficients to update the estimated heat leakage coefficient. The weighted coefficients are determined based on the stability index and are set in a segmented manner.
[0052] The equation for the heat leakage coefficient model is:
[0053] ;
[0054] Where U(k) is the estimated heat leakage coefficient at the k-th iteration, in watts per degree Celsius; U(k 1) is the estimated heat leakage coefficient from the previous iteration, in watts per degree Celsius; G(k) is the weighted gain coefficient for the k-th iteration, dimensionless; A(k) is the net heat flow at time k, in watts; ΔT(k) is the temperature difference between the inside and outside of the refrigerator at time k, in degrees Celsius. The net heat flow in this formula is obtained by subtracting the load heat flow from the cooling capacity, thus decoupling the heat flow from the load and the sealing performance. The weighted gain coefficient G(k) is adjusted according to the weighting coefficient determined by the stability index, as shown in the formula:
[0055] ;
[0056] in Let the covariance matrix be the updated value from the previous time step. This represents the weighting coefficient determined based on the stability index. The weighting gain coefficient is assigned a high weight when the temperature is stable and a low weight when the temperature fluctuates, ensuring the accuracy and robustness of the heat leakage coefficient estimation.
[0057] Specifically, the stability index is divided into two ranges, and a positive correlation is established between the weighting coefficient and the stability index. When the stability index is within a first preset range, the weighting coefficient is set to a first preset value. In this embodiment, the first preset range is set to 0.7 to 1.0, and the first preset value is set to 0.95. A high stability index indicates small temperature fluctuations, making the calculated net heat flow more reliable. A higher weighting is assigned to improve the accuracy of the heat leakage coefficient estimation.
[0058] When the stability index is within a second preset range, the weighting coefficient is set to a second preset value. In this embodiment, the second preset range is set to 0 to 0.7, and the second preset value is set to 0.7. A low stability index indicates drastic temperature fluctuations, which may be affected by instantaneous disturbances such as opening and closing doors or defrosting. In this case, the reliability of the calculated net heat flow is reduced, so a low weight is assigned to filter out interference and avoid misjudgment.
[0059] like Figure 2 The figure shows a comparison of the effects of a state monitoring method for a refrigerator according to an embodiment of the present invention. The horizontal axis represents the sampling step size, the upper vertical axis represents the estimated load heat capacity, and the lower vertical axis represents the estimated heat leakage coefficient. Solid lines represent the estimation curves using the method of the present invention, while dashed lines represent the estimation curves using the traditional fixed forgetting factor algorithm. The comparison of load heat capacity estimations shows that when the load inside the refrigerator increases abruptly, the method of the present invention dynamically adjusts the forgetting factor using a stability index, making the model more sensitive to new data, thus enabling it to quickly track changes in load heat capacity and converge rapidly to the true value. The comparison of heat leakage coefficient estimations shows that during the transient process of load changes, the traditional method cannot effectively decouple the influence of load heat absorption, resulting in significant oscillations and overshoot in the estimated heat leakage coefficient. In contrast, the method of the present invention calculates and subtracts the load heat flow to obtain the net heat flow, effectively eliminating load interference and making the estimated heat leakage coefficient curve more stable, thus more accurately reflecting the true insulation performance of the refrigerator.
[0060] Through the weighting strategy described above, the heat leakage coefficient model can maintain robustness under complex operating conditions and accurately reflect the true sealing performance of the refrigerator body and door seal.
[0061] S4: Determine the refrigerator's load capacity based on the estimated load heat capacity, and determine the refrigerator's sealing performance based on the estimated heat leakage coefficient.
[0062] After obtaining the estimated load heat capacity and heat leakage coefficient, the refrigerator's load and sealing performance are determined respectively. For example... Figure 3 The figure shows a state change curve of a state monitoring method for a refrigerator according to an embodiment of the present invention. The horizontal axis represents the sampling step size, the upper vertical axis represents the refrigerator's load level, and the lower vertical axis represents the refrigerator's sealing status. The method of the present invention maintains correct judgment results throughout the entire process, without false alarms. This further verifies that the method of the present invention can effectively achieve independent decoupling between load determination and sealing determination, significantly improving the monitoring's anti-interference capability and accuracy.
[0063] Determining the refrigerator's load based on the estimated load heat capacity includes: First, pre-setting multiple consecutive load heat capacity ranges and assigning a corresponding load level to each range. In this embodiment, the load heat capacity is divided into four ranges: 0 to 5000 joules per degree Celsius corresponds to the no-load level, 5000 to 15000 joules per degree Celsius corresponds to the light-load level, 15000 to 30000 joules per degree Celsius corresponds to the medium-load level, and above 30000 joules per degree Celsius corresponds to the heavy-load level. Second, determining the load heat capacity range to which the estimated load heat capacity belongs. Finally, outputting the load level corresponding to this range as the refrigerator's load determination result. This determination result can be used to optimize the compressor start-stop strategy, extending the shutdown time to save energy under light load and shortening the shutdown time to ensure cooling effect under heavy load.
[0064] Determining the refrigerator's airtightness based on the estimated heat leakage coefficient includes: First, setting a preset airtightness abnormality threshold. In this embodiment, based on the refrigerator's factory calibration data, the airtightness abnormality threshold is set to 1.5 times the normal heat leakage coefficient. Second, comparing the estimated heat leakage coefficient with the airtightness abnormality threshold. If the estimated heat leakage coefficient is greater than the airtightness abnormality threshold, a refrigerator airtightness abnormality signal is generated. This signal alerts the user via the refrigerator display screen or a mobile application to check if the door seal is aging or if the door is properly closed, allowing for timely maintenance to avoid increased energy consumption and decreased food preservation quality.
[0065] According to a second aspect of this application, this application also provides a condition monitoring system for a refrigerator. Figure 4 This is a structural block diagram of a condition monitoring system for a refrigerator according to an embodiment of this application. Figure 4 As shown, the system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a method for monitoring the status of a refrigerator according to the first aspect of this application. The system also includes other components well-known to those skilled in the art, such as a communication bus and a communication interface. Their configuration and functions are known in the art and will not be described further here.
[0066] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be within the scope of protection of the present invention.
Claims
1. A method for monitoring the status of a refrigerator, characterized in that, Monitoring methods include: Acquire internal and external temperature data of the refrigerator, as well as cooling capacity data of the refrigeration system; The stability index is obtained by calculating the fluctuation characteristics of the internal temperature data of the refrigerator within a preset time period, including: calculating the temperature change rate sequence of the internal temperature data of the refrigerator within a preset time period; calculating the variance of the temperature change rate sequence as the fluctuation characteristic value; and normalizing the fluctuation characteristic value to obtain the stability index. The stability index is negatively correlated with the fluctuation characteristic value and is used to characterize the stability of the internal temperature state of the refrigerator. A load heat capacity model based on recursive least squares is constructed based on cooling capacity data and temperature change rate. The forgetting factor of the load heat capacity model is dynamically adjusted according to the stability index, and the estimated load heat capacity at the current moment is obtained using the adjusted load heat capacity model. A heat leakage coefficient model based on recursive least squares is constructed based on net heat flow and the temperature difference between the inside and outside of the refrigerator. The weighting coefficients of the heat leakage coefficient model are adjusted according to the stability index. The load heat flow is obtained by multiplying the estimated load heat capacity by the current temperature change rate. The load heat flow is obtained by subtracting the cooling capacity data from the cooling capacity data. The net heat flow is then input into the adjusted heat leakage coefficient model to obtain the estimated heat leakage coefficient. The load of the refrigerator is determined based on the estimated load heat capacity, and the airtightness of the refrigerator is determined based on the estimated heat leakage coefficient.
2. The method for monitoring the status of a refrigerator according to claim 1, characterized in that, The calculation process of the forgetting factor includes: Establish a positive correlation mapping function between the stability index and the forgetting factor; Calculate the difference between the stability index at the current moment and the preset threshold to obtain the deviation value; The deviation value is substituted into the positive correlation mapping function for calculation to obtain the correction amount for the current forgetting factor; the correction amount is added to the current forgetting factor to obtain the adjusted forgetting factor. Boundary limiting processing is performed on the adjusted forgetting factor: if the adjusted forgetting factor exceeds the preset upper limit value, it is assigned the preset upper limit value; if the adjusted forgetting factor is lower than the preset lower limit value, it is assigned the preset lower limit value.
3. The method for monitoring the status of a refrigerator according to claim 2, characterized in that, The process of constructing the load heat capacity model includes: Establish a parameter estimation equation describing the linear relationship between cooling capacity data and temperature change rate, where the load heat capacity is the parameter to be estimated, the temperature change rate is the input variable, and the cooling capacity data is the observed value; The parameter estimation equation is solved using a recursive least squares algorithm. In each iteration, the covariance matrix is updated using the adjusted forgetting factor, thereby updating the estimated load heat capacity.
4. The method for monitoring the status of a refrigerator according to claim 1, characterized in that, The process of constructing the heat leakage coefficient model includes: Establish a linear relationship between net heat flow and the temperature difference between the inside and outside of the refrigerator; The linear relationship is converted into a parameter estimation equation, where the heat leakage coefficient is set as the parameter to be estimated, the net heat flow is set as the observed value, and the temperature difference between the inside and outside of the refrigerator is set as the input variable. The parameter estimation equation is solved using a recursive least squares algorithm. In each recursive iteration step, the reciprocal of the weighting coefficient is introduced into the denominator of the gain coefficient calculation formula of the recursive least squares algorithm, thereby updating the estimated value of the heat leakage coefficient.
5. A method for monitoring the status of a refrigerator according to claim 1, characterized in that, The calculation process of the weighting coefficients includes: The range of values for the stability index is divided into two intervals, and a positive correspondence is established between the weighting coefficient and the stability index. When the stability index is within a first preset range, the weighting coefficient is set to a first preset value; When the stability index is within the second preset range, the weighting coefficient is set to the second preset value; Wherein, the value in the first preset interval is greater than the value in the second preset interval, and the first preset value is greater than the second preset value.
6. The method for monitoring the status of a refrigerator according to claim 1, characterized in that, Determining the refrigerator's load capacity based on the estimated load heat capacity includes: Multiple consecutive load thermal capacity ranges are preset, and a corresponding load level is assigned to each range; Determine the load heat capacity range to which the estimated load heat capacity belongs; Output the load level corresponding to this range as the result of the refrigerator's load determination.
7. The method for monitoring the status of a refrigerator according to claim 1, characterized in that, Determining the refrigerator's airtightness based on the estimated heat leakage coefficient includes: Preset threshold for abnormal sealing; Compare the estimated heat leakage coefficient with the sealing anomaly threshold; If the estimated heat leakage coefficient is greater than the sealing abnormality threshold, a refrigerator sealing abnormality signal is generated.
8. A status monitoring system for a refrigerator, characterized in that, include: processor; A memory, wherein a computer program is stored; The processor is configured to execute the computer program to implement a method for monitoring the status of a refrigerator as described in any one of claims 1 to 7.