Anomaly detection method for ice maker, and ice making apparatus, ice maker and storage medium

Abstract

An anomaly detection method for an ice maker, and an apparatus, an ice maker and a storage medium. The anomaly detection method for an ice maker comprises: acquiring a plurality of pieces of consecutive detection data regarding preset operating parameters of an ice maker, wherein the detection data comprises first detection data and second detection data (S110); using a preset fitting model to perform first linear fitting on the first detection data, so as to obtain first fitting data corresponding to each piece of first detection data, and using the preset fitting model to perform second linear fitting on the second detection data, so as to obtain second fitting data corresponding to each piece of second detection data (S120); determining a first sum of variances corresponding to the first detection data and the first fitting data, and a second sum of variances corresponding to the second detection data and the second fitting data (S130); and determining an abnormal state of the ice maker on the basis of the relationship between the first sum of variances and the second sum of variances (S140).

Description

Methods for detecting malfunctions in ice makers, ice-making devices, ice makers and storage media Technical Field

[0001] This application relates to the field of refrigeration technology, such as a method, apparatus, ice maker, and storage medium for detecting abnormalities in an ice maker. Background Technology

[0002] Ice makers are widely used household appliances. Most conventional ice makers lack methods for detecting ice-making malfunctions, especially chewing ice makers. When the ice extruder freezes, continued operation for extended periods greatly increases the likelihood of damaging the gearbox and motor, and also generates significant dry friction noise, negatively impacting the user experience. Therefore, there is an urgent need for a method to detect ice maker malfunctions in real time. Summary of the Invention

[0003] This application provides an ice maker anomaly detection method, device, ice maker, and storage medium, achieving the effect of real-time detection of ice maker malfunctions.

[0004] According to one aspect of this application, a method for detecting anomalies in an ice maker is provided, comprising:

[0005] Acquire multiple continuous detection data of preset operating parameters of the ice maker, wherein the continuous detection data includes multiple first detection data of a first group and multiple second detection data of a second group;

[0006] A first linear fit is performed on the first detection data using a preset fitting model to obtain the first fitted data corresponding to each first detection data; a second linear fit is performed on the second detection data using a preset fitting model to obtain the second fitted data corresponding to each second detection data.

[0007] Determine the first sum of variances corresponding to the first detection data and the first fitted data, and the second sum of variances corresponding to the second detection data and the second fitted data;

[0008] The abnormal state of the ice maker is confirmed based on the relationship between the first variance and the second variance.

[0009] According to another aspect of this application, an ice-making apparatus is provided, comprising:

[0010] The parameter receiving module is configured to acquire multiple continuous detection data of preset operating parameters of the ice maker, wherein the continuous detection data includes multiple first detection data in a first group and multiple second detection data in a second group;

[0011] The parameter fitting module is configured to perform a first linear fit on the first detection data using a preset fitting model to obtain the first fitted data corresponding to each first detection data; and to perform a second linear fit on the second detection data using a preset fitting model to obtain the second fitted data corresponding to each second detection data.

[0012] The deviation calculation module is configured to determine the first variance sum corresponding to the first detection data and the first fitted data, and the second variance sum corresponding to the second detection data and the second fitted data;

[0013] The anomaly detection module is configured to determine the abnormal state of the ice maker based on the relationship between the first variance sum and the second variance sum.

[0014] According to another aspect of this application, an ice maker is provided, comprising:

[0015] One or more processors;

[0016] Memory, used to store one or more programs;

[0017] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-described method for detecting anomalies in an ice maker.

[0018] According to another aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, comprising: when the program is executed by a processor, implementing the above-described method for detecting anomalies in an ice maker. Attached Figure Description

[0019] Figure 1 is a flowchart of an abnormality detection method for an ice maker provided in Embodiment 1 of this application;

[0020] Figure 2 is a schematic diagram of the functional modules of an ice-making device provided in Embodiment 2 of this application;

[0021] Figure 3 is a schematic diagram of the frame structure of an ice maker provided in Embodiment 3 of this application.

[0022] Figure 4 is a schematic diagram of the control system of the ice maker in Figure 3;

[0023] Figure 5 shows the detection data, fitting variance, and variance ratio of the exhaust temperature and evaporation temperature obtained and stored. Detailed Implementation

[0024] Most conventional ice makers lack methods for detecting ice-making abnormalities, especially chewing ice makers. When the ice extruder freezes, continued operation for an extended period greatly increases the likelihood of damaging the gearbox and motor, and also generates significant dry friction noise, negatively impacting the user experience. Therefore, this application provides a method for real-time detection of ice-making abnormalities in an ice maker.

[0025] Example 1

[0026] Figure 1 is a flowchart of an anomaly detection method for an ice maker according to Embodiment 1 of this application. This embodiment can be applied to an ice-making device, which can be implemented by software and / or hardware, and is generally integrated into the ice maker. Accordingly, as shown in Figure 1, the method includes the following operations:

[0027] S110. Obtain multiple continuous detection data of preset operating parameters of the ice maker, wherein the continuous detection data includes multiple first detection data of a first group and multiple second detection data of a second group;

[0028] In one embodiment, referring to Figure 300, the ice maker 300 may include a compressor 310, a condenser 320, a capillary tube 330, an evaporator 340, and a control system 350. The preset operating parameters may include at least one of the following: exhaust temperature, evaporation temperature, compressor power, and condensation temperature. For example, the preset operating parameters may be at least one of the following: the exhaust temperature Tp of the compressor 310 exhaust pipe, the evaporation temperature Zf of the evaporator 340, the compressor power, and the condensation temperature Ln of the condenser 320, all detected by sensors 361-364. In this embodiment, multiple operating parameters are correlated; that is, when refrigeration malfunctions, the changes in these correlated parameters follow similar patterns, but the magnitudes of the changes differ. For example, a sensor 361 can be installed on the exhaust pipe of the compressor 310 shown in Figure 300, a sensor 362 can be installed on the copper pipe outlet of the evaporator 340, and a sensor 363 or an output node of the condenser 320 can be installed to sense the corresponding operating parameters. The control system 350 obtains the corresponding sensing results from the sensors 361-364. The compressor power is directly calculated by the sensor after the ammeter on the circuit board inside the ice maker detects the operating current of the compressor. That is, the compressor power is equal to the product of the compressor's operating voltage and operating current. Generally, the operating voltage is a constant value and can be used directly when calculating the compressor power. Since there are small fluctuations in the value during power supply, a voltmeter can also be installed on the circuit board inside the ice maker to detect the operating voltage in real time. In one embodiment, the obtained continuous detection data is stored sequentially in a preset storage unit, such as y(1)...y(n).

[0029] In one embodiment, the number of consecutive detection data can be 20-60, for example, 30. In some embodiments, the number of consecutive detection data is an even number. In one embodiment, the first group and the second group have the same number of data, 15 each. The first group of detection data is stored in y(1)...y(15), and the second group of detection data is stored in y(16)...y(30).

[0030] In one embodiment, the plurality of first detection data in the first group are all acquired before the plurality of second detection data in the second group.

[0031] In one embodiment, when monitoring an operating parameter, such as the evaporation temperature t, the initial detection and storage conditions are shown in Table 1 below:

[0032] Table 1: Detection data of evaporation temperature obtained and stored in the initial detection.

[0033] S120. Use a preset fitting model to perform a first linear fit on the first detection data to obtain the first fitted data corresponding to each first detection data; use a preset fitting model to perform a second linear fit on the second detection data to obtain the second fitted data corresponding to each second detection data.

[0034] In one embodiment, the preset fitting model is a linear model; the linear model can be...

[0035] Where, when n = 1, 2, 3, ..., j, The first fitted data

[0036] Where, when n = j+1, j+2, ..., i, For the second fitted data

[0037] The initial detection data sampling sequence number is x(n)=n=1,2,3,…j,j+1,j+2,...,i; where j is the number of the first detection data; and ij is the number of the second detection data.

[0038] The parameters b and a are calculated as follows:

[0039] Where, x i Equal to sampling number i, y i The detection result corresponding to the sampling sequence number i. The average of the first or second test data. It is the average of the serial numbers of the first test data or the average of the serial numbers of the second test data.

[0040] In this embodiment, when performing a first linear fit on the first detection data using a preset fitting model, a set of fitting parameters is calculated separately, for example, parameters b1 and a1 are obtained, wherein the first fitting data The calculation uses the first set of fitting parameters b1 and a1, for example... When performing a second linear fit on the second detection data using a preset fitting model, two sets of fitting parameters are calculated separately, for example, parameters b2 and a2 are obtained, where the second fitting data... The calculation uses the second set of fitting parameters b2 and a2, for example

[0041] In one embodiment, referring to Table 2 below, the distribution of the first and second fitted data is shown:

[0042] Table 2: Fitted data calculated from the detection data of evaporation temperature

[0043] S130. Determine the first variance sum corresponding to the first detection data and the first fitted data, and the second variance sum corresponding to the second detection data and the second fitted data.

[0044] In one embodiment, the calculation method for the sum of the first variances corresponding to the first detection data and the first fitted data is as follows:

[0045] The calculation method for the sum of the second variances corresponding to the second detection data and the second fitted data is determined as follows:

[0046] Where y1(n) is the first detection data obtained from detection sequence number 1 to j, and y2(n) is the second detection data obtained from detection sequence number j+1 to i. To obtain the first fitted data for the corresponding detection numbers 1 to j, Obtain the second fitted data for the corresponding detection numbers j+1 to i.

[0047] S140. Confirm the abnormal state of the ice maker based on the relationship between the first variance sum and the second variance sum.

[0048] In one embodiment, the method for determining the abnormal state of the ice maker based on the relationship between the first variance and the second variance is as follows: S2(n) > α × S1(n), where α is a sensitivity control parameter. When S2(n) > α × S1(n) is true, it indicates that the ice maker is malfunctioning. When S2(n) > α × S1(n) is false, it indicates that the current cooling function is operating normally. Based on a preset update frequency, such as 10 seconds, the detection data is shifted and updated, and then the determination of whether S2(n) > α × S1(n) is true is made again. The method for shifting and updating the detection data is as follows: y(k) = y(k+1); where k = 1, 2, 3, ..., n-1, y(n) = t(n+1), where t(n+1) is the latest detection data acquired at the next moment.

[0049] Referring to Table 3 below, the storage and simulation of the detection data t31 obtained after the first update detection are as follows:

[0050] Table 3: Detection data and fitting data of evaporation temperature obtained in the first update detection.

[0051] Referring to Table 4 below, the storage and simulation of the detection data t32 obtained after the second update detection are shown:

[0052] Table 4: Detection data and fitting data of evaporation temperature obtained from the second update detection.

[0053] In alternative embodiments, more than two operating parameters can be judged. The data storage, parameter calculation, and judgment method for each operating parameter can refer to the foregoing embodiments. When two operating parameters, such as evaporation temperature t and compressor power p, are monitored simultaneously, the evaporation temperature data can be referred to Tables 1-4, and the initial detection, storage, and simulation of compressor power p can be referred to Table 5.

[0054] Wherein, y'(n) is the location where the compressor power p is stored after each detection. The first detection data y'3(n) of the compressor power is obtained corresponding to the detection sequence number 1 to j (1-15 in the table above), and the second detection data y'4(n) of the compressor power is obtained corresponding to the detection sequence number j+1 to i (16-30 in the table above). This refers to the third set of fitted data obtained corresponding to detection numbers 1 to j. The fourth set of fitted data is obtained for the corresponding detection serial numbers j+1 to i. For example, in this embodiment, when performing a third linear fit on the compressor power corresponding to the first detection data using a preset fitting model, a third set of fitting parameters is calculated separately, such as parameters b3 and a3, where the third fitted data... The calculation uses the third set of fitting parameters b3 and a3, for example When performing a fourth linear fit on the second detection data corresponding to the compressor power using a preset fitting model, the fourth set of fitting parameters is calculated separately, for example, parameters b4 and a4 are obtained, where the fourth fitting data... The calculation uses the fourth set of fitting parameters b4 and a4, for example Based on the variance and formula, the third variance sum corresponding to the first detection data and the third fitted data in the compressor power is determined as follows: And based on the variance and formula, the fourth variance sum of the second detection data and the fourth fitted data corresponding to the compressor power is determined as follows:

[0055] Table 5: Initial detection data and fitting data of compressor power.

[0056] In this embodiment, the evaporation temperature t and compressor power p can be simultaneously detected and judged. The judgment formula related to evaporation temperature is: S2(n)>α×S1(n); the judgment formula related to compressor power p is: S2'(n)>α'×S1'(n), where α is the abnormal sensitivity parameter of evaporation temperature and α' is the abnormal sensitivity parameter of compressor power. When both of the above formulas are true, it can be judged that the ice maker is malfunctioning; otherwise, the ice maker is operating normally. The detection results can be updated and shifted in the same way as the evaporation temperature detection at preset intervals, such as 10-30 seconds, and then re-judged to achieve real-time monitoring of ice maker malfunctions.

[0057] In one embodiment, the preset operating parameters may be multiple, and the method for determining the abnormal state of the ice maker based on the relationship between the first variance and the second variance is as follows: when S2(n)>α×S1(n), S2'(n)>α'×S1'(n), S2"(n)>α"×S1"(n)... are all true, it indicates that the ice maker is abnormal, where α, α', α"... are control parameters for the sensitivity of different preset operating parameters.

[0058] In this embodiment, the shift update detection data is performed as follows: y(k) = y(k+1); where k = 1, 2, 3, ..., n-1, y(n) = p(n+1), where p(n+1) is the latest detection data obtained at the next moment.

[0059] Referring to Table 6 below, the storage and simulation results are as follows after the compressor power detection data (p31) is obtained from the first update detection:

[0060] Table 6: Detection data and fitting data of compressor power obtained from the first update detection.

[0061] Referring to Table 7 below, the storage and simulation of the compressor power detection data p32 obtained after the second update detection are shown:

[0062] Table 7: Compressor power detection data and fitting data obtained from the second update detection

[0063] Compared to related technologies, the anomaly detection method of the ice maker in this embodiment divides the detection data into two groups and performs linear fitting on each group. When an operational anomaly occurs, the fitting result of the latter group corresponding to at least one monitored operating parameter will be greater than the normal proportion of the fitting result of the former group, that is, satisfying S2(n)>α×S1(n) and / or satisfying S2'(n)>α'×S1'(n). This allows for real-time monitoring and detection of anomalies. In addition, since the two sets of related operating parameters are monitored synchronously at the same time, false alarms caused by single, occasional data anomalies are avoided, further ensuring the accuracy of the ice maker anomaly judgment.

[0064] In alternative embodiments, when the ice maker malfunctions, the process may further include: performing defrosting and ice-melting operations, or allowing the ice to melt naturally after a preset power outage, or notifying the system to perform an anomaly check. For example, a preset program can automatically perform defrosting and ice-melting operations, allow the ice to melt naturally after a preset power outage, or notify the system to perform an anomaly check when an anomaly occurs. Alternatively, maintenance information can be set to schedule corresponding maintenance work orders based on the time of the anomaly and send them to the maintenance personnel's mobile phones.

[0065] In an alternative embodiment, if the number of updates after an anomaly occurs does not exceed the number of the second group, and if the anomaly determination continues to occur after each update, the anomaly can be determined to be a serious anomaly, and an anomaly check needs to be performed. If the anomaly determination occurs occasionally after each update, the anomaly can be determined to be a temporary anomaly, and defrosting and ice-melting operations can be performed first, or the power can be cut off for a preset time to allow natural melting.

[0066] In an alternative embodiment, exhaust temperature, evaporation temperature, and compressor power can also be detected and judged simultaneously, with the same principle as before, so it will not be described again.

[0067] This application is mainly used to detect the transition of an ice maker from a normal state to an abnormal state. It is applicable to situations where the initial power-on detection is mostly normal, and if subsequent updated data shows abnormalities, the method in this application makes it easier to detect the transition from normal to abnormal.

[0068] The following is an explanation using actual test data. Figure 5 shows the test data, fitting variances, and variance ratios corresponding to the exhaust temperature and evaporation temperature obtained and stored (only some data are shown in the figure). Among them, t1 corresponds to the actual test result of exhaust temperature Tp, t2 corresponds to the actual test result of evaporation temperature Zf, S1(n) corresponds to the first 25 fitting variances, S2(n) corresponds to the last 25 fitting variances, and the corresponding variance ratios are α1 = S2(n) / S1(n) and α2 = S2(n) / S1(n).

[0069] When the ice maker is operating normally, the continuous detection data is basically linearly distributed; that is, the first line segment fitted by multiple first detection data points in the first group and the second line segment fitted by multiple second detection data points in the second group are essentially a straight line. When the ice maker changes from normal to abnormal operation, the first line segment fitted by multiple first detection data points and the second line segment fitted by multiple second detection data points in the second group become two straight lines with different slopes. In Figure 5, the calculated variance and S1(n) reflect the total distance of the detection data corresponding to the first line segment from the fitted first line segment, and the calculated variance and S2(n) reflect the total distance of the detection data corresponding to the second line segment from the fitted second line segment. The data in rows 571-574 represents the time when an anomaly occurs. The exhaust temperature data itself is not obvious, but after calculation and transformation, a sudden increase in α1 confirms that an anomaly has occurred.

[0070] The sensitivity control parameter α was determined through limited experiments. Those skilled in the art can first obtain α1 under abnormal conditions based on experimental data, and then set α based on α1 under abnormal conditions. For example, in this embodiment, taking exhaust temperature as an example, α can be set to 8; for higher sensitivity, it can be set to 9. As shown in Figure 5, under normal circumstances, the ratio α1 of S2(n) and S1(n) is less than 8 (e.g., in row 561 data). When an anomaly occurs, the ratio α1 of S2(n) and S1(n) will be close to or greater than 8 (e.g., in rows 571-574 data). Therefore, when the actual data calculation result α1 = A2(n) / S1(n) > 8, that is, the total distance S2(n) of the detection data corresponding to the second line segment deviating from the fitted second line segment is greater than α times the total distance S1(n) of the detection data corresponding to the first line segment deviating from the fitted first line segment, it can be basically determined that an anomaly has occurred.

[0071] Referring to Figure 5, taking evaporation temperature as an example, α2 = S2(n) / S1(n). Based on the α2 values ​​in rows 563-571, it can be seen that under abnormal conditions, the α2 value is generally greater than 9.9. If α2 is less than 9.9, it indicates normal fluctuations during equipment startup and power adjustment to a stable state. In other words, normal fluctuations are generally less than 9.9. Therefore, the sensitivity control parameter α can be set to 9. If experiments show that an α2 value of 9 sometimes also falls within the normal fluctuation range, then to improve sensitivity and avoid misjudgment, the sensitivity control parameter α can be set to 10.

[0072] The sensitivity control parameter α is a constant determined experimentally during the production of a specific equipment model, and this constant is written into the specific equipment model's program. Different parameters of the same equipment model, such as exhaust temperature and evaporation temperature, may also require different thresholds. For example, in Figure 5, the sensitivity control parameter α corresponding to exhaust temperature can be set to 8, and the sensitivity control parameter α corresponding to evaporation temperature can be set to 9. For different equipment models, the sensitivity control parameter α for the same influencing factor may be set differently. For example, taking evaporation temperature as an example, the sensitivity parameter α for evaporation temperature of equipment model A is set to 9, while the sensitivity parameter α for evaporation temperature of equipment model B is set to 14.

[0073] The present application discloses an abnormality detection method for an ice maker. This method acquires multiple continuous detection data points of preset operating parameters of the ice maker. Based on the number of continuous detection data points, the method divides the data into a first group and a second group, where the first group includes multiple first detection data points and the second group includes multiple second detection data points. A preset fitting model is used to perform a first linear fit on the first detection data points to obtain first fitted data points corresponding to each first detection data point. A preset fitting model is then used to perform a second linear fit on the second detection data points to obtain second fitted data points corresponding to each second detection data point. A first variance sum corresponding to the first detection data points and the first fitted data points, and a second variance sum corresponding to the second detection data points and the second fitted data points, are determined. The relationship between the first variance sum and the second variance sum confirms the abnormal state of the ice maker, thereby achieving real-time detection of whether the ice maker's refrigeration is abnormal. This method is convenient to implement and has a low cost. Furthermore, since this application can simultaneously monitor two related operating parameters, the accuracy and reliability of the ice maker's abnormality judgment results are ensured.

[0074]

Example 2

[0075] Figure 2 is a schematic diagram of an ice-making device provided in Embodiment 2 of this application. The device 200 can be implemented by software and / or hardware and can generally be integrated into an ice maker as shown in Figure 2. The device 200 includes a parameter receiving module 210, a parameter fitting module 220, a deviation calculation module 230, and an anomaly judgment module 240.

[0076] The parameter receiving module 210 is configured to acquire multiple continuous detection data of preset operating parameters of the ice maker, the continuous detection data including multiple first detection data of a first group and multiple second detection data of a second group.

[0077] In one embodiment, referring to Figure 300, the ice maker 300 may include a compressor 310, a condenser 320, a capillary tube 330, an evaporator 340, and a control system 350. The preset operating parameters may be at least one of the following: the exhaust temperature Tp of the compressor 310's exhaust pipe, the evaporation temperature Zf of the evaporator 340, the compressor power, and the condensation temperature Ln of the condenser 320, all detected by sensors 361-364. In this embodiment, multiple operating parameters are correlated; that is, when refrigeration malfunctions, the changes in these correlated parameters follow similar patterns, but the magnitudes of change differ. For example, a sensor 361 may be installed on the exhaust pipe of the compressor 310 (as shown in Figure 300), a sensor 362 may be installed at the copper tube outlet of the evaporator 340, and a sensor 363 may be installed at the input node or the output node of the condenser 320 to sense the corresponding operating parameters. The control system 350 obtains the corresponding sensing results from sensors 361-364.

[0078] In one embodiment, the parameter receiving module 210 is configured to store the obtained continuous detection data sequentially into a preset storage unit, such as y(1)...y(n), according to the detection order. In one embodiment, when monitoring an operating parameter, such as the evaporation temperature t, the storage of the initial detection data is shown in Table 1.

[0079] In one embodiment, the first group includes multiple first detection data, and the second group includes multiple second detection data, wherein the multiple first detection data of the first group are all acquired before the multiple second detection data of the second group.

[0080] In one embodiment, the number of consecutive detection data can be 20-60, for example, 30. In some implementations, the number of consecutive detection data is an even number. In one embodiment, the first group and the second group have the same number of data, 15 each. The first group of detection data is stored in y(1)...y(15), and the second group of detection data is stored in y(16)...y(30).

[0081] The parameter fitting module 220 is configured to perform a first linear fit on the first detection data using a preset fitting model to obtain the first fitted data corresponding to each first detection data; and to perform a second linear fit on the second detection data using a preset fitting model to obtain the second fitted data corresponding to each second detection data.

[0082] In one embodiment, the linear model is:

[0083] Where, when n = 1, 2, 3, ..., j, The first fitted data

[0084] Where, when n = j+1, j+2, ..., i, For the second fitted data

[0085] The initial detection data sampling sequence number is x(n)=n=1,2,3,…j,j+1,j+2,...,i; where j is the number of the first detection data; and ij is the number of the second detection data.

[0086] The parameters b and a are calculated as follows:

[0087] Where, x i Equal to sampling number i, y i The detection result corresponding to the sampling sequence number i. The average of the first or second test data. It is the average of the serial numbers of the first test data or the average of the serial numbers of the second test data.

[0088] In this embodiment, when performing a first linear fit on the first detection data using a preset fitting model, a set of fitting parameters is calculated separately, for example, parameters b1 and a1 are obtained, wherein the first fitting data The calculation uses the first set of fitting parameters b1 and a1, for example... When performing a second linear fit on the second detection data using a preset fitting model, two sets of fitting parameters are calculated separately, for example, parameters b2 and a2 are obtained, where the second fitting data... The calculation uses the second set of fitting parameters b2 and a2, for example In one embodiment, the distribution of the second fitted data is shown in Table 2.

[0089] The deviation calculation module 230 is configured to determine the first variance sum corresponding to the first detection data and the first fitted data, and the second variance sum corresponding to the second detection data and the second fitted data.

[0090] In one embodiment, the calculation method for the sum of the first variances corresponding to the first detection data and the first fitted data is as follows:

[0091] The calculation method for the sum of the second variances corresponding to the second detection data and the second fitted data is determined as follows:

[0092] Where y1(n) represents the first detection data obtained corresponding to detection sequence numbers 1 to j, and y2 ( n ) This refers to the second detection data obtained for the corresponding detection serial numbers j+1 to i. To obtain the first fitted data for the corresponding detection numbers 1 to j, Obtain the second fitted data for the corresponding detection numbers j+1 to i.

[0093] The anomaly detection module 240 is configured to determine the abnormal state of the ice maker based on the relationship between the first variance sum and the second variance sum.

[0094] In one embodiment, the method for determining the abnormal state of the ice maker based on the relationship between the first variance and the second variance is as follows: S2(n) > α × S1(n), where α is a sensitivity control parameter. When S2(n) > α × S1(n) is true, it indicates that the ice maker is malfunctioning. When S2(n) > α × S1(n) is false, it indicates that the current cooling function is operating normally. Based on a preset update frequency, such as 10 seconds, the detection data is shifted and updated, and then the determination of whether S2(n) > α × S1(n) is true is made again. The method for shifting and updating the detection data is as follows: y(k) = y(k+1); where k = 1, 2, 3, ..., n-1, y(n) = t(n+1), where t(n+1) is the latest detection data acquired at the next moment.

[0095] Table 3 shows the storage and simulation results after the first update detection data t31 is obtained. Table 4 shows the storage and simulation results after the second update detection data t32 is obtained.

[0096] In alternative embodiments, more than two operating parameters can be judged. The data storage, parameter calculation, and judgment method for each operating parameter can refer to the foregoing embodiments. When two operating parameters, such as evaporation temperature t and compressor power p, are monitored simultaneously, the evaporation temperature data can be referred to Tables 1-4, and the initial detection, storage, and simulation of compressor power p can be referred to Table 5.

[0097] Where y'(n) is the location where the compressor power p is stored after each detection, and y'3(n) is the first detection data of the compressor power obtained for the corresponding detection sequence number 1 to j (1-15 in the table above), and y'4(n) is the second detection data of the compressor power obtained for the corresponding detection sequence number j+1 to i (16-30 in the table above). To obtain the third set of fitted data corresponding to detection numbers 1 to j, The fourth set of fitted data is obtained for the corresponding detection serial numbers j+1 to i. For example, in this embodiment, when performing a third linear fit on the compressor power corresponding to the first detection data using a preset fitting model, three sets of fitting parameters are calculated separately, such as parameters b3 and a3, where the third set of fitted data... The calculation uses the third set of fitting parameters b3 and a3, for example When performing a fourth linear fit on the second detection data corresponding to the compressor power using a preset fitting model, four sets of fitting parameters are calculated separately, for example, parameters b4 and a4 are obtained, where the fourth fitting data... The calculation uses the fourth set of fitting parameters b4 and a4, for example Based on the variance and formula, the third variance sum corresponding to the first detection data and the third fitted data in the compressor power is determined as follows: Based on the variance and formula, the fourth variance sum of the second detection data and the fourth fitted data corresponding to the compressor power is determined as follows:

[0098] In this embodiment, the evaporation temperature t and compressor power p can be simultaneously detected and judged. The judgment formula based on the evaporation temperature is: S2(n)>α×S1(n); the judgment formula based on the compressor power p is: S2'(n)>α'×S1'(n), where, α Here, α' is the abnormal sensitivity parameter for evaporation temperature, and α' is the abnormal sensitivity parameter for compressor power. When both formulas are true, it can be determined that the ice maker is malfunctioning; otherwise, the ice maker is operating normally. The detection results can be updated and shifted in the same way as the evaporation temperature detection at preset intervals (e.g., 10-30 seconds) to achieve real-time monitoring of ice maker malfunctions.

[0099] For the storage and simulation of the compressor power detection data obtained from the first update test (p31), please refer to Table 6. For the storage and simulation of the compressor power detection data obtained from the second update test (p32), please refer to Table 7.

[0100] The anomaly detection device in this embodiment divides the detection data into two groups and performs linear fitting on each group. When an operational anomaly occurs, the fitting result of the latter group corresponding to at least one monitored operating parameter will be greater than the normal proportion of the fitting result of the former group, that is, satisfying S2(n)>α×S1(n) and / or satisfying S2'(n)>α'×S1'(n). This allows for real-time monitoring and detection of anomalies. In addition, since the two sets of related operating parameters are monitored synchronously at the same time, false alarms caused by single, occasional data anomalies are avoided, further ensuring the accuracy of the ice maker's anomaly judgment.

[0101] In one embodiment, there are multiple preset operating parameters. The method for determining the abnormal state of the ice maker based on the relationship between the first variance and the second variance is as follows: when S2(n)>α×S1(n), S2'(n)>α'×S1'(n), S2"”>α”×S1"”)… are all true, it indicates that the ice maker has malfunctioned, where α, α', α”… are control parameters for the sensitivity of different preset operating parameters. In an alternative embodiment, it may also include: an ice-melting operation module, configured to perform defrosting and ice-melting operations after an abnormality occurs; a power-off melting module, configured to allow natural melting after a preset power-off time after an abnormality occurs; and an abnormality notification module, configured to notify the implementation of an abnormality check after an abnormality occurs.

[0102] In an alternative embodiment, the anomaly detection module is configured to, when the number of updates after an anomaly occurs does not exceed the number of the second group, if the anomaly judgment continues to occur after each update, the anomaly can be determined to be a serious anomaly, and an anomaly check needs to be notified. If the anomaly judgment occurs occasionally after each update, the anomaly can be determined to be a temporary anomaly, and defrosting and ice-melting operations can be performed first, or the power can be cut off for a preset time to allow natural melting.

[0103] The above-described ice-making device can execute the ice maker anomaly detection method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment can be found in the ice maker anomaly detection method provided in any embodiment of this application. Since the ice-making device described above is capable of executing the ice maker anomaly detection method in the embodiments of this application, those skilled in the art can understand the specific implementation methods and various variations of the ice-making device in this embodiment based on the ice maker anomaly detection method described in the embodiments of this application. Therefore, how the ice-making device implements the ice maker anomaly detection method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the ice maker anomaly detection method in the embodiments of this application falls within the scope of protection of this application.

[0104]

Example 3

[0105] Figure 3 shows a schematic diagram of an ice maker according to Embodiment 3 of this application. Figure 4 is a schematic diagram of the control system of the ice maker in Figure 3. As shown in Figures 3-4, the ice maker 300 may include a compressor 310, a condenser 320, a capillary tube 330, an evaporator 340, and a control system 350. The control system 350 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, which is communicatively connected to the at least one processor 11. The memory stores computer programs that can be executed by at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the ROM 12 or the computer program loaded from the storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the control system 350. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0106] Multiple components in the control system 350 are connected to the I / O interface 15, including: input units 16, such as a keyboard, mouse, etc.; output units 17, such as various types of displays, speakers, etc.; storage units 18, such as disks, optical disks, etc.; and communication units 19, such as network cards, modems, wireless transceivers, etc. The communication unit 19 allows the control system 350 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0107] Processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Processor 11 may include microcontrollers, central processing units (CPUs), graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the anomaly detection method for an ice maker.

[0108] In some embodiments, the anomaly detection method for the ice maker can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the control system 350 of the ice maker 300 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the anomaly detection method for the ice maker described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the anomaly detection method for the ice maker by any other suitable means (e.g., by means of firmware).

[0109] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chips (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0110] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0111] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Optionally, a computer-readable storage medium may be a machine-readable signal medium. A machine-readable storage medium may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, RAM, ROM, erasable programmable read-only memory (EPROM or flash memory), optical fiber, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0112] To provide interaction with a user, the systems and techniques described herein can be implemented on a mobile terminal having: a display device (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the mobile terminal. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0113] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0114] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system. It addresses the shortcomings of traditional physical hosts and Virtual Private Server (VPS) services, such as high management difficulty and weak business scalability.

[0115]

Example 4

[0116] Embodiment 4 of this application also provides a computer storage medium for storing a computer program, which, when executed by a computer processor, is used to perform the abnormal detection method for an ice maker described in any of the above embodiments of this application.

[0117] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. The computer-readable storage medium may include: an electrical connection having one or more wires, a portable computer disk, a hard disk, RAM, ROM, an erasable programmable read-only memory (EPROM, or flash memory), an optical fiber, a CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0118] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0119] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including wireless, wire, optical fiber, radio frequency (RF), or any suitable combination thereof.

Claims

1. A method for detecting abnormalities in an ice maker, comprising: Acquire multiple continuous detection data of preset operating parameters of the ice maker, wherein the continuous detection data includes multiple first detection data in a first group and multiple second detection data in a second group; The first linear fit is performed on the first detection data using a preset fitting model to obtain the first fitting data corresponding to each first detection data. The second linear fit is performed on the second detection data using a preset fitting model to obtain the second fitting data corresponding to each second detection data. Determine the first sum of variances corresponding to the first detection data and the first fitted data, and the second sum of variances corresponding to the second detection data and the second fitted data; The abnormal state of the ice maker is confirmed based on the relationship between the first variance and the second variance.

2. The ice maker abnormality detection method according to claim 1, wherein The preset fitting model is a linear model; the multiple first detection data of the first group are all obtained before the multiple second detection data of the second group.

3. The ice maker abnormality detection method according to claim 2, wherein The linear model is wherein n = 1, 2, 3,..., j when, for the first fitted data wherein n = j + 1, j + 2,..., i, for the second fitted data The initial detection data sampling sequence number is x(n) = n = 1, 2, 3, ..., j, j+1, j+2, ..., i; where j is the number of the first detection data; and ij is the number of the second detection data. wherein the parameters b and a are calculated as follows: wherein x i equals the sample number i, y i is the detection result corresponding to the sample number i, an average value of the first detection data or the second detection data, It is the average of the serial numbers of the first test data or the average of the serial numbers of the second test data.

4. The ice maker abnormality detection method according to claim 3, wherein The first sum of squares of the first detection data and the first fitted data is determined in a manner that: The second sum of squares is determined by calculating the sum of squares of the difference between the second detected data and the second fitted data: wherein y1(n) is the first detection data obtained corresponding to detection serial numbers 1 to j, y2(n) is the second detection data obtained corresponding to detection serial numbers j+1 to i, to obtain first fitting data for the corresponding detection numbers 1 to j, Obtain the second fitted data for the corresponding detection numbers j+1 to i.

5. The ice maker abnormality detection method according to claim 4, wherein The method for determining the abnormal state of the ice maker based on the relationship between the first variance and the second variance is as follows: when S2(n)>α×S1(n) is true, it indicates that the ice maker is abnormal, where α is a sensitivity control parameter.

6. The ice maker abnormality detection method according to claim 5, wherein When S2(n) > α × S1(n) is not true, the detection data is updated by shifting and then the validity of S2(n) > α × S1(n) is checked again. The method of shifting and updating the detection data is: y(k) = y(k+1), where k = 1, 2, 3, ..., n-1; y(n) = t(n+1), where t(n+1) is the latest detection data obtained at the next moment.

7. The method for detecting abnormalities in an ice maker according to claim 5, wherein when there are two or more preset operating parameters, the method for determining the abnormal state of the ice maker based on the relationship between the first variance sum and the second variance sum of each preset operating parameter is as follows: when S2(n)>α×S1(n), S2'(n)>α'×S1'(n), S2"(n)>α"×S1"(n)... are all true, it indicates that the ice maker has an abnormality, wherein α, α', α"... are control parameters for the sensitivity of different preset operating parameters.

8. The abnormal detection method for any ice maker according to claims 1-6, wherein the preset operating parameters include at least one of exhaust temperature, evaporation temperature, compressor power, and condensation temperature.

9. An ice-making apparatus, comprising: The parameter receiving module is configured to acquire multiple continuous detection data of preset operating parameters of the ice maker, wherein the continuous detection data includes multiple first detection data in a first group and multiple second detection data in a second group; The parameter fitting module is configured to use a preset fitting model to perform a first linear fit on the first detection data to obtain the first fitting data corresponding to each first detection data. The second linear fit is performed on the second detection data using a preset fitting model to obtain the second fitting data corresponding to each second detection data. The deviation calculation module is configured to determine the first variance sum corresponding to the first detection data and the first fitted data, and the second variance sum corresponding to the second detection data and the second fitted data; The anomaly detection module is configured to determine the abnormal state of the ice maker based on the relationship between the first variance sum and the second variance sum.

10. An ice maker, comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the anomaly detection method for an ice maker as described in any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the anomaly detection method for an ice maker as described in any one of claims 1-8.

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