Abnormality detection method of ice making machine, ice making device, ice making machine and storage medium

Abstract

The embodiment of the present invention discloses an abnormality detection method, device, ice maker and medium for an ice maker. The abnormality detection method for an ice maker includes obtaining a plurality of continuous detection data of preset operating parameters of the ice maker, the detection data including first detection data and second detection data; performing a first linear fitting on the first detection data using a preset fitting model to obtain first fitting data corresponding to each first detection data; performing a second linear fitting on the second detection data using a preset fitting model to obtain second fitting data corresponding to each second detection data; 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; and confirming the abnormal state of the ice maker based on the relationship between the first sum of variances and the second sum of variances. The abnormality detection method for an ice maker of this embodiment achieves the effect of real-time detection of abnormalities of an ice maker.

Description

Technical Field

[0001] The present invention relates to the field of refrigeration technology, and in particular to an abnormality detection method and device for an ice maker, an ice maker and a storage medium. Background Art

[0002] Ice makers are household appliances that are used by many residents. Most existing ice makers do not have a means to detect ice making anomalies, especially for chewing ice ice makers. When the ice squeezer freezes, continuing to work for a long time will have a high probability of damaging the gearbox and motor, and will generate a lot of dry friction noise, affecting the user experience. Therefore, there is an urgent need to provide a method for real-time detection of ice making anomalies in ice makers. Summary of the invention

[0003] The embodiments of the present invention provide an abnormality detection method and device for an ice maker, an ice maker and a storage medium, which achieve the effect of detecting abnormality of the ice maker in real time.

[0004] According to one aspect of the present invention, there is provided an abnormality detection method for an ice maker, comprising: acquiring a plurality of continuous detection data of preset operating parameters of the ice maker, the continuous detection data comprising a plurality of first detection data of a first group and a plurality of second detection data of a second group; performing a first linear fitting on the first detection data using a preset fitting model to obtain first fitting data corresponding to each first detection data; performing a second linear fitting on the second detection data using a preset fitting model to obtain second fitting data corresponding to each second detection data; 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; and confirming the abnormal state of the ice maker according to the relationship between the first sum of variances and the second sum of variances.

[0005] Furthermore, the preset fitting model is a linear model; the first group of multiple first detection data are all acquired before the second group of multiple second detection data.

[0006] Furthermore, the linear model is , where n=1,2,3…j, The first fitting data , where n=j+1,j+2…i, The second fitting data , the sequence number of the initial detection data sampling is x(n)=n=1,2,3…j,j+1,j+2…i; where j is the number of the first detection data; ij is the number of the second detection data, where the calculation method of parameters b and a is: ; ; where x i Equal to sampling number i, y iis the detection result corresponding to the sampling number i, is the average value of the first detection data or the second detection data, It is the average value of the serial numbers of the first detection data or the average value of the serial numbers of the second detection data.

[0007] Furthermore, the calculation method of the first sum of variances corresponding to the first detection data and the first fitting data is determined as follows: ; Determine the calculation method of the second sum of variances corresponding to the second detection data and the second fitting data as follows: ;in, is the first detection data obtained corresponding to the detection sequence number 1 to j, The second detection data obtained corresponding to the detection sequence number j+1 to i.

[0008] Further, the determination method for confirming the abnormal state of the ice maker according to the relationship between the first variance sum and the second variance sum is: when When established, it indicates that the ice maker is abnormal, wherein is the control parameter of sensitivity.

[0009] Furthermore, when If it is not true, shift and update the test data and make further judgments Whether it is established, the method of shifting and updating the detection data is: ; where k=1,2,3…n-1, ,in The latest detection data obtained at the next moment.

[0010] Furthermore, the preset operating parameters are two or more, and the determination method for confirming the abnormal state of the ice maker based on the relationship between the first sum of variances and the second sum of variances is: when , , …when all of them are true, it means that the ice maker is abnormal. , , …are control parameters for the sensitivity of different preset operating parameters.

[0011] Furthermore, the preset operating parameters may be one, two or more of the exhaust temperature, evaporation temperature, compressor power and condensation temperature.

[0012] According to another aspect of the present invention, an ice-making device is provided, comprising: a parameter receiving module, used to obtain a plurality of continuous detection data of preset operating parameters of the ice-making machine, the continuous detection data comprising a plurality of first detection data of a first group and a plurality of second detection data of a second group; a parameter fitting module, used to perform a first linear fitting on the first detection data using a preset fitting model to obtain first fitting data corresponding to each first detection data; perform a second linear fitting on the second detection data using a preset fitting model to obtain second fitting data corresponding to each second detection data; a deviation calculation module, used to determine 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; and an abnormality judgment module, used to confirm an abnormal state of the ice-making machine based on a relationship between the first sum of variances and the second sum of variances.

[0013] According to another aspect of the present invention, there is provided an ice making machine, comprising: one or more processors;

[0014] The memory is 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 above-mentioned abnormality detection method for the ice maker.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, including: when the program is executed by a processor, the abnormality detection method of the ice-making machine as described above is implemented.

[0016] The abnormality detection method of the ice maker of the present application obtains a plurality of continuous detection data of preset operating parameters of the ice maker; divides the continuous detection data into a first group and a second group according to the number of the continuous detection data, wherein the first group includes a plurality of first detection data, and the second group includes a plurality of second detection data; performs a first linear fitting on the first detection data using a preset fitting model to obtain the first fitting data corresponding to each first detection data; performs a second linear fitting on the second detection data using a preset fitting model to obtain the second fitting data corresponding to each second detection data; determines the first sum of variances corresponding to the first detection data and the first fitting data, and the second sum of variances corresponding to the second detection data and the second fitting data; confirms the abnormal state of the ice maker according to the relationship between the first sum of variances and the second sum of variances, thereby achieving the function of real-time detection of whether the refrigeration of the ice maker is abnormal, and the implementation is convenient and the cost is low. In addition, since the present application can choose to synchronously monitor two operation-related parameters, the accuracy and reliability of the abnormality judgment result of the ice maker are guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 The present invention is a flowchart of an abnormality detection method for an ice maker provided in the first embodiment of the present invention.

[0019] Figure 2 It is a functional module diagram of an ice-making device provided in the second embodiment of the present invention.

[0020] Figure 3 It is a schematic diagram of the frame structure of an ice maker provided in the third embodiment of the present invention.

[0021] Figure 4 yes Figure 3 Schematic diagram of the control system of the ice machine. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] [Example 1]

[0025] Figure 1is a flow chart of an abnormality detection method for an ice-making machine provided in the first embodiment of the present invention. This embodiment can be applied to an ice-making device for execution. The device can be implemented by software and / or hardware and can generally be integrated into the ice-making machine. Figure 1 As shown, the method includes the following operations:

[0026] S110, obtaining a plurality of continuous detection data of preset operating parameters of the ice maker, wherein the continuous detection data includes a plurality of first detection data of a first group and a plurality of second detection data of a second group.

[0027] In one embodiment, refer to Figure 3 , 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 one, two or more of the exhaust temperature, the evaporation temperature, the compressor power and the condensation temperature. For example, the preset operating parameters may be one, two or more of the exhaust temperature Tp of the exhaust pipe of the compressor 310, the evaporation temperature Zf of the evaporator 340, the compressor power and the condensation temperature Ln of the condenser 320 detected by the sensors 361-364. In this embodiment, there is an operational correlation between the multiple operating parameters, that is, when the refrigeration is abnormal, the change rules between these parameters with operational correlation are similar, and the change amplitudes are different. Specifically, it can be Figure 3 The exhaust pipe of the compressor 310 shown is provided with a sensor 361, the copper pipe outlet of the evaporator 340 is provided with a sensor 362, and the input of the condenser 320 is provided with a sensor 363 or the output node is provided with a sensor 364, which are used 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 working current of the compressor, that is, the compressor power is equal to the product of the working voltage of the compressor and the working current. Generally, the working voltage is a constant value and can be used directly when calculating the compressor power. Since there are small value fluctuations during power supply, a voltmeter can also be provided on the circuit board inside the ice maker to detect the working voltage in real time. The method of obtaining the working voltage is not limited in this scheme. In one embodiment, the obtained continuous detection data is stored in the detection order in a preset storage unit, such as y(1)...y(n).

[0028] In one embodiment, the number of continuous detection data may be 20-60, for example, 30. Preferably, the number of continuous detection data is an even number. In one embodiment, the number of the first group and the second group is the same, 15 respectively. 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).

[0029] In one embodiment, the first group of multiple first detection data are all acquired before the second group of multiple second detection data.

[0030] In one embodiment, when monitoring an operating parameter, such as the evaporation temperature t, refer to the following Table 1, which is the initial detection storage situation:

[0031] Table 1: Initial test data of evaporation temperature obtained and stored

[0032] S120, performing a first linear fit on the first detection data using a preset fitting model to obtain first fitting data corresponding to each first detection data; performing a second linear fit on the second detection data using the preset fitting model to obtain second fitting data corresponding to each second detection data.

[0033] Specifically, in one embodiment, the preset fitting model is a linear model; the linear model can be , where n=1,2,3…j, The first fitting data , where n=j+1,j+2…i, The second fitting data , the serial number of the initial detection data sampling is x(n)=n=1,2,3…j,j+1,j+2…i; where j is the number of the first detection data; ij is the number of the second detection data.

[0034] The parameters b and a are calculated as follows: ; ; where x i Equal to sampling number i, y i is the detection result corresponding to the sampling number i, is the average value of the first detection data or the second detection data, It is the average value of the serial numbers of the first detection data or the average value of the serial numbers of the second detection data.

[0035] In this embodiment, when the first detection data is subjected to the first linear fitting using the preset fitting model, a set of fitting parameters is obtained by separate calculation, for example, parameters b1 and a1 are obtained, wherein the first fitting data The calculation of uses the first set of fitting parameters b1 and a1, for example When performing a second linear fit on the second detection data using the preset fitting model, two sets of fitting parameters are obtained by separate calculation, for example, parameters b2 and a2 are obtained, wherein the second fitting data The calculation of uses the second set of fitting parameters b2 and a2, for example .

[0036] In one embodiment, referring to Table 2 below, the distribution of the first and second fitting data is shown:

[0037] Table 2: Fitting data calculated for the test data of evaporation temperature

[0038] S130, 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.

[0039] Specifically, the calculation method for determining the first sum of variances corresponding to the first detection data and the first fitting data is: ; Determine the calculation method of the second sum of variances corresponding to the second detection data and the second fitting data as follows: ;in, is the first detection data obtained corresponding to the detection sequence number 1 to j, The second detection data obtained corresponding to the detection sequence number j+1 to i. Obtain the first fitting data for the corresponding detection numbers 1 to j, The second fitting data is obtained for the corresponding detection numbers j+1 to i.

[0040] S140: Confirming an abnormal state of the ice maker according to a relationship between the first sum of variances and the second sum of variances.

[0041] In one embodiment, the determination method for confirming the abnormal state of the ice maker based on the relationship between the first sum of variances and the second sum of variances is: ,in is the sensitivity control parameter, when When it is established, it means that the ice machine is abnormal. If it is not true, it means that the current cooling function is running normally. Based on the preset update frequency, for example, 10 seconds, the detection data is updated and further judged. Whether it is established, the method of shifting and updating the detection data is: ; where k=1,2,3…n-1, ,in The latest detection data obtained at the next moment.

[0042] Refer to Table 3 below, which shows the storage and simulation of the test data t31 obtained after the first update test:

[0043] Table 3: Detection data and fitting data of evaporation temperature obtained by the first update test

[0044] Refer to Table 4 below, which shows the storage and simulation of the test data t32 obtained after the second update test:

[0045] Table 4: The test data and fitting data of evaporation temperature obtained by the second update test

[0046] In an alternative embodiment, more than two operating parameters may be judged, and the data storage, parameter calculation and judgment method of each operating parameter may refer to the above embodiment. When two operating parameters, such as evaporation temperature t and compressor power p, are monitored at the same time, the evaporation temperature data may refer to Tables 1-4, and the initial detection storage and simulation of compressor power p may refer to the following Table 5:

[0047] Table 5: Initial test to obtain the compressor power test data and fitting data

[0048]

[0049] in, The location where the compressor power p is stored after each detection, wherein the first detection data of the compressor power is obtained corresponding to the detection sequence number 1 to j (1-15 in the above table) , corresponding to the detection sequence j+1 to i (16-30 in the above table), the second detection data of the compressor power is obtained . is the third fitting data obtained corresponding to the detection numbers 1 to j, The fourth fitting data is obtained for the corresponding detection numbers j+1 to i. Specifically, in this embodiment, when the third linear fitting is performed on the first detection data corresponding to the compressor power using the preset fitting model, a third set of fitting parameters is obtained by separate calculation, for example, parameters b3 and a3 are obtained, wherein the third fitting data The calculation of 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 the preset fitting model, a fourth set of fitting parameters is obtained by separate calculation, for example, parameters b4 and a4 are obtained, wherein the fourth fitting data The calculation of uses the fourth set of fitting parameters b4 and a4, for example According to the variance sum formula, the third sum of the first detection data and the third fitting data corresponding to the compressor power is determined as , and according to the variance sum formula, the fourth variance sum of the second detection data and the fourth fitting data corresponding to the compressor power is determined as .

[0050] In this embodiment, the evaporation temperature t and the compressor power p can be synchronously detected and judged at the same time, and the judgment formula related to the evaporation temperature is: ; The judgment formula based on the compressor power p is: ,in, is the abnormal sensitivity parameter of evaporation temperature, is the abnormal sensitivity parameter of the compressor power. When the above two formulas are both true, it can be judged that the ice maker is working abnormally, otherwise the ice maker is working normally. The detection result can be updated and shifted and stored in the same way as the evaporation temperature at every preset time interval, such as 10-30 seconds, and then re-judged to achieve real-time monitoring of the abnormal operation of the ice maker.

[0051] In one embodiment, the preset operating parameters may be multiple, and the determination method for confirming the abnormal state of the ice maker based on the relationship between the first variance sum and the second variance sum is: when , , …when all of them are true, it means that the ice maker is abnormal. , , …are control parameters for the sensitivity of different preset operating parameters.

[0052] In this embodiment, the method of shifting and updating the detection data is: ; where k=1,2,3…n-1, ,in The latest detection data obtained at the next moment.

[0053] Refer to Table 6 below, which shows the storage and simulation of the test data p31 of the compressor power obtained after the first update test:

[0054] Table 6: The test data and fitting data of the compressor power obtained by the first update test

[0055] Refer to Table 7 below, which shows the storage and simulation of the detection data p32 of the compressor power obtained by the second update detection:

[0056] Table 7: The test data and fitting data of the compressor power obtained by the second update test

[0057] Compared with the prior art, the abnormality detection method of the ice machine of this embodiment divides the detection data into two groups and performs linear fitting respectively. When an abnormality 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, it satisfies the and / or meet In this way, real-time monitoring can be carried out to detect any abnormalities. In addition, since two sets of related operating parameters are monitored synchronously at the same time, false alarms caused by single occasional data abnormalities are avoided, further ensuring the accuracy of ice machine abnormality judgment.

[0058] In an alternative embodiment, when the ice maker is in an abnormal state, the following further steps are performed: defrosting and melting ice, or power off for a preset time to allow natural melting, or notify an abnormality inspection. Specifically, a preset program can be used to automatically perform defrosting and melting ice when an abnormality occurs, or power off for a preset time to allow natural melting, or notify an abnormality inspection. It is also possible to set maintenance information, arrange a corresponding maintenance work order according to the time when the abnormality occurs, and send it to the maintenance personnel's mobile phone.

[0059] In an alternative embodiment, when the number of updates after the judgment of the occurrence of an abnormality does not exceed the number of the second group, if the abnormal judgment continues to appear after each update, it can be determined that the abnormality is a serious abnormality and it is necessary to notify the implementation of an abnormality inspection. If the abnormal judgment occurs occasionally after each update, it can be determined that the abnormality is a temporary abnormality and defrosting and ice-melting operations can be performed first, or the power can be turned off for a preset time for natural melting.

[0060] In an alternative embodiment, the exhaust temperature, evaporation temperature, and compressor power may be synchronously detected and judged simultaneously. The principle is the same as before and will not be repeated.

[0061] [Example 2]

[0062] Figure 2 2 is a schematic diagram of an ice-making device provided in the second embodiment of the present invention. The device 200 can be implemented by software and / or hardware, and can generally be integrated into an ice-making machine such as Figure 2 As shown, the device 200 includes a parameter receiving module 210, a parameter fitting module 220, a deviation calculation module 230 and an abnormality judgment module 240.

[0063] The parameter receiving module 210 is used to obtain a plurality of continuous detection data of preset operating parameters of the ice maker, wherein the continuous detection data includes a plurality of first detection data of a first group and a plurality of second detection data of a second group.

[0064] In one embodiment, refer to Figure 3, 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 one, two or more of the exhaust temperature Tp of the exhaust pipe of the compressor 310, the evaporation temperature Zf of the evaporator 340, the compressor power and the condensation temperature Ln of the condenser 320 detected by the sensors 361-364. In this embodiment, there is an operating correlation between the multiple operating parameters, that is, when the refrigeration is abnormal, the change rules between these parameters with operating correlation are similar, and the change amplitudes are different. Specifically, in Figure 3 The exhaust pipe of the compressor 310 shown is provided with a sensor 361, the copper tube outlet of the evaporator 340 is provided with a sensor 362, and the input node of the condenser 320 is provided with a sensor 363 or the output node is provided with a sensor 364, which are used to sense corresponding operating parameters, and the control system 350 obtains corresponding sensing results from sensors 361-364.

[0065] In one embodiment, the parameter receiving module 210 is used to store the obtained continuous detection data in a preset storage unit, such as y(1)...y(n) in sequence according to the detection order. In one embodiment, when monitoring an operating parameter, such as evaporation temperature t, the storage of the initial detection data refers to Table 1.

[0066] In one embodiment, the first group includes a plurality of first detection data, the second group includes a plurality of second detection data, and the plurality of first detection data of the first group are all acquired before the plurality of second detection data of the second group.

[0067] In one embodiment, the number of continuous detection data may be 20-60, for example, 30. Preferably, the number of continuous detection data is an even number. In one embodiment, the number of the first group and the second group is the same, 15 respectively. 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).

[0068] The parameter fitting module 220 is used to perform a first linear fitting on the first detection data using a preset fitting model to obtain first fitting data corresponding to each first detection data; and to perform a second linear fitting on the second detection data using a preset fitting model to obtain second fitting data corresponding to each second detection data.

[0069] Specifically, in one embodiment, the linear model is , where n=1,2,3…j, The first fitting data , where n=j+1,j+2…i, The second fitting data The serial number of the initial detection data sampling is x(n)=n=1,2,3…j,j+1,j+2…i; j is the number of the first detection data; ij is the number of the second detection data.

[0070] The parameters b and a are calculated as follows: ; ; where x i Equal to sampling number i, y i is the detection result corresponding to the sampling number i, is the average value of the first detection data or the second detection data, It is the average value of the serial numbers of the first detection data or the average value of the serial numbers of the second detection data.

[0071] In this embodiment, when the first detection data is subjected to the first linear fitting using the preset fitting model, a set of fitting parameters is obtained by separate calculation, for example, parameters b1 and a1 are obtained, wherein the first fitting data The calculation of uses the first set of fitting parameters b1 and a1, for example When performing a second linear fit on the second detection data using the preset fitting model, two sets of fitting parameters are obtained by separate calculation, for example, parameters b2 and a2 are obtained, wherein the second fitting data The calculation of uses the second set of fitting parameters b2 and a2, for example In one embodiment, the distribution of the second fitting data is shown in Table 2.

[0072] The deviation calculation module 230 is used to determine 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.

[0073] Specifically, the calculation method for determining the first sum of variances corresponding to the first detection data and the first fitting data is: ; Determine the calculation method of the second sum of variances corresponding to the second detection data and the second fitting data as follows: ;in, is the first detection data obtained corresponding to the detection sequence number 1 to j, The second detection data obtained corresponding to the detection sequence number j+1 to i. Obtain the first fitting data for the corresponding detection numbers 1 to j, The second fitting data is obtained for the corresponding detection numbers j+1 to i.

[0074] The abnormality determination module 240 is used to confirm the abnormal state of the ice maker according to the relationship between the first sum of variances and the second sum of variances.

[0075] In one embodiment, the determination method for confirming the abnormal state of the ice maker based on the relationship between the first sum of variances and the second sum of variances is: , where α is the sensitivity control parameter, when When it is established, it means that the ice machine is abnormal. If it is not true, it means that the current cooling function is running normally. Based on the preset update frequency, for example, 10 seconds, the detection data is updated and further judged. Whether it is established, the method of shifting and updating the detection data is: ; where k=1,2,3…n-1, ,in The latest detection data obtained at the next moment.

[0076] For the storage and simulation after the detection data t31 is obtained by the first update detection, refer to Table 3. For the storage and simulation after the detection data t32 is obtained by the second update detection, refer to Table 4.

[0077] In an alternative embodiment, more than two operating parameters may be judged, and the data storage, parameter calculation and judgment method of each operating parameter may refer to the above embodiment. When two operating parameters, such as evaporation temperature t and compressor power p, are monitored at the same time, the evaporation temperature data may refer to Tables 1-4, and the initial detection storage and simulation of compressor power p may refer to Table 5.

[0078] in, The location where the compressor power p is stored after each detection, where the first detection data of the compressor power is obtained corresponding to the detection sequence number 1 to j (1-15 in the above table). , corresponding to the detection sequence j+1 to i (16-30 in the above table), the second detection data of the compressor power is obtained . Obtain the third fitting data for the corresponding detection numbers 1 to j, The fourth fitting data is obtained for the corresponding detection numbers j+1 to i. Specifically, in this embodiment, when the third linear fitting is performed on the first detection data corresponding to the compressor power using the preset fitting model, three sets of fitting parameters are obtained by separate calculation, for example, parameters b3 and a3 are obtained, wherein the third fitting data The calculation of uses the third set of fitting parameters b3 and a3, for example When the preset fitting model is used to perform a fourth linear fit on the second detection data corresponding to the compressor power, four sets of fitting parameters are obtained by separate calculation, for example, parameters b4 and a4 are obtained, wherein the fourth fitting data The calculation of uses the fourth set of fitting parameters b4 and a4, for example According to the variance sum formula, the third sum of the first detection data and the third fitting data corresponding to the compressor power is determined as According to the variance sum formula, the fourth variance sum of the second detection data and the fourth fitting data corresponding to the compressor power is determined as .

[0079] In this embodiment, the evaporation temperature t and the compressor power p can be synchronously detected and judged at the same time, and the judgment formula related to the evaporation temperature is: ; The judgment formula based on the compressor power p is: ,in, is the abnormal sensitivity parameter of evaporation temperature, is the abnormal sensitivity parameter of the compressor power. When the above two formulas are both true, it can be judged that the ice maker is working abnormally, otherwise the ice maker is working normally. The detection result can be updated and shifted and stored in the same way as the evaporation temperature at every preset time interval, such as 10-30 seconds, and then re-judged to achieve real-time monitoring of the abnormal operation of the ice maker.

[0080] The storage and simulation of the detection data p31 of the compressor power obtained by the first update detection are referred to in Table 6. The storage and simulation of the detection data p32 of the compressor power obtained by the second update detection are referred to in Table 7.

[0081] The abnormality detection device of this embodiment divides the detection data into two groups and performs linear fitting respectively. When an abnormality 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, it satisfies and / or meet In this way, real-time monitoring can be carried out to detect any abnormalities. In addition, since two sets of related operating parameters are monitored synchronously at the same time, false alarms caused by single occasional data abnormalities are avoided, further ensuring the accuracy of ice machine abnormality judgment.

[0082] In one embodiment, the preset operating parameters are multiple, and the determination method for confirming the abnormal state of the ice maker based on the relationship between the first variance sum and the second variance sum is: when , , …when all of them are true, it means that the ice maker is abnormal. , , …are control parameters of the sensitivity of different preset operating parameters. In alternative embodiments, it may further include: an ice-melting operation module for performing defrosting and ice-melting operations after an abnormality occurs; a power-off melting module for performing natural melting after power-off for a preset time after an abnormality occurs; and an abnormality notification module for notifying the implementation of abnormality inspection after an abnormality occurs.

[0083] In an alternative embodiment, when the abnormality detection module updates the number of times after determining that an abnormality occurs and does not exceed the number of the second group, if the abnormality judgment continues to appear after each update, it can be determined that the abnormality is a serious abnormality and it is necessary to notify the implementation of an abnormality inspection; if the abnormality judgment appears occasionally after each update, it can be determined that the abnormality is a temporary abnormality and defrosting and ice-melting operations can be performed first, or the power can be turned off for a preset time for natural melting.

[0084] The above-mentioned ice-making device can execute the abnormality detection method for the ice-making machine provided in any embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For the technical details not described in detail in this embodiment, please refer to the abnormality detection method for the ice-making machine provided in any embodiment of the present invention. Since the ice-making device introduced above is a device that can execute the abnormality detection method for the ice-making machine in the embodiment of the present invention, based on the abnormality detection method for the ice-making machine introduced in the embodiment of the present invention, the technical personnel in this field can understand the specific implementation method of the ice-making device in this embodiment and its various variations, so how the ice-making device implements the abnormality detection method for the ice-making machine in the embodiment of the present invention will not be described in detail here. As long as the device adopted by the technical personnel in this field to implement the abnormality detection method for the ice-making machine in the embodiment of the present invention is within the scope of protection of this application.

[0085] [Example 3]

[0086] Figure 3 A structural schematic diagram of an ice maker provided in Embodiment 3 of the present invention is shown. Figure 4 yes Figure 3 The schematic diagram of the control system of the ice machine. Figure 3As shown, 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 connected to the at least one processor 11 in communication, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the control system 350 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through the bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0087] A number of components in the control system 350 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the control system 350 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0088] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a single-chip microcomputer, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as an abnormality detection method for an ice maker.

[0089] In some embodiments, the abnormality detection method of the ice maker can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a 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 the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the abnormality detection method of the ice maker described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the abnormality detection method of the ice maker in any other appropriate manner (for example, by means of firmware).

[0090] Various implementations 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), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] Computer programs for implementing the methods of the present invention 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0092] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0093] To provide interaction with a user, the systems and techniques described herein may be implemented on a mobile terminal having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the mobile terminal. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0094] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0095] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0096] [Example 4]

[0097] Embodiment 4 of the present invention further provides a computer storage medium storing a computer program, wherein the computer program is used to execute the abnormality detection method for an ice-making machine described in any of the above embodiments of the present invention when executed by a computer processor.

[0098] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM, or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device.

[0099] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0100] The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, radio frequency (RF), etc., or any suitable combination of the foregoing.

[0101] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting abnormality of an ice machine, characterized in that: include: Acquire a plurality of continuous detection data of preset operating parameters of the ice maker, wherein the continuous detection data includes a plurality of first detection data of a first group and a plurality of second detection data of a second group; Performing a first linear fit on the first detection data using a preset fitting model to obtain first fitting data corresponding to each first detection data; Performing a second linear fit on the second detection data using a preset fitting model to obtain second fitting data corresponding to each second detection data; Determine 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; confirming an abnormal state of the ice maker according to a relationship between the first sum of variances and the second sum of variances; Wherein, the preset fitting model is a linear model; the first group of multiple first detection data are all acquired before the second group of multiple second detection data; The linear model is , Among them, when n=1,2,3…j, The first fitting data , Among them, when n=j+1,j+2…i, The second fitting data , The serial number of the initial detection data sampling is x(n)=n=1,2,3…j,j+1,j+2…i; wherein j is the number of the first detection data; ij is the number of the second detection data, and the number of the first group and the second group is the same; The parameters b and a are calculated as follows: ； ； Among them, x i Equal to sampling number i, y i is the detection result corresponding to the sampling number i, is the average value of the first detection data or the second detection data, is the average value of the serial numbers of the first test data or the average value of the serial numbers of the second test data; The calculation method for determining the first sum of variances corresponding to the first detection data and the first fitting data is as follows: ； The calculation method for determining the second sum of variances corresponding to the second detection data and the second fitting data is: ； in, is the first detection data obtained corresponding to the detection sequence number 1 to j, The second detection data obtained for the detection sequence numbers j+1 to i; Wherein, the judgment method for confirming the abnormal state of the ice maker according to the relationship between the first variance sum and the second variance sum is: when When established, it indicates that the ice maker is abnormal, wherein is the control parameter of sensitivity.

2. The abnormality detection method of the ice making machine according to claim 1, characterized in that: when If it is not true, shift and update the test data and make further judgments Whether it is established, the method of shifting and updating the detection data is: , where k=1,2,3…n-1; ,in The latest detection data obtained at the next moment.

3. The abnormality detection method for an ice maker according to claim 1, when the preset operating parameters are two or more, the abnormal state of the ice maker is confirmed in the following manner according to the relationship between the first sum of variances and the second sum of variances of each preset operating parameter: , , …when all of them are true, it means that the ice maker is abnormal. , , …are control parameters for the sensitivity of different preset operating parameters.

4. The abnormality detection method for an ice maker according to any one of claims 1 to 3, wherein the preset operating parameters are one, two or more of the exhaust temperature, evaporation temperature, compressor power and condensation temperature.

5. An ice-making device, used to implement the abnormality detection method of an ice-making machine as claimed in any one of claims 1 to 4, characterized in that: include: A parameter receiving module, used for acquiring a plurality of continuous detection data of preset operating parameters of the ice maker, wherein the continuous detection data includes a plurality of first detection data of a first group and a plurality of second detection data of a second group; A parameter fitting module, used for performing a first linear fitting on the first detection data using a preset fitting model to obtain first fitting data corresponding to each first detection data; Performing a second linear fit on the second detection data using a preset fitting model to obtain second fitting data corresponding to each second detection data; a deviation calculation module, used to determine 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; The abnormality determination module is used to confirm the abnormal state of the ice maker according to the relationship between the first sum of variances and the second sum of variances.

6. An ice making machine, characterized in that: include: one or more processors; A memory for storing 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 abnormality detection method for the ice-making machine as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the abnormality detection method for an ice-making machine as claimed in any one of claims 1 to 4 is implemented.

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