Refrigerator abnormal cooling detection method and device, and refrigerator
By combining cloud server data with refrigerator operation data, the refrigerator compartment, freezer compartment, and variable temperature compartment can be accurately diagnosed, solving the problems of high false alarm rate and delayed diagnosis in refrigerator refrigeration fault diagnosis, and enabling timely detection of abnormalities and reducing the false judgment rate.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-28
- Publication Date
- 2026-06-12
Smart Images
Figure CN122191905A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of refrigerator technology, specifically to a method, device, and refrigerator for detecting refrigeration anomalies. Background Technology
[0002] In complex usage scenarios such as frequent door opening and closing, extreme ambient temperature and humidity, alternating operation of multiple functions, and the decline in product reliability after long-term use, users are prone to refrigeration malfunctions. Specifically, the actual temperature of the refrigerator compartment, freezer compartment, and variable temperature compartment deviates from the set value and remains higher. If such malfunctions are not identified in time, they can cause the food stored by users to spoil and be damaged, resulting in property loss.
[0003] Existing refrigerator fault diagnosis technologies mostly rely on local detection or single sensor threshold judgment, which have problems such as high false alarm rate, easily mistaking normal operations such as defrosting and temperature fluctuations caused by opening and closing the door as faults, and diagnostic delays that mean food is already damaged when users discover the fault.
[0004] Therefore, the technology still needs to be improved and enhanced. Summary of the Invention
[0005] This application provides a method, device, and refrigerator for detecting refrigeration abnormalities in a refrigerator, which can promptly detect abnormal conditions in the refrigerator, effectively improve the accuracy of diagnosing refrigeration failures, and reduce the misjudgment rate.
[0006] This application provides a method for detecting refrigeration anomalies in a refrigerator, the refrigerator comprising multiple compartments, the method comprising: Acquire the refrigerator's operating data, which includes cabinet temperature parameters and equipment operating status parameters; The operational data is uploaded to a cloud server, which has multiple fault diagnosis models that correspond one-to-one with the multiple compartments. Access the cloud server and determine the corresponding failure diagnosis result for each of the compartments based on the operating data and each failure diagnosis model; The abnormal detection results of the refrigerator's cooling are determined based on multiple of the aforementioned adverse diagnostic results.
[0007] In some embodiments, the plurality of compartments includes a refrigeration compartment, and determining the corresponding failure diagnosis result for each compartment based on the operating data and each failure diagnosis model includes: Based on the operational data, determine whether the current cold storage compartment meets the corresponding diagnostic admission criteria; If the refrigerator compartment currently meets the corresponding diagnostic access conditions, then under the corresponding stable cooling conditions, extract the current first temperature of the refrigerator compartment and the temperature data of the refrigerator compartment within a first preset time period from the operating data. The adverse diagnostic results of the refrigerator compartment are determined based on the first temperature and the temperature data.
[0008] In some embodiments, determining the adverse diagnostic result of the refrigerator compartment based on the first temperature and the temperature data includes: Determine whether the first temperature exceeds the first preset temperature range, and determine whether multiple temperature values in the temperature data all exceed the first preset temperature range; If the first temperature exceeds the first preset temperature range, or if multiple temperature values in the temperature data exceed the first preset temperature range, then the refrigerator compartment is diagnosed as having poor cooling; otherwise, the refrigerator compartment is diagnosed as having normal cooling.
[0009] In some embodiments, determining whether the current refrigerator compartment meets the corresponding diagnostic admission criteria based on the operational data includes: The refrigerator's door opening / closing status, defrosting status, compressor frequency, and cooling status of the refrigerator compartment are obtained from the operating data. If the refrigerator doors remain closed for the target duration, defrosting ends, the compressor frequency drops to zero, and the refrigerator compartment starts cooling, then the refrigerator compartment is determined to meet the diagnostic access criteria.
[0010] In some embodiments, the plurality of compartments includes a freezer compartment, and determining the corresponding failure diagnosis result for each compartment based on the operating data and each failure diagnosis model includes: Based on the operational data, determine whether the current freezer compartment meets the corresponding diagnostic admission criteria; If the freezer compartment currently meets the corresponding diagnostic access criteria, then the current functional mode of the freezer compartment is determined based on the operating data. The functional modes include deep freezing mode, quick freezing mode, and normal mode. Determine the corresponding first preset temperature threshold according to the functional mode; If, under the corresponding stable cooling conditions, the second temperature of the freezer compartment is greater than the first preset temperature threshold and the duration reaches the second preset duration, then the freezer compartment is diagnosed as having poor cooling; otherwise, the freezer compartment is diagnosed as having normal cooling.
[0011] In some embodiments, the plurality of compartments includes a variable temperature compartment, and determining the corresponding failure diagnosis result for each compartment based on the operating data and each failure diagnosis model includes: Based on the operational data, determine whether the current variable temperature room meets the corresponding diagnostic access conditions; If the variable temperature chamber currently meets the corresponding diagnostic access conditions, then under the corresponding stable cooling conditions, the third temperature and the set temperature of the variable temperature chamber are extracted from the operating data. Calculate the difference between the third temperature and the set temperature; If the difference exceeds the second preset temperature threshold and lasts for a duration of a third preset duration, the variable temperature chamber is diagnosed as having poor cooling; otherwise, the variable temperature chamber is diagnosed as having normal cooling.
[0012] In some embodiments, the refrigerator cooling anomaly detection method further includes: Access the cloud server and determine whether the user forgot to close the door based on the operational data and the forgotten door diagnosis model; If it is determined that the user forgot to close the door, the refrigerator is controlled to issue a reminder message indicating that the door was not closed.
[0013] In some embodiments, uploading the runtime data to a cloud server includes: If the change in any of the temperature values in the chamber temperature parameters exceeds the fifth preset temperature threshold, the changed temperature value will be uploaded to the cloud server. If any of the device operating status parameters changes, the changed status parameter will be uploaded to the cloud server. If the running data remains unchanged within the seventh preset time period, the running data will be uploaded to the cloud server again.
[0014] This application also provides a refrigeration anomaly detection device for a refrigerator, the refrigerator comprising multiple compartments, the refrigeration anomaly detection device comprising: The acquisition module is used to acquire the operating data of the refrigerator, including the refrigerator body temperature parameters and the equipment operating status parameters; The upload module is used to upload the operating data to the cloud server, and the cloud server has multiple defect diagnosis models that correspond one-to-one with the multiple compartments. The analysis module is used to access the cloud server and determine the corresponding defect diagnosis result for each compartment based on the operating data and each defect diagnosis model; and to determine the abnormal detection result of the refrigerator's cooling based on multiple defect diagnosis results.
[0015] This application also provides a refrigerator, including multiple compartments, for performing the above-described method for detecting refrigeration anomalies.
[0016] The refrigerator cooling anomaly detection method, device, and refrigerator provided in this application monitor the refrigerator's cooling status in real time via a cloud server, which facilitates timely detection of refrigerator anomalies and allows for sending fault alerts to users. Furthermore, the refrigerator's comprehensive multi-dimensional data analysis for cooling anomaly diagnosis, along with the use of targeted fault diagnosis models for different compartments, effectively improves the accuracy of cooling failure diagnosis and reduces the false alarm rate. Attached Figure Description
[0017] The technical solution and other beneficial effects of this application will become apparent from the following detailed description of specific embodiments in conjunction with the accompanying drawings.
[0018] Figure 1 A flowchart of a refrigerator cooling anomaly detection method provided in an embodiment of this application.
[0019] Figure 2 Another flowchart of the refrigerator cooling anomaly detection method provided in the embodiments of this application.
[0020] Figure 3 Another flowchart of the refrigerator cooling anomaly detection method provided in the embodiments of this application.
[0021] Figure 4 Another flowchart of the refrigerator cooling anomaly detection method provided in the embodiments of this application.
[0022] Figure 5 This is a schematic diagram of the structure of the refrigeration anomaly detection device provided in the embodiments of this application. Detailed Implementation
[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0024] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Features thus defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.
[0025] This application provides a method for detecting refrigeration abnormalities in a refrigerator, the refrigerator including multiple compartments such as a refrigerator compartment, a freezer compartment, and a variable temperature compartment. For an example, please refer to [link to example]. Figure 1 , Figure 1A flowchart illustrating a refrigerator cooling anomaly detection method provided in this application embodiment. The cooling anomaly detection method includes the following steps S101-S104: Step S101: Obtain the refrigerator's operating data, including cabinet temperature parameters and equipment operating status parameters; For example, the cabinet temperature parameters include the refrigerator compartment temperature, freezer compartment temperature, variable temperature compartment temperature, and freezer defrosting temperature. Equipment operating status parameters include the set temperature of each compartment, the open / closed status of each door, the on / off status of the refrigerator cooling system, the function mode of the freezer compartment, the defrosting status of the refrigerator, the compressor operating frequency, the status of the refrigerator damper, the status of the freezer fan, and the operating status of the ice removal motor. The refrigerator may also acquire environmental parameters such as ambient temperature and humidity and upload them to a cloud server to determine the corresponding fault diagnosis results for each compartment based on the environmental parameters, operating data, and each fault diagnosis model.
[0026] For example, the opening / closing states of each door include: the open / closed state of the refrigerator door, the open / closed state of the freezer door, and the open / closed state of the variable temperature compartment door; the on / off state of the refrigerator cooling system includes the start / stop state of the refrigerator cooling system; the functional modes of the freezer include quick-freeze mode, deep-freeze mode, and normal mode; the defrost status of the refrigerator indicates the working status of the refrigerator defrost system, including defrosting in progress and defrosting completed; the compressor operating frequency indicates the current operating frequency value of the compressor; the refrigerator air damper status includes the open / closed state of the refrigerator air damper; the freezer fan status includes the running / stopping state and speed of the freezer fan; and the ice removal motor operating status includes the running / stopping state of the ice removal motor.
[0027] Step S102: Upload the running data to the cloud server. The cloud server has multiple defect diagnosis models that correspond one-to-one with multiple compartments. Optionally, uploading operational data to the cloud server includes: if the change in any temperature value among the enclosure temperature parameters exceeds a fifth preset temperature threshold, then the changed temperature value is uploaded to the cloud server; if any status parameter among the device operational status parameters changes, then the changed status parameter is uploaded to the cloud server; if the operational data remains unchanged within a seventh preset time period, then the operational data is uploaded to the cloud server again. The third preset temperature threshold is, for example, 0.1℃; the seventh preset time period is, for example, 30 minutes.
[0028] For example, the refrigerator uploads operational data to the cloud server according to the following rules: For temperature data, an upload is triggered whenever the temperature changes by a preset amount, such as 0.1°C. For status parameters such as the open / closed status of each door, the on / off status of the refrigerator cooling system, the status of the refrigerator damper, the status of the freezer fan, and the operating status of the ice removal motor, an upload is triggered whenever a status change occurs. For the freezer compartment's functional modes, an upload is triggered each time a mode is switched. For the compressor's operating frequency, an upload is triggered whenever the frequency value changes. Additionally, if there are no data changes within a preset time period, such as 30 minutes, a heartbeat packet upload is automatically triggered to ensure a normal communication link between the refrigerator and the cloud. If no data is reported to the cloud server within a preset time period, such as 60 minutes, the refrigerator's cooling anomaly detection process is terminated, and the diagnostic process is retried after data transmission resumes normally.
[0029] Step S103: Access the cloud server and determine the corresponding failure diagnosis result for each room based on the running data and each failure diagnosis model; To address the different cooling needs, operating logic, and fault characteristics of different compartments, the cloud server is equipped with multiple fault diagnosis models, such as those for the refrigerator compartment, freezer compartment, and variable temperature compartment. For example, the cloud server uses the corresponding fault diagnosis model to diagnose cooling malfunctions in the refrigerator compartment, freezer compartment, and variable temperature compartment. This improves the accuracy of cooling fault diagnosis and reduces the false alarm rate.
[0030] Step S104: Determine the abnormal detection results of the refrigerator's cooling based on multiple adverse diagnostic results.
[0031] For example, if there are multiple compartments with poor cooling, the refrigerator is determined to have a cooling malfunction.
[0032] The refrigerator cooling anomaly detection method provided in this application uses a cloud server to monitor the refrigerator's cooling status in real time, which helps to promptly detect abnormalities and send fault alerts to the user. Furthermore, by integrating multi-dimensional data for cooling anomaly diagnosis and using targeted fault diagnosis models for different compartments, the method effectively improves the accuracy of cooling failure diagnosis and reduces the false alarm rate.
[0033] For further details, please refer to Figure 2 , Figure 2 Another flowchart of the refrigerator cooling anomaly detection method provided in this application embodiment. The refrigerator compartment is one of the multiple compartments. A cloud server has a pre-set fault diagnosis model for the refrigerator compartment. The step S103 above, which determines the fault diagnosis result for each compartment based on the operating data and each fault diagnosis model, may include the following steps S201-S203: Step S201: Determine whether the current cold storage compartment meets the corresponding diagnostic admission criteria based on the operating data; If the refrigerator compartment meets the corresponding diagnostic criteria, it means that the refrigerator compartment has entered a stable, initial state, and it is possible to begin diagnosing refrigeration problems and obtain accurate diagnostic results.
[0034] To determine if the current refrigerator compartment meets the diagnostic admission criteria, for example, the refrigerator's door opening / closing status, defrosting status, compressor frequency, and refrigerator damper status can be extracted from operational data. Then, it is determined whether the following conditions are met: First, all refrigerator doors remain closed for a target duration, such as two hours; second, the defrosting status changes to the defrosting-complete state; third, the compressor's operating frequency returns to zero; and fourth, the refrigerator damper is open. If the current refrigerator compartment simultaneously meets all four conditions, then it is determined that the current refrigerator compartment meets the diagnostic admission criteria. The order in which the first, second, third, and fourth conditions are met can be arbitrary.
[0035] Step S202: If the cold storage room currently meets the corresponding diagnostic access conditions, then under the condition that the cold storage room meets the corresponding stable cooling conditions, extract the current first temperature of the cold storage room and the temperature data of the cold storage room within the first preset time period in the operating data. Specifically, regarding how to determine whether the refrigerator compartment meets the corresponding stable cooling conditions, for example, during the process of diagnosing whether the refrigerator compartment is not cooling properly, the defrosting status, door opening and closing status, refrigerator damper status, acquired refrigerator compartment temperature, and data reporting status of the refrigerator can be monitored in real time. If the defrosting status changes from finished to in progress, any door changes its opening and closing status, the refrigerator compartment temperature shows an invalid value, such as less than -60℃ or greater than 60℃, the refrigerator damper status changes to closed, or the data reporting times out, it is considered that the current refrigerator compartment does not meet the corresponding stable cooling conditions, and the diagnosis of poor cooling in the refrigerator compartment is terminated, and the process returns to step S201 above.
[0036] The first preset duration is, for example, two hours, and the temperature data of the refrigerator compartment within the first preset duration is the temperature data of the refrigerator compartment in the past two hours.
[0037] Step S203: Determine the adverse diagnosis result of the cold storage compartment based on the first temperature and temperature data.
[0038] Therefore, determining refrigeration anomalies in the refrigerator compartment based on dual-time temperature readings improves the accuracy of identifying persistent refrigeration problems. For example, determining the malfunction diagnosis of the refrigerator compartment based on the first temperature and temperature data can include: Determine whether the first temperature exceeds the first preset temperature range, and determine whether multiple temperature values in the temperature data all exceed the first preset temperature range; If the first temperature exceeds the first preset temperature range, or if multiple temperature values in the temperature data exceed the first preset temperature range, then the refrigerator compartment is diagnosed as having poor cooling; otherwise, the refrigerator compartment is diagnosed as having normal cooling.
[0039] The first preset temperature range is, for example, 0℃-13℃. The temperature data includes multiple actual temperature values of the refrigerator compartment uploaded by the refrigerator over the past two hours.
[0040] In some embodiments, please refer to Figure 3 , Figure 3 Another flowchart of the refrigerator cooling anomaly detection method provided in this application embodiment. The multiple compartments include a freezer compartment. The cloud server also has a preset fault diagnosis model for the freezer compartment. The step S103 above, which determines the fault diagnosis result for each compartment based on the operating data and each fault diagnosis model, may further include the following steps S301-S304: Step S301: Determine whether the current freezer compartment meets the corresponding diagnostic admission criteria based on the operating data; If the freezer compartment meets the corresponding diagnostic access criteria, it means that the freezer compartment has entered a stable, initial state, and the refrigeration failure diagnosis of the freezer compartment can begin and obtain accurate diagnostic results.
[0041] To determine whether the current freezer compartment meets the diagnostic admission criteria, for example, the refrigerator door opening / closing status, defrosting status, and compressor frequency can be extracted from the operating data. Then, it is determined whether the following conditions are met: First, multiple refrigerator doors remain closed for a duration reaching a target duration, such as two hours; second, the defrosting status changes to the defrosting-complete state; and third, the compressor operating frequency returns to zero. If the current freezer compartment simultaneously meets the first, second, and third conditions, it is determined that the current freezer compartment meets the diagnostic admission criteria. The order of the first, second, and third conditions can be arbitrary. In some embodiments, determining whether the current freezer compartment meets the diagnostic admission criteria further includes: extracting the refrigerator freezer fan status and the ice removal motor operating status from the operating data; then, it is determined whether the following conditions are met: Fourth, the refrigerator freezer fan is on; and fifth, the ice removal motor has stopped operating. If the current freezer compartment simultaneously meets the first, second, third, fourth, and fifth conditions, it is determined that the current freezer compartment meets the diagnostic admission criteria.
[0042] Step S302: If the freezer currently meets the corresponding diagnostic access conditions, determine the current functional mode of the freezer based on the operating data. The functional modes include deep freezing mode, quick freezing mode, and normal mode. It should be noted that in normal mode, both the quick-freeze and deep-freeze functions of the freezer compartment are turned off; in deep-freeze mode, the quick-freeze function of the freezer compartment is turned off; and in quick-freeze mode, the deep-freeze function of the freezer compartment is turned off. Malfunction diagnostic models, for example, determine the current functional mode of the freezer compartment based on the on / off status of the deep-freeze and quick-freeze functions in the operating data.
[0043] Step S303: Determine the corresponding first preset temperature threshold according to the functional mode; For example, if the current function mode of the freezer compartment is deep freezing mode or quick freezing mode, the corresponding first preset temperature threshold is -10℃; if the current function mode of the freezer compartment is normal mode, the corresponding first preset temperature threshold is -5℃.
[0044] Step S304: If, under the corresponding stable cooling conditions, the second temperature of the freezer compartment is greater than the first preset temperature threshold and the duration reaches the second preset duration, then the freezer compartment is diagnosed as having poor cooling; otherwise, the freezer compartment is diagnosed as having normal cooling.
[0045] To determine whether the freezer compartment meets the corresponding stable cooling conditions, for example, during the process of diagnosing whether the freezer compartment is not cooling properly, the defrosting status, door opening and closing status, freezer compartment function mode, acquired freezer compartment temperature, and data reporting status of the refrigerator can be monitored in real time. If the defrosting status changes from defrosting finished to defrosting in progress, any door opening and closing status changes, the freezer compartment temperature shows an invalid value, such as less than -60℃ or greater than 60℃, the freezer compartment function mode is switched, or the data reporting times out, then it is considered that the current freezer compartment does not meet the corresponding stable cooling conditions, and the diagnosis of poor cooling of the freezer compartment is terminated, and the process returns to step S301 above.
[0046] The second preset duration is, for example, 2 hours. If the actual temperature of the freezer compartment continues to exceed the first preset temperature threshold within 2 hours, the freezer compartment is deemed to be malfunctioning.
[0047] In some embodiments, please refer to Figure 4 , Figure 4 Another flowchart of the refrigerator cooling anomaly detection method provided in this application embodiment. The multiple compartments include a variable temperature compartment. The cloud server also has a preset fault diagnosis model for the variable temperature compartment. The step S103 above, which determines the fault diagnosis result for each compartment based on the operating data and each fault diagnosis model, may further include the following steps S401-S404: Step S401: Determine whether the current variable temperature room meets the corresponding diagnostic access conditions based on the operating data; To determine whether the current variable temperature compartment meets the diagnostic criteria, for example, the following steps can be taken: extract the refrigerator's door opening / closing status, the set temperature of the variable temperature compartment, the defrosting status, and the compressor frequency from the operating data; then, determine whether the following conditions are met: First, all refrigerator doors remain closed for a target duration, such as two hours; second, the refrigerator's defrosting status changes to the defrosting-complete state; third, the compressor's operating frequency returns to zero; and fourth, the set temperature of the variable temperature compartment is less than 0°C. If the current variable temperature compartment simultaneously meets the first, second, third, and fourth conditions, then it is determined that the current variable temperature compartment meets the diagnostic criteria. The order in which the first, second, third, and fourth conditions are met can be arbitrary.
[0048] Step S402: If the variable temperature chamber currently meets the corresponding diagnostic access conditions, then under the corresponding stable cooling conditions, extract the third temperature and set temperature of the current variable temperature chamber from the operating data. To determine whether the variable temperature compartment meets the corresponding stable cooling conditions, for example, during the process of diagnosing whether the variable temperature compartment is malfunctioning, the set temperature of the variable temperature compartment, the defrosting status of the refrigerator, the opening and closing status of the door, the set setting of the variable temperature compartment, the actual temperature of the variable temperature compartment, and the data reporting status can be monitored in real time. If the set temperature of the variable temperature compartment is greater than 0℃, the defrosting status changes from defrosting ended to defrosting in progress, any door changes its opening and closing status, the temperature of the variable temperature compartment shows an invalid value, such as less than -60℃ or greater than 60℃, the set setting of the variable temperature compartment is switched, or the data reporting times out, it is considered that the current variable temperature compartment does not meet the corresponding stable cooling conditions, and the diagnosis of malfunctioning cooling of the variable temperature compartment is terminated, and the process returns to step S301 above.
[0049] Step S403: Calculate the difference between the third temperature and the set temperature; Step S404: If the difference exceeds the second preset temperature threshold and the duration reaches the third preset duration, the variable temperature chamber is diagnosed as having poor cooling; otherwise, the variable temperature chamber is diagnosed as having normal cooling.
[0050] The second preset temperature threshold is, for example, 6℃. The third preset duration is, for example, 2 hours. If the fault diagnosis model continuously determines that the temperature difference between the actual temperature and the set temperature of the variable temperature chamber is greater than 6℃ within two hours, then the variable temperature chamber is deemed to be malfunctioning.
[0051] In some embodiments, to eliminate the possibility of excessively high refrigerator temperature due to the user leaving the door open and to prevent misjudgment as an abnormality in the refrigerator's cooling performance, a diagnostic model for cases where the user forgets to close the door can also be set up. The refrigerator cooling abnormality detection method also includes: Access the cloud server and determine whether the user forgot to close the door based on the runtime data and the forgotten door diagnosis model; If it is determined that the user forgot to close the door, the refrigerator will send a reminder message indicating that the door was not closed.
[0052] Optionally, determining whether a user forgot to close the door based on operational data and a door-forgot-to-close diagnostic model includes: obtaining the refrigerator compartment temperature, freezer compartment temperature, and door open / close status from the operational data; If the refrigerator compartment temperature is higher than the third preset temperature threshold and remains higher than the fourth preset time, or the freezer compartment temperature is higher than the fourth preset temperature threshold and remains higher than the fourth preset time, and the refrigerator door is continuously open for more than the fifth preset time or the cumulative door opening time exceeds the sixth preset time, then it is determined that the user forgot to close the door.
[0053] The notification for forgetting to close the door can be delivered via sound, light, display screen, mobile app, or voice. For example, the refrigerator can beep and the indicator light can flash to remind the user to close the door. Alternatively, a notification can be pushed to the user's mobile app to remind them to close the door.
[0054] The refrigerator cooling anomaly detection method provided in this application uses a cloud server to monitor the refrigerator's cooling status in real time, which helps to promptly detect abnormalities and send fault alerts to the user. Furthermore, by integrating multi-dimensional data for cooling anomaly diagnosis and using targeted fault diagnosis models for different compartments, the method effectively improves the accuracy of cooling failure diagnosis and reduces the false alarm rate.
[0055] This application also provides a refrigerator cooling anomaly detection device, for example, please refer to [link to example]. Figure 5 , Figure 5 This is a schematic diagram of the structure of the refrigeration anomaly detection device provided in an embodiment of this application. The refrigeration anomaly detection device 500 includes an acquisition module 510, an upload module 520, and an analysis module 530.
[0056] The acquisition module 510 is used to acquire the refrigerator's operating data, including environmental parameters, cabinet temperature parameters, equipment status parameters, and operating parameters of core components; the upload module 520 is used to upload the operating data to a cloud server, which has multiple fault diagnosis models that correspond one-to-one with each compartment; the analysis module 530 is used to access the cloud server and determine the fault diagnosis result for each compartment based on the operating data and each fault diagnosis model; and determine the abnormal detection result of the refrigerator's cooling based on the multiple fault diagnosis results.
[0057] The refrigerator cooling anomaly detection device provided in this application embodiment monitors the refrigerator's cooling status in real time via a cloud server, which facilitates timely detection of refrigerator anomalies and sends fault alerts to the user. Furthermore, by integrating multi-dimensional data for cooling anomaly diagnosis and using targeted fault diagnosis models for different compartments, the device effectively improves the accuracy of cooling fault diagnosis and reduces the false alarm rate.
[0058] This application also provides a refrigerator, which includes multiple compartments and a controller for executing the refrigeration anomaly detection method of the refrigerator in any of the above embodiments. The refrigerator also includes various sensors for collecting operational data, such as: The refrigerator compartment temperature sensor, deployed inside the refrigerator compartment, is used to collect the real-time temperature of the refrigerator compartment and can be an NTC thermistor; the freezer compartment temperature sensor, deployed inside the freezer compartment, is used to collect the real-time temperature of the freezer compartment and can be an NTC thermistor; the variable temperature compartment temperature sensor, deployed inside the variable temperature compartment, is used to collect the real-time temperature of the variable temperature compartment and can also be an NTC thermistor; the ambient temperature sensor is used to sense the real-time temperature of the external environment of the refrigerator and can also be an NTC thermistor; the ambient humidity sensor is used to collect the real-time humidity of the external environment of the refrigerator and can also be an NTC thermistor; the defrost temperature sensor, deployed at the evaporator, is used to collect the real-time temperature of the evaporator during the defrost process and can also be an NTC thermistor; multiple door switch sensors are used to detect the open and closed status of multiple doors and can also be Hall effect sensors; and a fault timer is used to detect the duration of the abnormal temperature in the refrigerator compartment and to determine whether the fault is persistent.
[0059] The refrigerator provided in this application embodiment monitors its cooling status in real time via a cloud server, which facilitates the timely detection of refrigerator anomalies and allows for the sending of fault alerts to users. Furthermore, the refrigerator's comprehensive multi-dimensional data analysis for diagnosing cooling anomalies, along with the application of targeted fault diagnosis models for different compartments, effectively improves the accuracy of diagnosing cooling malfunctions and reduces the false alarm rate.
[0060] This application also provides a storage medium storing a computer program that, when executed, performs the aforementioned refrigerator cooling anomaly detection method. Integrated modules / units, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various refrigerator cooling anomaly detection method embodiments described above.
[0061] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.
[0062] The above provides a detailed description of the refrigerator cooling anomaly detection method, device, refrigerator, and storage medium provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the technical solutions and core ideas of this application. Those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.
Claims
1. A method for detecting refrigeration abnormalities in a refrigerator, characterized in that, The refrigerator includes multiple compartments, and the method for detecting refrigeration anomalies includes: Acquire the refrigerator's operating data, which includes cabinet temperature parameters and equipment operating status parameters; The operational data is uploaded to a cloud server, which has multiple fault diagnosis models that correspond one-to-one with the multiple compartments. Access the cloud server and determine the corresponding failure diagnosis result for each of the compartments based on the operating data and each failure diagnosis model; The abnormal detection results of the refrigerator's cooling are determined based on multiple of the aforementioned adverse diagnostic results.
2. The method for detecting refrigeration abnormalities in a refrigerator according to claim 1, characterized in that, The plurality of compartments includes a refrigerated compartment, and the determination of the corresponding defect diagnosis result for each compartment based on the operating data and each defect diagnosis model includes: Based on the operational data, determine whether the current cold storage compartment meets the corresponding diagnostic admission criteria; If the refrigerator compartment currently meets the corresponding diagnostic access conditions, then under the corresponding stable cooling conditions, extract the current first temperature of the refrigerator compartment and the temperature data of the refrigerator compartment within a first preset time period from the operating data. The adverse diagnostic results of the refrigerator compartment are determined based on the first temperature and the temperature data.
3. The method for detecting refrigeration abnormalities in a refrigerator according to claim 2, characterized in that, The step of determining the adverse diagnostic result of the refrigerator compartment based on the first temperature and the temperature data includes: Determine whether the first temperature exceeds the first preset temperature range, and determine whether multiple temperature values in the temperature data all exceed the first preset temperature range; If the first temperature exceeds the first preset temperature range, or if multiple temperature values in the temperature data exceed the first preset temperature range, then the refrigerator compartment is diagnosed as having poor cooling; otherwise, the refrigerator compartment is diagnosed as having normal cooling.
4. The method for detecting refrigeration abnormalities in a refrigerator according to claim 2, characterized in that, The step of determining whether the current cold storage room meets the corresponding diagnostic admission criteria based on the operational data includes: The refrigerator's door opening / closing status, defrosting status, compressor frequency, and cooling status of the refrigerator compartment are obtained from the operating data. If the refrigerator doors remain closed for the target duration, defrosting ends, the compressor frequency drops to zero, and the refrigerator compartment starts cooling, then the refrigerator compartment is determined to meet the diagnostic access criteria.
5. The method for detecting refrigeration abnormalities in a refrigerator according to any one of claims 1-4, characterized in that, The plurality of compartments includes a freezer compartment, and the determination of the corresponding defect diagnosis result for each compartment based on the operating data and each defect diagnosis model includes: Based on the operational data, determine whether the current freezer compartment meets the corresponding diagnostic admission criteria; If the freezer compartment currently meets the corresponding diagnostic access criteria, then the current functional mode of the freezer compartment is determined based on the operating data. The functional modes include deep freezing mode, quick freezing mode, and normal mode. Determine the corresponding first preset temperature threshold according to the functional mode; If, under the corresponding stable cooling conditions, the second temperature of the freezer compartment is greater than the first preset temperature threshold and the duration reaches the second preset duration, then the freezer compartment is diagnosed as having poor cooling; otherwise, the freezer compartment is diagnosed as having normal cooling.
6. The method for detecting refrigeration abnormalities in a refrigerator according to any one of claims 1-4, characterized in that, The plurality of compartments includes a variable temperature compartment, and the determination of the corresponding defect diagnosis result for each compartment based on the operating data and each defect diagnosis model includes: Based on the operational data, determine whether the current variable temperature room meets the corresponding diagnostic access conditions; If the variable temperature chamber currently meets the corresponding diagnostic access conditions, then under the corresponding stable cooling conditions, the third temperature and the set temperature of the variable temperature chamber are extracted from the operating data. Calculate the difference between the third temperature and the set temperature; If the difference exceeds the second preset temperature threshold and lasts for a duration of a third preset duration, the variable temperature chamber is diagnosed as having poor cooling; otherwise, the variable temperature chamber is diagnosed as having normal cooling.
7. The method for detecting refrigeration abnormalities in a refrigerator according to any one of claims 1-4, characterized in that, Also includes: Access the cloud server and determine whether the user forgot to close the door based on the operational data and the forgotten door diagnosis model; If it is determined that the user forgot to close the door, the refrigerator is controlled to issue a reminder message indicating that the door was not closed.
8. The method for detecting refrigeration abnormalities in a refrigerator according to any one of claims 1-4, characterized in that, Uploading the operational data to the cloud server includes: If the change in any of the temperature values in the chamber temperature parameters exceeds the fifth preset temperature threshold, the changed temperature value will be uploaded to the cloud server. If any of the device operating status parameters changes, the changed status parameter will be uploaded to the cloud server. If the running data remains unchanged within the seventh preset time period, the running data will be uploaded to the cloud server again.
9. A refrigerator cooling anomaly detection device, characterized in that, The refrigerator includes multiple compartments, and the refrigeration anomaly detection device includes: The acquisition module is used to acquire the operating data of the refrigerator, including the refrigerator body temperature parameters and the equipment operating status parameters; The upload module is used to upload the operating data to the cloud server, and the cloud server has multiple defect diagnosis models that correspond one-to-one with the multiple compartments. The analysis module is used to access the cloud server and determine the corresponding defect diagnosis result for each compartment based on the operating data and each defect diagnosis model; and to determine the abnormal detection result of the refrigerator's cooling based on multiple defect diagnosis results.
10. A refrigerator, characterized in that, It includes multiple compartments and is used to perform the refrigeration anomaly detection method as described in any one of claims 1-8.