Fault monitoring method and device, electronic equipment and storage medium
The operating data of clothing processing equipment components is obtained and analyzed through cloud platform servers, which solves the problem of difficult component failure positioning, realizes rapid repair and fault prediction, and improves maintenance efficiency and user experience.
Patent Information
- Application Number
- CN202410101878.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-23
- Publication Date
- 2025-07-25
AI Technical Summary
In the prior art, the components of the clothing processing equipment cannot be quickly positioned when the components of the clothing processing equipment fail, resulting in low maintenance efficiency and affecting the user experience.
The cloud platform server obtains the current operating data of multiple components inside the clothing processing equipment, uses the historical operating data information to determine whether the current operating data is abnormal, predicts the failure time, and sends component information and failure time to the maintenance personnel.
Quickly locate faulty components inside clothing processing equipment, improve maintenance efficiency, predict failure time in advance, avoid failure occurrence, and improve user experience.
Smart Images
Figure CN120367001A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to Internet of Things technology, and in particular to a fault monitoring method, device, electronic device and storage medium. Background Art
[0002] During the daily process of processing clothes by a clothes processing device, once a fault occurs, it may be either a fault in the operating program of the clothes processing device or a fault in the components inside the clothes processing device, resulting in a fault in the clothes processing device.
[0003] In the existing technology, only the energy consumption during the operation of the clothes processing device is monitored, and it is determined whether there is a fault in the clothes processing device based on the monitored energy consumption during the operation of the clothes processing device. However, when a fault in a component causes a fault in the operation of the clothes processing device, it is impossible to quickly locate the faulty component inside the clothes processing device, resulting in a low repair efficiency and affecting the user experience. Summary of the Invention
[0004] The present application provides a fault monitoring method, device, electronic device and storage medium to solve the problem in the existing technology that when a fault in a component causes a fault in the operation of the clothes processing device, it is impossible to quickly locate the faulty component inside the clothes processing device, resulting in a low repair efficiency and affecting the user experience.
[0005] In a first aspect, the present application provides a fault monitoring method applied to a cloud platform server, and the method includes:
[0006] Obtain the current operation data of multiple components inside the current clothes processing device;
[0007] For each of the components, determine whether the current operation data of the component is abnormal operation data according to the historical operation data information corresponding to the component; wherein, the historical operation data is the operation data of historical components stored in the cloud platform server, and the historical components are components with the same device attributes as the component;
[0008] If the current operation data is abnormal operation data, determine the predicted fault time of the target component corresponding to the current operation data according to the data information of the current operation data, and send the component information of the target component and the predicted fault time to the maintenance personnel.
[0009] In a second aspect, the present application further provides a fault monitoring device configured in a cloud platform server, including:
[0010] An obtaining module, configured to obtain the current operation data of multiple components inside the current clothes processing device;
[0011] A determination module, configured to determine, for each of the components, whether the current operating data of the component is abnormal operating data according to the historical operating data information corresponding to the component; wherein the historical operating data is the operating data of historical components stored in the cloud platform server, and the historical components are components having the same device attributes as the component.
[0012] A prediction module, configured to, if the current operating data is abnormal operating data, determine the predicted failure time of the target component corresponding to the current operating data according to the data information of the current operating data, and send the component information of the target component and the predicted failure time to the maintenance personnel.
[0013] In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the fault monitoring method described in any embodiment of the present application is implemented.
[0014] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the fault monitoring method described in any one of the present application is implemented.
[0015] The solution of the present application is applied to a cloud platform server, which obtains the current operating data of multiple components inside the current laundry processing device; for each component, it determines whether the current operating data of the component is abnormal operating data according to the historical operating data information corresponding to the component; wherein the historical operating data is the operating data of historical components stored in the cloud platform server, and the historical components are components having the same device attributes as the component; if the current operating data is abnormal operating data, it determines the predicted failure time of the target component corresponding to the current operating data according to the data information of the current operating data, and sends the component information of the target component and the predicted failure time to the maintenance personnel. That is, on the one hand, the solution of the present application determines whether the current operating data of the component is abnormal operating data according to the historical operating data information corresponding to the component, so as to determine whether the operation of the component is abnormal, quickly locate the components that may fail inside the laundry processing device, and thus improve the maintenance efficiency. On the other hand, it determines the predicted failure time of the target component corresponding to the current operating data according to the data information of the current operating data, can predict in advance the time when the target component fails, and process the target component in advance to avoid the occurrence of the failure, effectively solving the situation of failures during operation and improving the user experience. Description of the Drawings
[0016] To more clearly illustrate the technical solution of the present application, the accompanying drawings required in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as a limitation of the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.
[0017] Figure 1 is a schematic flowchart of a fault monitoring method provided by the present application;
[0018] Figure 2 is another schematic flowchart of a fault monitoring method provided by the present application;
[0019] Figure 3 is a schematic structural diagram of a fault monitoring device provided by the present application;
[0020] Figure 4 is a schematic structural diagram of an electronic device provided by the present application. Detailed Embodiments
[0021] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application. Additionally, it should be noted that for the sake of description, only parts related to the present application rather than all structures are shown in the drawings.
[0022] Figure 1 is a schematic flowchart of a fault monitoring method provided by the present application. This method can be executed by the fault monitoring device provided by the present application, and the device can be implemented in software and / or hardware. In a specific embodiment, the device can be integrated into an electronic device. For example, the electronic device can be a computer. The following embodiments will be described by taking the cloud platform server in which the device is integrated into the electronic device as an example. Refer to Figure 1 , the method can specifically include the following steps:
[0023] Step 101, obtain the current operation data of multiple components inside the current laundry processing device.
[0024] Specifically, the current laundry treatment device is a device capable of treating laundry, such as a washing machine, a dryer, etc. In the current laundry treatment device, there are multiple components. For example, there are multiple components in a washing machine. When the components operate, they generate multiple operation data, such as voltage, current, power consumption, power, etc. In one possible implementation, multiple component data detection modules are installed inside the current laundry treatment device, and the cloud platform server obtains the current operation data detected by the multiple component data detection modules as the current operation data of multiple components inside the current laundry treatment device. In another possible implementation, the current laundry treatment device sends the current operation data of multiple components to the cloud platform server, and the cloud platform server receives the current operation data of multiple components inside the current laundry treatment device.
[0025] Exemplarily, multiple component data detection modules are installed inside the current laundry treatment device, and the current operation data of each component will be detected by the data detection module. The cloud platform server obtains the current operation data of multiple components detected by the multiple component data detection modules.
[0026] Step 102, for each component, determine whether the current operation data of the component is abnormal operation data according to the historical operation data information corresponding to the component.
[0027] Among them, the historical operation data is the operation data of historical components stored in the cloud platform server, and the historical components are components with the same device attributes as the component.
[0028] Specifically, each component has a historical component with the same device attribute as the component. Among the data stored in the cloud platform server, the historical operation data corresponding to the historical component is the historical operation data corresponding to the component. The historical operation data information can also be multiple data information, such as data information on voltage, current, power consumption, power, etc. The abnormal operation data is the operation data corresponding to when the component operates abnormally. Determining whether the current operation data of the component is abnormal operation data according to the historical operation data information corresponding to the component can be to compare the data when the component operates abnormally in the historical operation data information with the current operation data to determine whether the current operation data is abnormal operation data. It can also be to compare the data when the component operates normally in the historical operation data information with the current operation data to determine whether the current operation data is normal operation data.
[0029] Exemplarily, component B is a component with the same device attribute as component A. The historical operation data information corresponding to component A is that the current value when historical component B operates normally is between 2A and 5A, and the current operation data of component A is that the current value is 8A. Therefore, it is determined that the current operation data of the component is abnormal operation data.
[0030] Optionally, before determining whether the current operating data of the component is abnormal operating data according to the historical operating data information corresponding to the component, steps 21 to 22 may be executed to determine the historical operating data information corresponding to the component.
[0031] Step 21: Obtain the operating program corresponding to the current laundry treatment device.
[0032] Specifically, the laundry treatment device corresponds to an operating program. The operating programs corresponding to different laundry treatment devices may be the same or different. Obtain the operating program corresponding to the current laundry treatment device.
[0033] Step 22: Determine the historical operating data information corresponding to the component according to the operating program corresponding to the current laundry treatment device and the historical operating program corresponding to the historical operating data information stored in the cloud platform server.
[0034] Specifically, after obtaining the operating program corresponding to the current laundry treatment device, compare the historical operating program corresponding to the historical operating data information stored in the cloud platform server with the operating program corresponding to the current laundry treatment device. When the historical operating program corresponding to the historical operating data information is the same as the operating program corresponding to the current laundry treatment device, determine that the historical operating data information is the historical operating data information corresponding to the component. Determining the historical operating data information corresponding to the component according to the operating program corresponding to the current laundry treatment device and the historical operating program corresponding to the historical operating data information stored in the cloud platform server can increase the accuracy of determining the historical operating data information.
[0035] Step 103: If the current operating data is abnormal operating data, determine the predicted failure time of the target component corresponding to the current operating data according to the data information of the current operating data, and send the component information and the predicted failure time of the target component to the maintenance personnel.
[0036] Specifically, the data information of the current operating data is the data value of the current operating data and the relevant information of the current operating data. For example, if the current operating data is the current of a component, the data information of the current operating data may include the current value of the component, the current change trend of the component, the operating duration corresponding to the current value of the component, the number of operating times corresponding to the current value of the component, etc. When the current operating data is abnormal operating data, the predicted failure time of the target component corresponding to the current operating data is determined according to the data information of the current operating data. For example, the data information of the current operating data includes the change trend of the current operating data, and the predicted failure time of the target component corresponding to the current operating data is determined according to the change trend of the current operating data. After obtaining the predicted failure time of the target component, the component information and the predicted failure time of the target component are sent to the maintenance personnel. The component information of the target component is the information that can identify the target component, such as the number of the target component, etc., which is convenient for the maintenance personnel to repair the target component. The component information and the predicted failure time of the target component can also be sent to the user terminal corresponding to the current laundry treatment device to inform the user of the possible failure situation in advance, avoid the occurrence of failures when the user uses the laundry treatment device, and improve the user experience. The component information and the predicted failure time of the target component can also be sent to the current laundry treatment device, and the current laundry treatment device displays the component information and the predicted failure time of the target component on the device display, which is convenient for the user or the maintenance personnel to understand.
[0037] Exemplarily, the data information of the current operating data is that the change trend of the current of component A increases with time. According to this change trend, it is determined that the predicted failure time of component A is ten minutes later. After obtaining the predicted failure time of component A, the number of component A and the predicted failure time are sent to the maintenance personnel, which is convenient for the maintenance personnel to repair component A.
[0038] Before sending the component information and the predicted failure time of the target component to the maintenance personnel, the time interval between the predicted failure time and the current moment can also be determined. When the time interval between the predicted failure time and the current moment is less than or equal to the preset time interval, the component information and the predicted failure time of the target component are sent to the maintenance personnel.
[0039] Exemplarily, the time interval between the predicted failure time and the current moment is 1 day, and the preset time interval is 10 days. Then, the component information and the predicted failure time of the target component are sent to the maintenance personnel.
[0040] Optionally, the component information of the target component includes at least one of the following: the name of the target component, the failure cause of the target component, and the repair plan corresponding to the failure cause of the target component.
[0041] Exemplarily, the name of the target component, the cause of the failure of the target component, and the repair plan corresponding to the cause of the failure of the target component are all sent to the maintenance personnel, facilitating the maintenance personnel to repair the target component.
[0042] Optionally, the data information of the current operating data includes the operating duration of the current operating data. Determining the predicted failure time of the target component corresponding to the current operating data can be achieved through steps 31 to 33.
[0043] Step 31: Obtain the historical data information corresponding to the current operating data, and determine the failure generation duration corresponding to the historical data information.
[0044] Among them, the historical data information corresponding to the current operating data is the data information with the same attributes as the current operating data in the historical operating data information.
[0045] Specifically, the historical operating data information corresponding to the component may include data information with multiple different data attributes, such as the current, voltage, power, etc. corresponding to the component. The historical data information corresponding to the current operating data is the data information with the same attributes as the current operating data in the historical operating data information. For example, if the current operating data is current, the historical data information corresponding to the current operating data is the historical current data information in the historical operating data information. In the historical operating data information stored in the cloud platform server, obtain the historical data information corresponding to the current operating data, and then determine the failure generation duration corresponding to the historical data information, that is, determine the duration from the occurrence of the historical data in the historical data information to the failure of the historical component. For example, the historical data information is that the current value of component B is 8A, and the corresponding failure generation duration is 10s, that is, the duration from the current value of component B being 8A to the failure of component B is 10s.
[0046] Exemplarily, the current operating data is that the current value of component A is 8A, the historical data information corresponding to component A is that the current value of component B is 8A, and the failure generation duration corresponding to this historical data information is 10s.
[0047] Step 32: Obtain the duration difference based on the operating duration of the current operating data and the failure generation duration.
[0048] Specifically, after obtaining the failure generation duration and the operating duration of the current operating data, calculate the duration difference between the failure generation duration and the operating duration of the current operating data.
[0049] Exemplarily, the operating duration of the current value of component A being 8A is 3s, the failure generation duration is 10s, and the obtained duration difference is 7s.
[0050] Step 33: Determine the predicted failure time of the target component corresponding to the current operation data according to the current time and the time difference.
[0051] Specifically, after obtaining the time difference, add the current time to the time difference to determine the predicted failure time of the target component corresponding to the current operation data.
[0052] Exemplarily, if the current time is 18:00:00 and the time difference is 7s, then the predicted failure time of the target component corresponding to the current operation data is determined to be 18:00:07.
[0053] Optionally, the data information of the current operation data includes the number of operation times of the current operation data. Determining the predicted failure time of the target component corresponding to the current operation data according to the data information of the current operation data can be achieved through Steps 34 to 36.
[0054] Step 34: Obtain the historical data information corresponding to the current operation data and determine the number of abnormal times corresponding to the historical data information.
[0055] Among them, the historical data information corresponding to the current operation data is the data information with the same attributes as the current operation data in the historical operation data information.
[0056] Specifically, the historical operation data information corresponding to the component may include data information with multiple different data attributes, such as the current, voltage, power, etc. of the component. The historical data information corresponding to the current operation data is the data information with the same attributes as the current operation data in the historical operation data information. For example, if the current operation data is current, then the historical data information corresponding to the current operation data is the historical current data information in the historical operation data information. In the historical operation data information stored in the cloud platform server, obtain the historical data information corresponding to the current operation data, and then determine the number of abnormal times corresponding to the historical data information, that is, determine the number of abnormal times from the first occurrence of abnormality in the historical data to the failure of the historical component. For example, the historical data information is that the current value of component B is 8A, and the corresponding number of abnormal times is 5 times, that is, when the number of times the current value of component B is 8A reaches 5 times, component B fails.
[0057] Exemplarily, the current operation data is that the current value of component A is 8A, and the historical data information corresponding to component A is that the current value of component B is 8A, and the number of abnormal times corresponding to this historical data information is 5 times.
[0058] Step 35: Obtain the difference in the number of times according to the number of operation times of the current operation data and the number of abnormal times.
[0059] Specifically, after obtaining the number of abnormal times and the number of operation times of the current operation data, calculate the difference in the number of abnormal times and the number of operation times of the current operation data.
[0060] Exemplarily, the number of times the current value of component A is 8A is 2 times, the number of abnormal times is 5 times, and the difference in the number of times is 3 times.
[0061] Step 36: Determine the predicted failure time of the target component corresponding to the current operating data according to the current time, the difference in the number of times, and the abnormal operating duration corresponding to each occurrence of an abnormality in the historical data information.
[0062] Specifically, in a possible implementation manner, after obtaining the difference in the number of times, among the differences in the number of times of the historical data information and the current operating data, the abnormal operating duration corresponding to each occurrence of an abnormality is the same. Exemplarily, the occurrence duration when the current value of component B is 8A is 5s each time. Multiply the difference in the number of times of the historical data information by the abnormal operating duration and then add the result to the current time to determine the predicted failure time of the target component corresponding to the current operating data.
[0063] Exemplarily, the number of times the current value of component A is 8A is 2 times, the number of abnormal times is 5 times, the current time is 18:00:00, and the abnormal operating duration when the current value of component B is 8A in the historical data information is 5s. Therefore, the predicted failure time of the target component corresponding to the current operating data is determined to be 18:00:15.
[0064] In another possible implementation manner, after obtaining the difference in the number of times, among the abnormal times corresponding to the difference in the number of times of the historical data information and the current operating data, the abnormal operating duration corresponding to each occurrence of an abnormality is different. Exemplarily, the duration of the third occurrence when the current value of component B is 8A is 5s, the duration of the fourth occurrence is 6s, and the duration of the fifth occurrence is 8s. Add the abnormal operating duration corresponding to each occurrence of an abnormality among the abnormal times corresponding to the difference in the number of times of the historical data information to the current time, and then add the result to the current time to determine the predicted failure time of the target component corresponding to the current operating data.
[0065] Exemplarily, the number of times the current value of component A is 8A is 2 times, the number of abnormal times is 5 times, the current time is 18:00:00, the duration of the third occurrence when the current value of component B is 8A in the historical data information is 5s, the duration of the fourth occurrence is 6s, and the duration of the fifth occurrence is 8s. Therefore, the predicted failure time of the target component corresponding to the current operating data is determined to be 18:00:19.
[0066] The solution of this application is applied to a cloud platform server to obtain the current operation data of multiple components inside the current clothing processing device. For each component, determine whether the current operation data of the component is abnormal operation data according to the historical operation data information corresponding to the component. Among them, the historical operation data is the operation data of historical components stored in the cloud platform server, and the historical components are components with the same device attributes as the component. If the current operation data is abnormal operation data, determine the predicted failure time of the target component corresponding to the current operation data according to the data information of the current operation data, and send the component information and the predicted failure time of the target component to the maintenance personnel. That is, on the one hand, according to the historical operation data information corresponding to the component, determine whether the current operation data of the component is abnormal operation data, so as to determine whether the operation of the component is abnormal, quickly locate the components that may be faulty inside the clothing processing device, and thus improve the maintenance efficiency. On the other hand, determine the predicted failure time of the target component corresponding to the current operation data according to the data information of the current operation data, which can predict in advance the time when the target component fails, and process the target component in advance to avoid the occurrence of failures, effectively solve the situation of failures during operation, and improve the user experience.
[0067] Figure 2 It is another process schematic diagram of the fault monitoring method provided by this application. In this embodiment, based on the embodiment shown and various optional implementation solutions, the steps before obtaining the current operation data of multiple components are described in detail, and how to determine whether the current operation data of the component is abnormal operation data according to the historical operation data corresponding to the component is described in detail. As Figure 1 shown, the method may include the following steps: Figure 2 shown, the method may include the following steps:
[0068] Step 201, obtain the historical operation data information and the historical operation status of the historical components corresponding to the historical operation data information.
[0069] Among them, the historical operation status includes historical normal operation status and historical abnormal operation status.
[0070] Specifically, the cloud platform server obtains the historical operation data information of multiple historical components and the historical operation status of the historical components corresponding to the historical operation data information. In a possible implementation manner, after the historical operation data information and the historical operation status of the historical components are detected by multiple component data detection modules, they are sent to the cloud platform server, and the cloud platform server receives the historical operation data information and the historical operation status of the historical components corresponding to the historical operation data information. In another possible implementation manner, after the historical operation data information and the historical operation status of the historical components are detected by multiple component data detection modules, the cloud platform server obtains the historical operation data information and the historical operation status of the historical components corresponding to the historical operation data information from the multiple component data detection modules. After the cloud platform server obtains multiple historical operation data information and multiple corresponding historical operation statuses, the data accuracy is relatively high and the error is relatively small.
[0071] Step 202: Determine the historical normal operation data information and the historical abnormal operation data information according to the correspondence between the historical operation data information and the historical operation status.
[0072] Specifically, the historical operation data information corresponding to the historical normal operation status is the historical normal operation data information, and the historical operation data information corresponding to the historical abnormal operation status is the historical abnormal operation data information.
[0073] Step 203: Obtain the current operation data of multiple components inside the current laundry treatment device.
[0074] Specifically, in the current laundry treatment device, there are multiple components. For example, there are multiple components in a washing machine. When the components operate, they will generate multiple operation data, such as voltage, current, power consumption, power, etc. In a possible implementation manner, multiple component data detection modules are installed inside the current laundry treatment device, and the cloud platform server obtains the current operation data detected by the multiple component data detection modules as the current operation data of multiple components inside the current laundry treatment device. In another possible implementation manner, the current laundry treatment device sends the current operation data of multiple components to the cloud platform server, and the cloud platform server receives the current operation data of multiple components inside the current laundry treatment device.
[0075] Exemplarily, multiple component data detection modules are installed inside the current laundry treatment device, and the current operation data of each component will be detected by the data detection module, and the cloud platform server obtains the current operation data of multiple components detected by the multiple component data detection modules.
[0076] Step 204: For each component, if the current operation data matches the historical normal operation data information, determine that the current operation data of the component is normal operation data.
[0077] Specifically, for each component, compare the current operating data with the historical normal operating data information to determine whether the current operating data is normal operating data. If the current operating data matches the historical normal operating data information, then determine that the current operating data of the component is normal operating data.
[0078] Exemplarily, the historical normal operating data information corresponding to component A is that the current value when historical component B operates normally is between 2A and 5A, and the current operating data of component A is a current value of 3A. Therefore, it is determined that the current operating data of the component is normal operating data.
[0079] Step 205, for each component, if the current operating data matches the historical abnormal operating data information, then determine that the current operating data of the component is abnormal operating data.
[0080] Specifically, for each component, compare the current operating data with the historical abnormal operating data information to determine whether the current operating data is abnormal operating data. If the current operating data matches the historical abnormal operating data information, then determine that the current operating data of the component is abnormal operating data.
[0081] Exemplarily, the historical abnormal operating data information corresponding to component A is that the current value when historical component B operates abnormally is 8A, and the current operating data of component A is a current value of 8A. Therefore, it is determined that the current operating data of the component is abnormal operating data.
[0082] Step 206, if the current operating data is abnormal operating data, then determine the predicted failure time of the target component corresponding to the current operating data according to the data information of the current operating data, and send the component information and the predicted failure time of the target component to the maintenance personnel.
[0083] Specifically, when the current operation data is abnormal operation data, the predicted failure time of the target component corresponding to the current operation data is determined according to the data information of the current operation data. For example, the data information of the current operation data includes the change trend of the current operation data, and the predicted failure time of the target component corresponding to the current operation data is determined according to the change trend of the current operation data. After obtaining the predicted failure time of the target component, the component information and the predicted failure time of the target component are sent to the maintenance personnel, where the component information of the target component is the information that can identify the target component, such as the number of the target component, etc., facilitating the maintenance personnel to perform maintenance on the target component. The component information and the predicted failure time of the target component can also be sent to the user terminal corresponding to the current laundry handling device, informing the user in advance of the possible failure situation, avoiding the occurrence of failures when the user uses the laundry handling device, and improving the user experience. The component information and the predicted failure time of the target component can also be sent to the current laundry handling device, and the current laundry handling device displays the component information and the predicted failure time of the target component on the device display, facilitating the user or the maintenance personnel to understand.
[0084] Exemplarily, the data information of the current operation data is that the change trend of the current of component A increases with time. According to this change trend, it is determined that the predicted failure time of component A is ten minutes later. After obtaining the predicted failure time of component A, the number of component A and the predicted failure time are sent to the maintenance personnel, facilitating the maintenance personnel to perform maintenance on component A.
[0085] It should be understood that although Figure 2 the steps in the flowchart of Figure 2 are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover,
[0086] The solution of this application is applied to a cloud platform server. Before obtaining the current operation data of multiple components, it obtains historical operation data information and the historical operation status of the historical components corresponding to the historical operation data information, so that the accuracy of the historical operation data information in the cloud platform server is relatively high, and the judgment accuracy of determining whether the current operation data is abnormal operation data is improved. It can complete the judgment of the current operation data based on the historical normal operation data information and the historical abnormal operation data information, increasing the judgment means for determining whether the current operation data is abnormal operation data and further improving the maintenance efficiency.
[0087] Figure 3 It is a structural schematic diagram of a fault monitoring device provided by this application. This device is applicable to execute the fault monitoring method provided by this application. This fault monitoring device is configured in a cloud platform server. As Figure 3 shown, this device may specifically include:
[0088] An acquisition module 301, configured to acquire the current operation data of multiple components inside the current laundry processing device.
[0089] A determination module 302, configured to, for each of the components, determine whether the current operation data of the component is abnormal operation data according to the historical operation data information corresponding to the component; wherein, the historical operation data is the operation data of the historical components stored in the cloud platform server, and the historical components are components with the same device attributes as the component.
[0090] A prediction module 303, configured to, if the current operation data is abnormal operation data, determine the predicted failure time of the target component corresponding to the current operation data according to the data information of the current operation data, and send the component information of the target component and the predicted failure time to the maintenance personnel.
[0091] In an embodiment, before the acquisition module 301 acquires the current operation data of multiple components inside the current laundry processing device, it is further configured to: acquire the historical operation data information and the historical operation status of the historical components corresponding to the historical operation data information; wherein, the historical operation status includes a historical normal operation status and a historical abnormal operation status; determine the historical normal operation data information and the historical abnormal operation data information according to the correspondence between the historical operation data information and the historical operation status.
[0092] In one embodiment, the determining module 302 is specifically configured to: for each of the components, if the current operation data matches the historical normal operation data information, determine the current operation data of the component as normal operation data; for each of the components, if the current operation data matches the historical abnormal operation data information, determine the current operation data of the component as abnormal operation data.
[0093] In one embodiment, the data information of the current operation data includes the operation duration of the current operation data. The prediction module 303 determines the predicted failure time of the target component corresponding to the current operation data according to the data information of the current operation data, including: obtaining the historical data information corresponding to the current operation data, and determining the failure generation duration corresponding to the historical data information; wherein, the historical data information corresponding to the current operation data is the data information with the same attributes as the current operation data in the historical operation data information; obtaining a duration difference according to the operation duration of the current operation data and the failure generation duration; determining the predicted failure time of the target component corresponding to the current operation data according to the current time and the duration difference.
[0094] In one embodiment, the data information of the current operation data includes the number of operations of the current operation data. The prediction module 303 determines the predicted failure time of the target component corresponding to the current operation data according to the data information of the current operation data, including: obtaining the historical data information corresponding to the current operation data, and determining the number of abnormal times corresponding to the historical data information; wherein, the historical data information corresponding to the current operation data is the data information with the same attributes as the current operation data in the historical operation data information; obtaining a number difference according to the number of operations of the current operation data and the number of abnormal times; determining the predicted failure time of the target component corresponding to the current operation data according to the current time, the number difference, and the abnormal operation duration corresponding to each occurrence of abnormality in the historical data information.
[0095] In one embodiment, before the determining module 302 determines whether the current operation data of the component is abnormal operation data according to the historical operation data information corresponding to the component, it is further configured to: obtain the operation program corresponding to the current laundry treatment device; determine the historical operation data information corresponding to the component according to the operation program corresponding to the current laundry treatment device and the historical operation program corresponding to the historical operation data information stored in the cloud platform server.
[0096] In one embodiment, the component information of the target component of the prediction module 303 includes at least one of the following: the name of the target component, the cause of the failure of the target component, and the repair solution corresponding to the cause of the failure of the target component.
[0097] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. For the specific working process of the above-described functional modules, reference can be made to the corresponding process in the foregoing method embodiments, which will not be elaborated here.
[0098] The device of the present application is configured in a cloud platform server to obtain the current operation data of multiple components inside the current laundry processing device; for each component, determine whether the current operation data of the component is abnormal operation data according to the historical operation data information corresponding to the component; wherein, the historical operation data is the operation data of historical components stored in the cloud platform server, and the historical components are components with the same device attributes as the component; if the current operation data is abnormal operation data, then determine the predicted failure time of the target component corresponding to the current operation data according to the data information of the current operation data, and send the component information and the predicted failure time of the target component to the maintenance personnel. That is, on the one hand, according to the historical operation data information corresponding to the component, it is determined whether the current operation data of the component is abnormal operation data, so as to determine whether the operation of the component is abnormal, quickly locate the components that may malfunction inside the laundry processing device, thereby improving the maintenance efficiency. On the other hand, according to the data information of the current operation data, the predicted failure time of the target component corresponding to the current operation data is determined, which can predict in advance the time when the target component fails, and process the target component in advance to avoid the occurrence of failures, effectively solving the situation of failures during operation and improving the user experience.
[0099] The present application also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the fault monitoring method provided in any of the foregoing embodiments.
[0100] The present application also provides a computer-readable medium, on which a computer program is stored. When the program is executed by a processor, it implements the fault monitoring method provided in any of the foregoing embodiments.
[0101] Next, refer to Figure 4 , which shows a schematic structural diagram of an electronic device 400 suitable for implementing the present application. Figure 4 The electronic device shown is only an example and should not impose any limitations on the functions and usage scope of the present application.
[0102] As Figure 4As shown, the electronic device 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the programs stored in the read-only memory (ROM) 402 or the programs loaded from the storage section 408 into the random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.
[0103] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read from it can be installed into the storage section 408 as needed.
[0104] Specifically, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 409, and / or installed from the removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, the above functions defined in the system of the present application are executed.
[0105] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can, for example, but is not limited to, be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: 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 of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. And in this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.
[0106] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.
[0107] The modules and / or units involved in this application can be implemented in software or in hardware. The described modules and / or units can also be provided in a processor. For example, it can be described as: A processor includes an acquisition module, a determination module, and a prediction module. Among them, the names of these modules do not constitute a limitation to the module itself in some cases.
[0108] As another aspect, this application also provides a computer-readable medium. This computer-readable medium can be included in the device described in the above embodiments; it can also exist alone without being assembled into the device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by the device, the device includes:
[0109] Acquire the current operation data of multiple components inside the current clothing processing device; for each component, determine whether the current operation data of the component is abnormal operation data according to the historical operation data information corresponding to the component; where the historical operation data is the operation data of historical components stored in the cloud platform server, and the historical components are components with the same device attributes as the component; if the current operation data is abnormal operation data, then determine the predicted failure time of the target component corresponding to the current operation data according to the data information of the current operation data, and send the component information and predicted failure time of the target component to the maintenance personnel.
[0110] According to the technical solution of this application, applied to the cloud platform server, acquire the current operation data of multiple components inside the current clothing processing device; for each component, determine whether the current operation data of the component is abnormal operation data according to the historical operation data information corresponding to the component; where the historical operation data is the operation data of historical components stored in the cloud platform server, and the historical components are components with the same device attributes as the component; if the current operation data is abnormal operation data, then determine the predicted failure time of the target component corresponding to the current operation data according to the data information of the current operation data, and send the component information and predicted failure time of the target component to the maintenance personnel. That is, for the solution of this application, on the one hand, determine whether the current operation data of the component is abnormal operation data according to the historical operation data information corresponding to the component, so as to determine whether the operation of the component is abnormal, quickly locate the components that may malfunction inside the clothing processing device, thereby improving the maintenance efficiency. On the other hand, determine the predicted failure time of the target component corresponding to the current operation data according to the data information of the current operation data, which can predict in advance the time when the target component fails, and process the target component in advance to avoid the occurrence of failures, effectively solving the situation of failures during operation, and improving the user experience.
[0111] The above specific embodiments do not limit the protection scope of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of this application shall be included within the protection scope of this application.
Claims
1. A fault monitoring method, characterized in that, Applied to a cloud platform server, the method includes: Obtain the current operation data of multiple components inside the current clothing processing device; For each of the components, determine whether the current operation data of the component is abnormal operation data according to the corresponding historical operation data information of the component; wherein, the historical operation data is the operation data of historical components stored in the cloud platform server, and the historical components are components with the same device attributes as the component; If the current operation data is abnormal operation data, determine the predicted failure time of the target component corresponding to the current operation data according to the data information of the current operation data, and send the component information of the target component and the predicted failure time to the maintenance personnel.
2. The method according to claim 1, wherein Before obtaining the current operation data of multiple components inside the current clothing processing device, the method further includes: Obtain the historical operation data information and the historical operation status of the historical components corresponding to the historical operation data information; wherein, the historical operation status includes historical normal operation status and historical abnormal operation status; Determine the historical normal operation data information and the historical abnormal operation data information according to the corresponding relationship between the historical operation data information and the historical operation status.
3. The method according to claim 2, characterized in that The step of, for each of the components, determining whether the current operation data of the component is abnormal operation data according to the corresponding historical operation data of the component includes: For each of the components, if the current operation data matches the historical normal operation data information, determine that the current operation data of the component is normal operation data; For each of the components, if the current operation data matches the historical abnormal operation data information, determine that the current operation data of the component is abnormal operation data.
4. The method according to claim 1, wherein The data information of the current operation data includes the operation duration of the current operation data. The step of determining the predicted failure time of the target component corresponding to the current operation data according to the data information of the current operation data includes: Obtain the historical data information corresponding to the current operation data and determine the failure generation duration corresponding to the historical data information; wherein, the historical data information corresponding to the current operation data is the data information with the same attributes as the current operation data in the historical operation data information; Obtain the duration difference according to the operation duration of the current operation data and the failure generation duration; Determine the predicted failure time of the target component corresponding to the current operation data according to the current time and the duration difference.
5. The method according to claim 1, wherein The data information of the current operation data includes the operation times of the current operation data. The step of determining the predicted failure time of the target component corresponding to the current operation data according to the data information of the current operation data includes: Obtain the historical data information corresponding to the current operation data and determine the abnormal times corresponding to the historical data information; wherein, the historical data information corresponding to the current operation data is the data information with the same attributes as the current operation data in the historical operation data information; Obtain a difference in counts based on the number of runs of the current operating data and the number of anomalies. Based on the current time, the difference in counts, and the abnormal operating duration corresponding to each occurrence of an anomaly in the historical data information, determine the predicted failure time of the target component corresponding to the current operating data.
6. The method according to claim 1, characterized in that Before determining whether the current operating data of the component is abnormal operating data according to the historical operating data information corresponding to the component, the method further includes: Obtain the operating program corresponding to the current laundry treatment device. Based on the operating program corresponding to the current laundry treatment device and the historical operating program corresponding to the historical operating data information stored in the cloud platform server, determine the historical operating data information corresponding to the component.
7. The method according to claim 1, characterized in that, The component information of the target component includes at least one of the following: the name of the target component, the cause of the failure of the target component, and the repair solution corresponding to the cause of the failure of the target component.
8. A fault monitoring device, characterized in that, Configured in the cloud platform server, including: An acquisition module for acquiring the current operating data of multiple components inside the current laundry treatment device. A determination module for, for each of the components, determining whether the current operating data of the component is abnormal operating data according to the historical operating data information corresponding to the component; wherein, the historical operating data is the operating data of historical components stored in the cloud platform server, and the historical components are components with the same device attributes as the component. A prediction module for, if the current operating data is abnormal operating data, determining the predicted failure time of the target component corresponding to the current operating data according to the data information of the current operating data, and sending the component information of the target component and the predicted failure time to the maintenance personnel.
9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the fault monitoring method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the fault monitoring method according to any one of claims 1 to 7.