CN112734138A - Fault early warning method, device, equipment and storage medium - Google Patents
Fault early warning method, device, equipment and storage medium Download PDFInfo
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Abstract
The application relates to a fault early warning method, a fault early warning device, equipment and a storage medium, wherein the method is applied to a cloud server and comprises the following steps: acquiring operation parameters of all parts of the household appliance, wherein the operation parameters comprise parameter names and parameter values; determining a fault component after determining that the household appliance has a fault according to the parameter value of each parameter name and the normal value range of each parameter name; determining a risk coefficient of the fault component according to the operation parameters of the fault component; and acquiring a risk grade corresponding to the risk coefficient, and performing fault early warning according to the risk grade to solve the problem that the conventional household appliance is unreasonable in fault early warning mechanism.
Description
Technical Field
The present application relates to the field of home appliance technologies, and in particular, to a method, an apparatus, a device, and a storage medium for fault early warning.
Background
In daily life, household appliances are more and more complete, such as washing machines, dry cleaning machines or dish washing machines and the like, which have important significance for improving the quality of life of people, but various unexpected faults occur in the household appliances, which influence the user experience if the faults are light and influence the performance of the whole machine if the faults are serious, and even cause safety problems.
The existing household appliance fault detection technology usually detects a fault component after the household appliance cannot normally operate, and an early warning mechanism for faults is not reasonable, so that the use requirements of people on the household appliance cannot be met.
Disclosure of Invention
The application provides a fault early warning method, a fault early warning device, equipment and a storage medium, which are used for solving the problem that a fault early warning mechanism of household electrical appliance equipment is unreasonable.
In a first aspect, an embodiment of the present application provides a fault early warning method, where the method is applied to a cloud server, and includes:
acquiring operation parameters of all parts of the household appliance, wherein the operation parameters comprise parameter names and parameter values;
determining a fault component after determining that the household appliance has a fault according to the parameter value of each parameter name and the normal value range of each parameter name;
determining a risk coefficient of the fault component according to the operation parameters of the fault component;
and acquiring a risk grade corresponding to the risk coefficient, and performing fault early warning according to the risk grade.
Optionally, the determining, according to the parameter value of each parameter name and the normal value range of each parameter name, a failed component after determining that the household appliance has a failure includes:
acquiring abnormal parameters of which the parameter values do not belong to the normal value range according to the parameter values of the parameter names and the normal value range of the parameter names;
and determining the fault component after determining that the household appliance has the fault according to the abnormal parameters.
Optionally, the obtaining, according to the parameter value of each parameter name and the normal value range of each parameter name, an abnormal parameter of which the parameter value does not belong to the normal value range includes:
determining the current operation stage of the household appliance according to the operation parameters of all the components;
screening the operation parameters of each part to obtain important parameters of each part corresponding to the current operation stage;
respectively carrying out the following processing on each important parameter: comparing the parameter value corresponding to the parameter name in the important parameter with a normal value range corresponding to the parameter name to obtain a comparison result, wherein the comparison result is used for indicating whether the parameter value is in the normal value range;
and taking the important parameter of which the comparison result is not in the normal value range as the abnormal parameter.
Optionally, the determining that the home appliance device has a fault according to the abnormal parameter includes:
counting the number of the abnormal parameters and the total number of the important parameters;
calculating the specific gravity value of the number of the abnormal parameters in the total number;
and when the specific gravity value is greater than a preset specific gravity value, determining that the household appliance has a fault.
Optionally, determining a fault component after determining that the home appliance has a fault according to the abnormal parameter includes:
comparing the abnormal parameters with respective fault data sets of each component of the household appliance equipment respectively to obtain respective comparison similarity of each component;
and taking the part with the contrast similarity larger than a preset similarity value as the fault part.
Optionally, determining a fault component after determining that the home appliance has a fault according to the abnormal parameter includes:
and sequentially carrying out the following comparison processing processes on each component of the household appliance:
comparing the abnormal parameters with the fault data set of the component to obtain contrast similarity, and judging whether the contrast similarity is greater than a preset similarity value;
if so, taking the part as the fault part, and stopping the comparison processing process;
otherwise, continuing to perform the comparison processing on the next part.
Optionally, the determining a risk factor of the faulty component according to the operating parameter of the faulty component includes:
acquiring preset coefficient values corresponding to the fault component and the current operation stage;
and taking the result obtained by multiplying the preset coefficient value and the specific gravity value as the risk coefficient of the fault component.
Optionally, the risk factor defining value comprises: a first risk coefficient boundary value and a second risk coefficient boundary value, the first risk coefficient boundary value being less than the second risk coefficient boundary value;
the obtaining of the risk level corresponding to the risk coefficient includes:
if the risk coefficient is smaller than the first risk coefficient defining value, determining that the risk grade of the fault component is a low risk grade;
determining the risk level of the faulty component as a medium risk level if the risk coefficient is between the first risk coefficient defining value and the second risk coefficient defining value;
and if the risk coefficient is larger than the second risk coefficient defining value, determining the risk grade of the fault component as a high risk grade.
Optionally, the performing fault early warning according to the risk level includes:
when the risk level of the fault component is the low risk level, sending a first notification message to a user terminal, wherein the first notification message is used for notifying a user of correct use of the household appliance;
when the risk level of the fault component is the medium risk level, sending a second notification message to the user terminal, wherein the second notification message is used for proposing to the user to maintain the household appliance, and sending a third notification message to a maintenance end, and the third notification message is used for notifying maintenance personnel to maintain the household appliance within preset time;
when the risk level of the fault component is the high risk level, sending a stop operation instruction to the household appliance, and sending a fourth notification message to the user terminal, wherein the fourth notification message is used for notifying a user that the household appliance stops working, and sending a fifth notification message to the maintenance end, and the fifth notification message is used for notifying maintenance personnel to immediately maintain the household appliance.
In a second aspect, an embodiment of the present application provides a fault early warning apparatus, including:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring operation parameters of all parts of the household appliance, and the operation parameters comprise parameter names and parameter values;
the fault component monitoring module is used for determining a fault component after determining that the household appliance has a fault according to the parameter value of each parameter name and the normal value range of each parameter name;
the risk coefficient determining module is used for determining the risk coefficient of the fault component according to the operation parameters of the fault component;
and the risk grade division module is used for acquiring the risk grade corresponding to the risk coefficient and carrying out fault early warning according to the risk grade.
In a third aspect, an embodiment of the present application provides an electronic device, including: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the memory for storing a computer program;
the processor is configured to execute the program stored in the memory, so as to implement the fault warning method according to the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the fault pre-warning method according to the first aspect.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages: according to the method provided by the embodiment of the application, after the fact that the household appliance is in failure is determined by obtaining the operation parameters of all parts of the household appliance, according to the parameter value of each parameter name and the normal value range of each parameter name, the failure part is determined, the risk coefficient of the failure part is obtained through the operation parameters of the failure part, the corresponding risk grade is obtained according to the risk coefficient, and corresponding failure early warning is sent according to different risk grades, so that the part which is likely to fail is judged in advance through the operation parameters of the household appliance, the risk grade of the failure is judged in advance through the operation parameters, the failure early warning is carried out according to the risk grade, the failure processing mechanism is more complete and reasonable, the problem that the alarm is carried out after the failure occurs and the use of a user is influenced is avoided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic structural diagram of a fault warning system in an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a washing machine according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a parameter acquisition module in the washing machine according to the embodiment of the present application;
FIG. 4 is a schematic flow chart of a fault warning method in an embodiment of the present application;
FIG. 5 is a flow chart illustrating a method for determining a failed component in an embodiment of the present application;
FIG. 6 is a flowchart illustrating a method for risk classification according to an embodiment of the present application;
fig. 7 is a schematic flowchart of a detailed fault warning method in an embodiment of the present application;
FIG. 8 is a schematic diagram of a fault warning apparatus according to an embodiment of the present disclosure;
fig. 9 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Description of reference numerals:
the system comprises a 1-cloud server, a 2-household appliance, a 21-parameter acquisition module, a 22-main control module, a 23-communication module, a 2101-power acquisition sub-module, a 2102-voltage acquisition sub-module, a 2103-current acquisition sub-module, a 2104-noise acquisition sub-module, a 2105-vibration frequency acquisition sub-module, a 2106-cylinder part displacement acquisition sub-module, a 2107-water flow acquisition sub-module, a 2108-wind flow acquisition sub-module, a 2109-temperature acquisition sub-module, a 2110-humidity acquisition sub-module, a 3-user terminal and a 4-maintenance terminal.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As shown in fig. 1, an embodiment of the present application provides a fault early warning system, including: the system comprises a cloud server 1, household electrical appliance equipment 2, a user terminal 3 and a maintenance terminal 4;
the household appliance 2 may be a washing machine, a dry cleaning machine or a dishwasher, etc., and it should be noted that this is only an example and is not intended to limit the scope of the present application.
In the following embodiments, for convenience of understanding, a washing machine is taken as an example, and similar processing procedures may be adopted for other types of household appliances.
The household appliance 2 is a washing machine, as shown in fig. 2, comprising: parameter acquisition module 21, host system 22 and communication module 23, wherein, communication module 23 is used for establishing communication connection with cloud server 1, and this communication connection can be wireless connection (for example bluetooth, WIFI), also can be wired connection. The specific connection is not limited here.
As shown in fig. 3, in a specific embodiment, the parameter collecting module 21 includes: the power acquisition submodule 2101, the voltage acquisition submodule 2102, the current acquisition submodule 2103, the noise acquisition submodule 2104, the vibration frequency acquisition submodule 2105, the cylinder part displacement acquisition submodule 2106, the water flow acquisition submodule 2107, the wind flow acquisition submodule 2108, the temperature acquisition submodule 2109 and the humidity acquisition submodule 2110. The sub-modules are connected with the main control module 22, collected operating parameters such as power, voltage, current, noise, vibration frequency, cylinder displacement, water flow, air volume, air speed, temperature and humidity and corresponding parameter values are sent to the cloud server in real time or at regular time, the operating parameters are compared, analyzed and processed to determine whether a fault exists in the washing machine, if the fault exists, which component has the fault, and the risk level is determined according to the parameters of the fault component.
The sub-modules included in the parameter acquisition module 21 are only described herein, but the parameter acquisition module does not include only the above sub-modules, for example, a content weight acquisition sub-module may also be included, which is only for illustration and is not intended to limit the scope of the present application.
As shown in fig. 4, a method for performing fault early warning by a cloud server in the embodiment of the present application includes the following steps:
In a specific embodiment, the operation parameters include parameter names and parameter values, such as a parameter including a parameter name of voltage and a parameter name of current, a parameter value of voltage is 5V, a parameter value of current is 0.5A, etc., which are only for explanation of the operation parameters and are not intended to limit the scope of the present application.
And 402, determining a fault component after determining that the household appliance has a fault according to the parameter value of each parameter name and the normal value range of each parameter name.
In a specific embodiment, after determining that the household appliance has a fault according to the parameter value of each parameter name and the normal value range of each parameter name, determining a faulty component includes:
acquiring abnormal parameters of which the parameter values do not belong to the normal value range according to the parameter values of the parameter names and the normal value range of the parameter names; and determining a fault component after determining that the household appliance has a fault according to the abnormal parameters.
For example, taking voltage as an example, when the voltage value exceeds the range of the normal value of voltage from 5V to 10V, the voltage is taken as an abnormal parameter. Here, the division of the exception parameter is merely for explanation and is not used to limit the scope of protection of the present application.
By acquiring the operation parameters of all parts in the operation process of the washing machine in real time or in a timing manner, if abnormal parameters exist, the failure of the washing machine is indicated, and then the failure of all parts of the washing machine is accurately monitored.
During specific implementation, according to the parameter value of each parameter name and the normal value range of each parameter name, obtaining an abnormal parameter of which the parameter value does not belong to the normal value range, including:
determining the current operation stage of the household appliance according to the operation parameters of each component; screening the operation parameters of each component to obtain important parameters of each component corresponding to the current operation stage; the following processing is respectively carried out on each important parameter: comparing the parameter value corresponding to the parameter name in the important parameter with the normal value range corresponding to the parameter name to obtain a comparison result, wherein the comparison result is used for indicating whether the parameter value is in the normal value range; and taking the important parameter which is not in the normal value range as the abnormal parameter.
And screening the operation parameters to obtain important parameters of each component corresponding to the operation stage of the household appliance, and determining whether the household appliance has a fault in the current operation stage according to the important parameters.
The mapping relation between the important parameters of each part and the running stage of the household appliance is configured in the cloud server in advance.
For example, a first interval and a second interval of an important parameter are preset according to the operation stage of the washing machine, and the first interval and the second interval respectively correspond to different operation stages. For example, if a certain important parameter belongs to a second interval, it represents that the current operation stage is the operation stage corresponding to the second interval.
For example, if the obtained operation parameters include the vibration frequency parameter of the drum and the displacement parameter of the drum component, which belong to the second interval, that is, the drum is operating at the time, the washing machine may be operated to the washing stage or the dehydration stage, and if the weight parameter of the content in the drum belongs to the first interval, the washing machine is operated to the washing stage.
For example, the cloud server determines that the current washing stage is the washing stage of the washing machine according to the operation parameters of all parts of the washing machine, and screens the operation parameters of the drum part of the washing machine to obtain important parameters such as a vibration frequency parameter of the drum, a displacement parameter of the drum, a power parameter of the drum and the like.
The vibration frequency parameter of the roller is compared with the normal vibration frequency value range of the roller, the displacement parameter of the roller is compared with the normal displacement value range of the roller, the power parameter of the roller is compared with the normal power value range of the roller, the comparison result of the three important parameters is obtained, and the parameter of which the comparison result is not in the normal value range is taken as the abnormal parameter.
It should be noted that, the example is only provided for convenience of understanding, and does not mean that only the drum operates when the household appliance (in this example, the washing machine) of the present application operates to the washing stage, and important parameters may exist in other operating components, which also does not mean that the important parameters of the drum only include three parts, namely, the vibration frequency parameter, the displacement parameter and the power parameter, and may also include other parameters.
In a specific embodiment, determining that the home device has a fault according to the abnormal parameter includes:
counting the number of abnormal parameters and the total number of important parameters;
calculating the specific gravity value of the number of the abnormal parameters in the total number;
and when the specific gravity value is greater than the preset specific gravity value, determining that the household appliance has a fault.
For example, the preset specific gravity value is 0.3, when the washing machine is operated to the washing stage, the total number of the important parameters of the drum is 10, and the number of the abnormal parameters is 2, the specific gravity value of the parameters is 0.2 at this time and is less than the preset specific gravity value of 0.3, the washing machine is considered to have no fault at this time, if the number of the abnormal parameters is 3, the specific gravity value of the parameters is 0.3 at this time and is equal to the preset specific gravity value of 0.3, the washing machine is also considered to have no fault at this time, and the number of the abnormal parameters is 4, the specific gravity value of the parameters is 0.4 at this time and is greater than the preset specific gravity value of 0.3, and.
It is not meant that the total number of the important parameters of the washing machine from the operation to the washing stage is 10, but the present embodiment is only explained for the purpose of illustration, and is not intended to limit the protection scope of the present application.
In a specific embodiment, after determining that the home appliance has a fault according to the abnormal parameter, determining a faulty component includes:
respectively comparing the abnormal parameters with respective fault data sets of each component of the household appliance to obtain respective comparison similarity of each component; and taking the part with the contrast similarity larger than the preset similarity value as a fault part.
For example, the abnormal parameters are respectively compared with a drum fault data set of the washing machine and a fault data set of the power supply device to obtain the contrast similarity between the abnormal parameters and the drum fault data set, and the contrast similarity between the abnormal parameters and the fault data set of the power supply device, and if the contrast similarity between the abnormal parameters and the drum fault data set is greater than a preset similarity value, the drum is a fault component; and if the contrast similarity between the abnormal parameter and the fault data set of the power supply device is greater than the preset similarity value, the power supply device is a fault component.
The method provided by the embodiment can be applied to the condition that the household appliance has more fault components, and the accuracy of judging the fault components of the household appliance is more accurate.
In a specific embodiment, after determining that the home appliance has a fault according to the abnormal parameter, determining a faulty component includes:
as shown in fig. 5, the following comparison process is performed on each component of the home appliance in sequence:
And step 503, taking the current component as a fault component, and stopping the comparison processing process.
For example, the abnormal parameters are sequentially compared with a drum fault data set of the washing machine and a fault data set of the power supply device to obtain a contrast similarity, if the contrast similarity is greater than a preset similarity value, the drum is a fault part, the contrast processing process of the power supply device is stopped, and if the contrast similarity is less than the preset similarity value, the contrast processing process of the power supply device is performed.
The method provided by the embodiment of the application can obviously shorten the time for judging the fault component of the household appliance.
And step 403, determining the risk coefficient of the fault component according to the operation parameters of the fault component.
In one embodiment, determining a risk factor for a failed component based on an operating parameter of the failed component comprises:
acquiring preset coefficient values corresponding to the fault component and the current operation stage; and the result obtained by multiplying the preset coefficient value by the specific gravity value is used as the risk coefficient of the fault component.
For example, when the washing machine is in a water inlet stage, the preset coefficient value corresponding to the water inlet device should be greater than the preset coefficient values corresponding to other operation stages, and similarly, the drum of the washing machine should have the corresponding preset coefficient values greater than the preset coefficient values corresponding to other operation stages (water inlet and water discharge stages) in a washing stage of the washing machine, that is, the preset coefficient values of the same fault component are different in different operation stages of the household appliance.
And the risk coefficient of the fault component is obtained by the product of the preset coefficient value and the specific gravity value, so that the obtained risk coefficient is more in line with the operation stage of the household appliance, and a powerful basis is provided for the classification of the risk grade.
And step 404, acquiring a risk grade corresponding to the risk coefficient, and performing fault early warning according to the risk grade.
In one embodiment, the risk factor defining values comprise: a first risk coefficient defining value and a second risk coefficient defining value, the first risk coefficient defining value being less than the second risk coefficient defining value;
acquiring a risk grade corresponding to the risk coefficient, wherein the risk grade comprises the following steps:
if the risk coefficient is smaller than the first risk coefficient defining value, determining the risk grade of the fault component as a low risk grade; determining the risk level of the faulty component as a medium risk level if the risk coefficient is between the first risk coefficient defining value and the second risk coefficient defining value; and if the risk coefficient is larger than the second risk coefficient defining value, determining the risk grade of the fault component as a high risk grade.
In specific implementation, as shown in fig. 6, the method flow is as follows:
At step 605, the risk level of the failed component is determined to be an intermediate risk level.
At step 606, the risk level of the failed component is determined to be a high risk level.
The low risk level, the medium risk level and the high risk level may still be further subdivided, for example, the low risk level is specifically subdivided into a first low risk level, a second low risk level and a third low risk level, where the grade division depends on the division precision of the risk defining value, and the low risk level, the medium risk level and the high risk level are further subdivided, and still belong to the protection scope of the present application.
In a specific embodiment, sending a risk for fault early warning according to the risk level includes:
when the risk level of the fault component is a low risk level, a first notification message is sent to the user terminal, the first notification message is used for notifying the user of correctly using the household appliance, when the risk level evaluation result shows that the low risk level exists, the user terminal can only give a certain bad use habit in daily use, and the user terminal prompts the user to correct the use habit when using the washing machine.
And when the risk grade of the fault component is the medium risk grade, sending a second notification message to the user terminal, wherein the second notification message is used for proposing the maintenance of the household appliance to the user, and sending a third notification message to the maintenance end, and the third notification message is used for notifying maintenance personnel to maintain the household appliance within the preset time.
And when the risk grade evaluation result shows that the risk grade is a medium risk grade, namely a certain risk exists and the distance is long, starting a medium risk early warning mechanism, sending a maintenance suggestion notification to a maintenance end by the cloud server, suggesting maintenance personnel to perform home investigation or user self investigation after a period of time, and sending the maintenance suggestion notification to the user terminal.
And when the risk level of the fault component is a high risk level, sending a stop operation instruction to the household appliance, sending a fourth notification message to the user terminal, wherein the fourth notification message is used for notifying the user that the household appliance stops working, and sending a fifth notification message to the maintenance end, and the fifth notification message is used for notifying maintenance personnel to immediately maintain the household appliance.
And when the risk level evaluation result shows that the risk level is high, starting a high risk level early warning mechanism, sending a maintenance request to a maintenance end to require maintenance personnel to immediately perform maintenance treatment, sending a system operation notification to the user terminal, informing the user to immediately stop using the washing machine, and sending an operation stop instruction to the washing machine.
To assist in understanding the present solution, the following provides an example comprising a complete process:
if shown in fig. 7, the specific process of the fault early warning method is as follows:
In step 702, the cloud server compares each important parameter with the corresponding normal value range, determines the number of abnormal parameters, and takes the ratio of the number to the total number as a specific gravity value.
In step 704, the home appliance does not have a fault, and the process is ended.
And step 706, determining the risk level of the fault according to the fault component and sending a fault early warning.
Based on the same concept, the embodiment of the present application provides a fault early warning device, and specific implementation of the device may refer to the description of the method embodiment, and repeated details are not repeated, as shown in fig. 8, the device mainly includes:
an obtaining module 801, configured to obtain operation parameters of each component of the home appliance, where the operation parameters include parameter names and parameter values;
the failure component monitoring module 802 is configured to determine a failure component after determining that the household appliance has a failure according to the parameter value of each parameter name and the normal value range of each parameter name;
a risk coefficient determining module 803, configured to determine a risk coefficient of a failed component according to an operating parameter of the failed component;
and a risk grade division module 804, configured to obtain a risk grade corresponding to the risk coefficient, and perform fault early warning according to the risk grade.
Based on the same concept, an embodiment of the present application further provides an electronic device, as shown in fig. 9, the electronic device mainly includes: a processor 901, a communication interface 902, a memory 903 and a communication bus 904, wherein the processor 901, the communication interface 902 and the memory 903 are in communication with each other through the communication bus 904. The memory 903 stores a program executable by the processor 901, and the processor 901 executes the program stored in the memory 903, so as to implement the following steps: acquiring operation parameters of all parts of the household appliance, wherein the operation parameters comprise parameter names and parameter values; determining a fault component after determining that the household appliance has a fault according to the parameter value of each parameter name and the normal value range of each parameter name; determining a risk coefficient of the fault component according to the operation parameters of the fault component; and acquiring a risk grade corresponding to the risk coefficient, and performing fault early warning according to the risk grade.
The communication bus 904 mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus 904 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 9, but this does not indicate only one bus or one type of bus.
The communication interface 902 is used for communication between the electronic apparatus and other apparatuses.
The Memory 903 may include a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Alternatively, the memory may be at least one storage device located remotely from the processor 901.
The Processor 901 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc., and may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components.
In yet another embodiment of the present application, a computer-readable storage medium is further provided, in which a computer program is stored, and when the computer program runs on a computer, the computer program causes the computer to execute the fault pre-warning method described in the above embodiment.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The available media may be magnetic media (e.g., floppy disks, hard disks, tapes, etc.), optical media (e.g., DVDs), or semiconductor media (e.g., solid state drives), among others.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (12)
1. A fault early warning method is characterized by being applied to a cloud server and comprising the following steps:
acquiring operation parameters of all parts of the household appliance, wherein the operation parameters comprise parameter names and parameter values;
determining a fault component after determining that the household appliance has a fault according to the parameter value of each parameter name and the normal value range of each parameter name;
determining a risk coefficient of the fault component according to the operation parameters of the fault component;
and acquiring a risk grade corresponding to the risk coefficient, and performing fault early warning according to the risk grade.
2. The method of claim 1, wherein determining a failed component after determining that the home appliance device has a failure according to the parameter value of each parameter name and the normal value range of each parameter name comprises:
acquiring abnormal parameters of which the parameter values do not belong to the normal value range according to the parameter values of the parameter names and the normal value range of the parameter names;
and determining the fault component after determining that the household appliance has the fault according to the abnormal parameters.
3. The method according to claim 2, wherein the obtaining, according to the parameter value of each parameter name and the normal value range of each parameter name, an abnormal parameter of which the parameter value does not belong to the normal value range comprises:
determining the current operation stage of the household appliance according to the operation parameters of all the components;
screening the operation parameters of each part to obtain important parameters of each part corresponding to the current operation stage;
respectively carrying out the following processing on each important parameter: comparing the parameter value corresponding to the parameter name in the important parameter with a normal value range corresponding to the parameter name to obtain a comparison result, wherein the comparison result is used for indicating whether the parameter value is in the normal value range;
and taking the important parameter of which the comparison result is not in the normal value range as the abnormal parameter.
4. The method of claim 3, wherein determining that the home device is faulty according to the abnormal parameter comprises:
counting the number of the abnormal parameters and the total number of the important parameters;
calculating the specific gravity value of the number of the abnormal parameters in the total number;
and when the specific gravity value is greater than a preset specific gravity value, determining that the household appliance has a fault.
5. The method according to any one of claims 2 to 4, wherein determining a failed component after determining that the household appliance has a failure according to the abnormal parameter comprises:
comparing the abnormal parameters with respective fault data sets of each component of the household appliance equipment respectively to obtain respective comparison similarity of each component;
and taking the part with the contrast similarity larger than a preset similarity value as the fault part.
6. The method according to any one of claims 2 to 4, wherein determining a failed component after determining that the household appliance has a failure according to the abnormal parameter comprises:
and sequentially carrying out the following comparison processing processes on each component of the household appliance:
comparing the abnormal parameters with the fault data set of the component to obtain contrast similarity, and judging whether the contrast similarity is greater than a preset similarity value;
if so, taking the part as the fault part, and stopping the comparison processing process;
otherwise, continuing to perform the comparison processing on the next part.
7. The method of claim 4, wherein determining the risk factor for the failed component based on the operating parameters of the failed component comprises:
acquiring preset coefficient values corresponding to the fault component and the current operation stage;
and taking the result obtained by multiplying the preset coefficient value and the specific gravity value as the risk coefficient of the fault component.
8. The method of claim 1, 2, 3, 4 or 7, wherein the risk factor defining value comprises: a first risk coefficient boundary value and a second risk coefficient boundary value, the first risk coefficient boundary value being less than the second risk coefficient boundary value;
the obtaining of the risk level corresponding to the risk coefficient includes:
if the risk coefficient is smaller than the first risk coefficient defining value, determining that the risk grade of the fault component is a low risk grade;
determining the risk level of the faulty component as a medium risk level if the risk coefficient is between the first risk coefficient defining value and the second risk coefficient defining value;
and if the risk coefficient is larger than the second risk coefficient defining value, determining the risk grade of the fault component as a high risk grade.
9. The method of claim 8, wherein the performing fault pre-warning according to the risk level comprises:
when the risk level of the fault component is the low risk level, sending a first notification message to a user terminal, wherein the first notification message is used for notifying a user of correct use of the household appliance;
when the risk level of the fault component is the medium risk level, sending a second notification message to the user terminal, wherein the second notification message is used for proposing to the user to maintain the household appliance, and sending a third notification message to a maintenance end, and the third notification message is used for notifying maintenance personnel to maintain the household appliance within preset time;
when the risk level of the fault component is the high risk level, sending a stop operation instruction to the household appliance, and sending a fourth notification message to the user terminal, wherein the fourth notification message is used for notifying a user that the household appliance stops working, and sending a fifth notification message to the maintenance end, and the fifth notification message is used for notifying maintenance personnel to immediately maintain the household appliance.
10. A fault warning device, comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring operation parameters of all parts of the household appliance, and the operation parameters comprise parameter names and parameter values;
the fault component monitoring module is used for determining a fault component after determining that the household appliance has a fault according to the parameter value of each parameter name and the normal value range of each parameter name;
the risk coefficient determining module is used for determining the risk coefficient of the fault component according to the operation parameters of the fault component;
and the risk grade division module is used for acquiring the risk grade corresponding to the risk coefficient and carrying out fault early warning according to the risk grade.
11. An electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the memory for storing a computer program;
the processor is configured to execute the program stored in the memory to implement the fault warning method according to any one of claims 1 to 9.
12. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method of any one of claims 1 to 9.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114036020A (en) * | 2021-11-08 | 2022-02-11 | 北京沃东天骏信息技术有限公司 | Fault diagnosis and alarm method, device, electronic equipment and computer readable medium |
| CN114167781A (en) * | 2021-11-30 | 2022-03-11 | 杭州老板电器股份有限公司 | Early warning system and method for abnormal conditions of kitchen appliances |
| CN114596014A (en) * | 2022-05-10 | 2022-06-07 | 智昌科技集团股份有限公司 | Industrial transportation robot right management method, system, equipment and storage medium |
| CN115060998A (en) * | 2022-06-23 | 2022-09-16 | 博锐尚格科技股份有限公司 | A method, device, equipment and medium for determining abnormality of electricity consumption in transmission lines |
| CN115708027A (en) * | 2021-08-18 | 2023-02-21 | 中国石油天然气股份有限公司 | Equipment detection system and method |
| CN116360367A (en) * | 2023-03-29 | 2023-06-30 | 合肥汇一能源科技有限公司 | Industrial equipment Internet of things data acquisition method and system |
| CN116402492A (en) * | 2023-03-16 | 2023-07-07 | 广东省智能家电研究院 | Household appliance maintenance early warning method and related equipment thereof |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102844721A (en) * | 2010-02-26 | 2012-12-26 | 株式会社日立制作所 | Failure source diagnosis system and method |
| CN104601401A (en) * | 2014-12-26 | 2015-05-06 | 珠海格力电器股份有限公司 | Detection method, device and system for cloud-controlled household appliance |
| CN108062088A (en) * | 2017-12-15 | 2018-05-22 | 广州广电运通金融电子股份有限公司 | Station terminal failure risk method for early warning, device, terminal and storage medium |
| CN108335462A (en) * | 2017-01-19 | 2018-07-27 | 杭州老板电器股份有限公司 | A kind of fault early warning method and kitchen appliance |
| CN110175756A (en) * | 2019-05-07 | 2019-08-27 | 岭澳核电有限公司 | Nuclear power station information system operational safety method for early warning, device, equipment and medium |
| CN110232499A (en) * | 2019-04-26 | 2019-09-13 | 中国电力科学研究院有限公司 | A kind of power distribution network information physical side method for prewarning risk and system |
-
2021
- 2021-01-27 CN CN202110112692.9A patent/CN112734138B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102844721A (en) * | 2010-02-26 | 2012-12-26 | 株式会社日立制作所 | Failure source diagnosis system and method |
| CN104601401A (en) * | 2014-12-26 | 2015-05-06 | 珠海格力电器股份有限公司 | Detection method, device and system for cloud-controlled household appliance |
| CN108335462A (en) * | 2017-01-19 | 2018-07-27 | 杭州老板电器股份有限公司 | A kind of fault early warning method and kitchen appliance |
| CN108062088A (en) * | 2017-12-15 | 2018-05-22 | 广州广电运通金融电子股份有限公司 | Station terminal failure risk method for early warning, device, terminal and storage medium |
| CN110232499A (en) * | 2019-04-26 | 2019-09-13 | 中国电力科学研究院有限公司 | A kind of power distribution network information physical side method for prewarning risk and system |
| CN110175756A (en) * | 2019-05-07 | 2019-08-27 | 岭澳核电有限公司 | Nuclear power station information system operational safety method for early warning, device, equipment and medium |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115708027A (en) * | 2021-08-18 | 2023-02-21 | 中国石油天然气股份有限公司 | Equipment detection system and method |
| CN114036020A (en) * | 2021-11-08 | 2022-02-11 | 北京沃东天骏信息技术有限公司 | Fault diagnosis and alarm method, device, electronic equipment and computer readable medium |
| CN114167781A (en) * | 2021-11-30 | 2022-03-11 | 杭州老板电器股份有限公司 | Early warning system and method for abnormal conditions of kitchen appliances |
| CN114596014A (en) * | 2022-05-10 | 2022-06-07 | 智昌科技集团股份有限公司 | Industrial transportation robot right management method, system, equipment and storage medium |
| CN115060998A (en) * | 2022-06-23 | 2022-09-16 | 博锐尚格科技股份有限公司 | A method, device, equipment and medium for determining abnormality of electricity consumption in transmission lines |
| CN116402492A (en) * | 2023-03-16 | 2023-07-07 | 广东省智能家电研究院 | Household appliance maintenance early warning method and related equipment thereof |
| CN116402492B (en) * | 2023-03-16 | 2024-05-31 | 广东省智能家电研究院 | Household appliance maintenance early warning method and related equipment thereof |
| CN116360367A (en) * | 2023-03-29 | 2023-06-30 | 合肥汇一能源科技有限公司 | Industrial equipment Internet of things data acquisition method and system |
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