CN112101665A - Fault detection early warning method and device, storage medium and electronic equipment - Google Patents
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Abstract
The invention discloses a fault detection early warning method, a fault detection early warning device, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring operation data of the intelligent equipment; detecting whether the operation data comprises target fault data or not; when the operating data comprises target fault data, acquiring target associated fault data associated with the target fault data; and generating fault detection early warning information according to the target associated fault data and the target fault data. By adopting the method, the fault positions corresponding to the target fault data and the target associated fault data respectively can be detected by maintenance personnel according to the fault detection early warning information, so that the problems that the intelligent equipment needs to be maintained for many times and has repeated complaints due to the fact that the associated fault is likely to occur after the intelligent equipment breaks down are effectively solved, and the product experience and after-sale satisfaction of users are greatly improved.
Description
Technical Field
The invention belongs to the field of computers, and particularly relates to a fault detection early warning method and device, a storage medium and electronic equipment.
Background
With the development of internet technology and the wide application of big data technology, various industries begin to seriously consider the commercial value brought by big data so as to better serve users of the industries and improve the product competitiveness.
The inventor finds that, after a target failure occurs in a smart device, a plurality of other related failures related to the target failure may occur within a period of time, so that a maintenance person can maintain the smart device for a plurality of times, and the experience and after-sales satisfaction of a user on the smart device are greatly affected.
Disclosure of Invention
The invention provides a fault detection early warning method, a fault detection early warning device, a storage medium and electronic equipment, which can obtain a correlation fault after a target fault occurs, so that a user can carry out synchronous maintenance according to the target fault and the correlation fault, the problems of repeated maintenance and repeated complaints caused by multiple faults are avoided, and the product experience and after-sale satisfaction of the user are greatly improved.
In a first aspect, the present invention provides a fault detection and early warning method, where the method includes:
acquiring operation data of the intelligent equipment;
detecting whether the operation data comprises target fault data or not;
when the operating data comprises target fault data, acquiring target associated fault data associated with the target fault data;
and generating fault detection early warning information according to the target associated fault data and the target fault data.
Optionally, in the fault detection and early warning method, obtaining target associated fault data associated with the target fault data includes:
acquiring historical fault data corresponding to the intelligent equipment, and performing statistical analysis on the historical fault data to obtain multiple associated faults occurring after the target fault data occurs and the fault frequency of each associated fault;
and obtaining target associated fault data according to the multiple associated faults and the fault frequency of each associated fault.
Optionally, in the fault detection and early warning method, obtaining target associated fault data according to the multiple associated faults and the fault frequency of each associated fault includes:
and taking the associated faults of which the fault frequency is greater than a preset threshold value in the multiple associated faults as target associated fault data.
Optionally, in the fault detection and early warning method, obtaining the target associated fault data according to the multiple associated faults and the fault frequency of each associated fault includes:
and sequencing according to the occurrence frequency of each correlation fault in the multiple correlation faults from large to small, and taking the correlation faults respectively corresponding to the occurrence frequencies of the faults in the preset number as target correlation fault data.
Optionally, in the fault detection and early warning method, the method further includes:
searching the severity level corresponding to each target associated fault data from the preset corresponding relation of the severity levels of the faults;
obtaining a fault priority coefficient of each target associated fault data according to the severity level corresponding to each target associated fault data and the fault frequency of each target associated fault data;
generating fault detection early warning information according to the target associated fault data and the target fault data, wherein the fault detection early warning information comprises the following steps:
and sequencing the target associated fault data according to the fault priority coefficient of each target associated fault data, and generating fault detection early warning information comprising the sequenced target associated fault data and the target fault data.
Optionally, in the fault detection and early warning method, obtaining the fault priority coefficient of each target associated fault data according to the severity level corresponding to each target associated fault data and the fault frequency of each target associated fault data includes:
acquiring a severity proportion coefficient corresponding to each severity grade;
calculating the severity grade corresponding to each kind of target associated fault data, the severity proportion coefficient corresponding to each severity grade and the fault frequency of each kind of target associated fault data by adopting a preset calculation formula to obtain a fault priority coefficient of the target associated fault data, wherein the preset calculation formula comprises the following steps:
piassociating a fault priority coefficient, x, of fault data for the ith targetiAssociating the frequency of occurrence of fault data for the ith target, AiAnd associating the severity of the fault data with the ith target by a ratio coefficient.
Optionally, in the above method for detecting and warning a fault, the fault data includes a preset fault code, and detecting whether the operating data includes the fault data includes:
and detecting whether the operation data comprises at least one of a plurality of preset fault codes.
In a third aspect, the present invention provides a fault detection and early warning apparatus, including:
the first acquisition module is used for acquiring the operating data of the intelligent equipment;
the detection module is used for detecting whether the operation data comprises target fault data or not;
the second acquisition module is used for acquiring target associated fault data associated with the target fault data when the operating data comprises the target fault data;
and the early warning module is used for generating fault detection early warning information according to the target associated fault data and the target fault data.
In a third aspect, the present invention provides a storage medium storing a computer program, which when executed by one or more processors implements the fault detection and warning method as described above
In a fourth aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to execute the fault detection and early warning method
The invention provides a fault detection early warning method, a fault detection early warning device, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring operation data of the intelligent equipment; detecting whether the operation data comprises target fault data or not; when the operating data comprises target fault data, acquiring target associated fault data associated with the target fault data; and generating fault detection early warning information according to the target associated fault data and the target fault data. By adopting the method, when the target fault data exists in the operation data of the intelligent equipment, the target associated fault data of the target fault data is obtained, and the fault detection early warning information is generated according to the target associated fault data of the target fault data set, so that maintenance personnel can detect the fault positions corresponding to the target fault data and the target associated fault data respectively according to the fault detection early warning information, the problems that the intelligent equipment needs to be maintained for many times and is complained repeatedly due to the fact that the associated fault is likely to occur after the intelligent equipment fails are effectively solved, and the product experience and after-sale satisfaction of users are greatly improved.
Drawings
The accompanying drawings are included to provide a further understanding of the technology or prior art of the present application and are incorporated in and constitute a part of this specification. The drawings expressing the embodiments of the present application are used for explaining the technical solutions of the present application, and should not be construed as limiting the technical solutions of the present application.
Fig. 1 is a schematic flow chart of a fault detection and early warning method provided in an embodiment of the present application.
Fig. 2 is a schematic flowchart of step S120 in fig. 1.
Fig. 3 is another schematic flow chart of a fault detection and early warning method according to an embodiment of the present disclosure.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the accompanying drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the corresponding technical effects can be fully understood and implemented. The embodiments and the features of the embodiments can be combined without conflict, and the technical solutions formed are all within the scope of the present invention.
Additionally, the steps illustrated in the flow charts of the figures may be performed in a computer such as a set of computer-executable instructions. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.
First embodiment
Referring to fig. 1, the present embodiment provides a fault detection and early warning method, which may be executed by one or more processors, and when the one or more processors execute the method, the method includes the following steps:
step S110: and acquiring the operating data of the intelligent equipment.
Step S120: and detecting whether the operation data comprises target fault data.
Step S130: and when the running data comprises target fault data, obtaining target associated fault data associated with the target fault data.
Step S140: and generating fault detection early warning information according to the target associated fault data and the target fault data.
By adopting the steps, when target fault data exist in the operating data of the intelligent equipment, the target associated fault data of the target fault data are acquired, and fault detection early warning information is generated according to the target associated fault data of the target fault data set, so that maintenance personnel can detect fault positions corresponding to the target fault data and the target associated fault data respectively according to the fault detection early warning information, the problems that the intelligent equipment needs to be maintained for many times and has repeated complaints due to the fact that the associated fault possibly occurs after the intelligent equipment breaks down are effectively avoided, and the product experience and after-sale satisfaction of users are greatly improved.
Specifically, in step S110, the operation data of the intelligent device may be acquired in real time, or acquired by a signal acquisition device connected to the intelligent device, and is not specifically limited herein and may be set according to actual requirements.
The operation data of the smart device may include, but is not limited to, voltage, current, indoor temperature, humidity, motor operation speed, etc. of the machine operation. It will be appreciated that the operational data for different types of smart devices may be different, for example, when the smart device is a refrigerator or an air conditioner, and may also include cooling parameters.
It should be noted that, when the intelligent device has a fault during operation, the operation data may include fault data, where the fault data may be fault codes and/or fault descriptions, where the fault codes and the fault descriptions are basic data of a company, for example, a fault code of a refrigerant leakage is E1, and a system display E1 proves the refrigerant leakage.
In step S120, the manner of detecting whether the operation data includes the target fault data may be to determine whether at least one target fault data belonging to the multiple types of fault data exists in the operation data. In this embodiment, when the fault data is a fault code, the step S120 may specifically be: and detecting whether the operation data comprises at least one of a plurality of preset fault codes.
In step S130, the target associated fault data associated with the target fault data may be obtained by searching for associated fault data associated with the target fault data from a preset corresponding table, and it is understood that a plurality of types of fault data and associated fault data associated with each type of fault data may be stored in the preset corresponding table. Or when the operating data includes target fault data, searching historical fault data corresponding to the intelligent device from a preset fault database, and performing statistical analysis on the historical data to obtain associated fault data related to the target fault data.
Referring to fig. 2, in the present embodiment, the step S120 includes:
step S122: and acquiring historical fault data corresponding to the intelligent equipment, and performing statistical analysis on the historical fault data to obtain multiple associated faults occurring after the target fault data occurs and the fault frequency of each associated fault.
The method for obtaining the historical fault data corresponding to the intelligent device may be to obtain the historical fault data of the intelligent device from a historical maintenance database or a historical fault database, which is not specifically limited herein and may be set according to actual requirements.
Step S124: and obtaining target associated fault data according to the multiple associated faults and the fault frequency of each associated fault.
In step S124, the associated faults with the fault frequency greater than the preset threshold value in the multiple associated faults may be used as the target associated fault data, or the associated faults may be sorted according to the occurrence frequency of each associated fault in the multiple associated faults from large to small, and the associated faults corresponding to the fault occurrence frequencies in the preset number are used as the target associated fault data and are set according to actual requirements.
It should be noted that, when the associated fault of which the fault frequency is greater than the preset threshold value in the multiple associated faults is taken as the target associated fault data in step S124, it may be considered that, after the target fault corresponding to the target fault data occurs, a fault occurs immediately thereafter and is taken as an associated fault, the associated fault is counted to obtain the occurrence frequency of the associated fault, and when the occurrence frequency of the associated fault is greater than the preset value, for example, 1%, the corresponding associated fault is taken as the target associated fault.
When the step S124 ranks the multiple associated faults according to the occurrence frequency of each associated fault in the multiple associated faults in descending order, and takes the associated faults corresponding to the preset number of fault occurrence frequencies as the target associated fault data, the associated fault with the fault occurrence frequency ranked in the order of top 5 or top 3 may be taken as the target associated fault.
In step S140, generating the fault detection warning information according to the target-related fault data and the target fault data may be generating warning information including fault positions corresponding to the target-related fault data and the target fault data, or generating warning information for arranging the target fault data and the target-related fault data according to a preset arrangement rule.
Referring to fig. 3, in this embodiment, in order to facilitate the user to perform the fault detection, in this embodiment, the method further includes:
step S160: and searching the severity level corresponding to each target associated fault data from the preset corresponding relationship of the severity level of the fault.
Step S170: and obtaining the fault priority coefficient of each target associated fault data according to the corresponding severity grade of each target associated fault data and the fault frequency of each target associated fault data.
Specifically, different severity ratios are corresponding to different severity levels, and the severity ratio corresponding to each severity level is obtained; calculating the severity grade corresponding to each kind of target associated fault data, the severity proportion coefficient corresponding to each severity grade and the fault frequency of each kind of target associated fault data by adopting a preset calculation formula to obtain a fault priority coefficient of the target associated fault data, wherein the preset calculation formula comprises the following steps:
picorrelating the number of failures for the ith targetAccording to the fault priority coefficient, xiAssociating the frequency of occurrence of fault data for the ith target, AiAnd associating the severity of the fault data with the ith target by a ratio coefficient.
The step S150 may specifically be: and sequencing the target associated fault data according to the fault priority coefficient of each target associated fault data, and generating fault detection early warning information comprising the sequenced target associated fault data and the target fault data.
Through the setting, after the user maintains the fault corresponding to the target fault data, the user can conveniently perform further inspection and maintenance according to the priority ranking sequence of the target related fault data, and therefore the fault overhauling efficiency can be improved.
Example two
The embodiment provides a fault detection early warning method, which is applied to electronic equipment associated with intelligent equipment, wherein various operation state detection sensors are installed in the intelligent equipment and used for detecting operation data of the intelligent equipment and uploading the operation data to the electronic equipment according to specific frequency. The electronic equipment is used for comparing the operation data with preset fault data to detect whether target fault data matched with the preset fault data exist in the operation data, and when the target fault data exist, the historical fault data of the intelligent equipment are searched from the maintenance data of the current electric equipment, so that the historical fault data of the intelligent equipment are utilized to perform statistical analysis to determine whether target related data related to the target fault data exist. (wherein, the statistical analysis of the historical fault data of the intelligent device is used to determine whether target-related data related to the target fault data exists or not, whether the target-related data exists or not may be determined to be strongly related according to a preset empirical threshold, for example, whether the frequency of an X1 fault type occurring immediately after a certain X fault type is 1%, the frequency of an X2 fault type occurring is 2%, the frequency of an X3 fault type occurring is 3%, the frequency of other fault types is less than 1%, if the threshold set in the background is 1%, and if greater than or equal to 1%, the relationship is considered to be strongly related, then the X1, X2, and X3 fault types are determined to be strongly related to the X fault type, so that X1, X2, and X3 are used as target-related fault data; the frequency of the occurrence of each related fault may also be automatically calculated according to a calculation logic according to rule one, then the order from top to bottom, the top N related faults are taken according to the order size, n value can be set by itself to determine target-related fault data), determining the fault levels a1, a2 and A3 of the fault types with strong association relationship according to the corresponding relationship between the fault types of the basic data and the fault levels, determining the priority of the associated faults, obtaining the final priority Pi according to the ratio of the frequency of the occurrence of the strong association faults multiplied by the ratio of the fault levels, and sorting from large to small, for example: the fault level of X1 is A1, the fault level of X2 is A2, the fault level of X3 is A3, and in addition, the severity of the A1 level is 70%, the severity of the A2 level is 20%, the severity of the A3 level is 10%, and the specific formula is as follows:
thus, it follows: and P1, P3 and P2, and generating fault detection early warning information by the sequencing result and the target fault information and pushing the fault detection early warning information to maintenance personnel, wherein the fault detection early warning information comprises: "the current fault X may cause faults X1, X2 and X3 in synchronization, and the corresponding priority is X1> X3> X2", so that the maintenance personnel can check related faults in synchronization when going to service. Therefore, the problems that the intelligent equipment needs to be maintained for many times and has repeated complaints due to the fact that the intelligent equipment is possibly associated with faults after the intelligent equipment breaks down are effectively solved, and product experience and after-sale satisfaction of users are greatly improved.
EXAMPLE III
The embodiment provides a fault detection early warning device, which comprises a first acquisition module, a detection module, a second acquisition module and an early warning module.
The first acquisition module is used for acquiring the operating data of the intelligent equipment.
The description of the first obtaining module may specifically refer to the detailed description of step S110 in the foregoing method embodiment, that is, step S110 may be executed by the first obtaining module.
The detection module is used for detecting whether the operation data comprises target fault data.
The description of the detection module may refer to the detailed description of step S120 in the foregoing method embodiment, that is, step S120 may be executed by the detection module.
The second obtaining module is configured to obtain target associated fault data associated with the target fault data when the operating data includes the target fault data.
The description of the second obtaining module may refer to the detailed description of step S130 in the foregoing method embodiment, that is, step S130 may be executed by the second obtaining module.
And the early warning module is used for generating fault detection early warning information according to the target associated fault data and the target fault data.
The description of the early warning module may refer to the detailed description of step S140 in the foregoing method embodiment, that is, step S140 may be executed by the early warning module.
Example four
The present embodiment provides a storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by one or more processors may implement the failure detection warning method of the embodiment.
The specific embodiment process of the above method steps can be referred to as embodiment one, and the detailed description of this embodiment is not repeated herein.
EXAMPLE five
The embodiment of the application provides an electronic device, which comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the fault detection early warning method in the first embodiment is provided.
It is to be understood that the electronic device may also include multimedia components, input/output (I/O) interfaces, and communication components.
The processor is configured to perform all or part of the steps in the fault detection and warning method according to the first embodiment. The memory is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.
The Processor may be an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to execute the fault detection and warning method in the first embodiment.
The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.
In the embodiments provided in the present application, it should be understood that the disclosed method can be implemented in other ways. The embodiments described above are merely illustrative, and the flowcharts and block diagrams in the figures, for example, illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
It should be noted that the above description is only a specific embodiment of the present application, but the above description is only an embodiment adopted for facilitating understanding of the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure, and it is intended that all such changes and modifications as fall within the true spirit and scope of the disclosure be embraced therein. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A fault detection early warning method is characterized by comprising the following steps:
acquiring operation data of the intelligent equipment;
detecting whether the operation data comprises target fault data or not;
when the operating data comprises target fault data, acquiring target associated fault data associated with the target fault data;
and generating fault detection early warning information according to the target associated fault data and the target fault data.
2. The fault detection and early warning method according to claim 1, wherein obtaining target-associated fault data associated with the target fault data comprises:
acquiring historical fault data corresponding to the intelligent equipment, and performing statistical analysis on the historical fault data to obtain multiple associated faults occurring after the target fault data occurs and the fault frequency of each associated fault;
and obtaining target associated fault data according to the multiple associated faults and the fault frequency of each associated fault.
3. The fault detection early warning method according to claim 2, wherein obtaining target associated fault data according to the plurality of associated faults and the fault frequency of each associated fault comprises:
and taking the associated faults of which the fault frequency is greater than a preset threshold value in the multiple associated faults as target associated fault data.
4. The fault detection and early warning method according to claim 2, wherein obtaining target associated fault data according to the plurality of associated faults and the fault frequency of each associated fault comprises:
and sequencing according to the occurrence frequency of each correlation fault in the multiple correlation faults from large to small, and taking the correlation faults respectively corresponding to the occurrence frequencies of the faults in the preset number as target correlation fault data.
5. The fault detection and warning method according to claim 2, further comprising:
searching the severity level corresponding to each target associated fault data from the preset corresponding relation of the severity levels of the faults;
obtaining a fault priority coefficient of each target associated fault data according to the severity level corresponding to each target associated fault data and the fault frequency of each target associated fault data;
generating fault detection early warning information according to the target associated fault data and the target fault data, wherein the fault detection early warning information comprises the following steps:
and sequencing the target associated fault data according to the fault priority coefficient of each target associated fault data, and generating fault detection early warning information comprising the sequenced target associated fault data and the target fault data.
6. The fault detection early warning method according to claim 5, wherein obtaining the fault priority coefficient of each target associated fault data according to the severity level corresponding to each target associated fault data and the fault frequency of each target associated fault data comprises:
acquiring a severity proportion coefficient corresponding to each severity grade;
calculating the severity grade corresponding to each target associated fault data, the severity proportion coefficient corresponding to each severity grade and the fault frequency of each target associated fault data by adopting a preset calculation formula to obtain target associated fault dataA fault priority coefficient of the fault data, wherein the preset calculation formula includes:
piassociating a fault priority coefficient, x, of fault data for the ith targetiAssociating the frequency of occurrence of fault data for the ith target, AiAnd associating the severity of the fault data with the ith target by a ratio coefficient.
7. The fault detection and early warning method according to claim 1, wherein the fault data includes a preset fault code, and the detecting whether the operation data includes the fault data includes:
and detecting whether the operation data comprises at least one of a plurality of preset fault codes.
8. A fault detection and early warning device, the device comprising:
the first acquisition module is used for acquiring the operating data of the intelligent equipment;
the detection module is used for detecting whether the operation data comprises target fault data or not;
the second acquisition module is used for acquiring target associated fault data associated with the target fault data when the operating data comprises the target fault data;
and the early warning module is used for generating fault detection early warning information according to the target associated fault data and the target fault data.
9. A storage medium storing a computer program, wherein the computer program, when executed by one or more processors, implements the fault detection warning method according to any one of claims 1 to 7.
10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to perform the fault detection warning method according to any one of claims 1 to 7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113254056A (en) * | 2021-04-16 | 2021-08-13 | 荣耀终端有限公司 | Method and equipment for updating early warning and fault repairing |
| CN115388610A (en) * | 2021-05-24 | 2022-11-25 | 合肥华凌股份有限公司 | Refrigerator fault prediction method, device and system and electronic equipment |
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