CN113240140A - Fault detection method, device, equipment and storage medium of physical equipment - Google Patents
Fault detection method, device, equipment and storage medium of physical equipment Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device, equipment and a storage medium for detecting faults of physical equipment. The method comprises the following steps: responding to a fault query instruction of the target physical device, and acquiring a plurality of device data which are stored in a database cluster and correspond to the target physical device; analyzing the data of each device, and determining whether the data of each device contains abnormal data; and when the data of each device contains abnormal data, generating a maintenance strategy corresponding to the target physical device according to the abnormal data, and visually displaying the maintenance strategy. According to the scheme of the embodiment of the invention, the fault of the physical equipment can be rapidly detected, the maintenance strategy can be generated in time, and a large amount of labor cost is saved.
Description
Technical Field
The present invention relates to computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for detecting a failure of a physical device.
Background
In the industrial production field, production and manufacturing equipment (physical equipment) is numerous, the equipment operates every day, the performance and the state of the equipment need to be monitored and diagnosed frequently, and production stagnation caused by sudden failure of the equipment is avoided, so that serious loss is caused.
At present, the fault of each device is mainly determined in a manual inspection mode, the fault of the device is difficult to diagnose in time, and a large amount of labor cost is needed.
How to monitor each physical device and quickly detect the fault generated by the physical device is a key problem concerned by the personnel in the industry.
Disclosure of Invention
Embodiments of the present invention provide a method, an apparatus, a device, and a storage medium for detecting a fault of a physical device, so as to quickly detect the fault of the physical device, generate a maintenance policy in time, and save a large amount of labor cost.
In a first aspect, an embodiment of the present invention provides a method for detecting a fault of a physical device, where the method includes:
responding to a fault query instruction of a target physical device, and acquiring a plurality of device data which are stored in a database cluster and correspond to the target physical device;
analyzing the equipment data to determine whether the equipment data contain abnormal data;
and when the equipment data contain abnormal data, generating a maintenance strategy corresponding to the target physical equipment according to the abnormal data, and visually displaying the maintenance strategy.
In a second aspect, an embodiment of the present invention further provides a device for detecting a failure of a physical device, where the device includes:
the device data acquisition module is used for responding to a fault query instruction of a target physical device and acquiring a plurality of device data which are stored in a database cluster and correspond to the target physical device;
the device data analysis module is used for analyzing each device data and determining whether each device data contains abnormal data;
and the maintenance strategy generation module is used for generating a maintenance strategy corresponding to the target physical device according to the abnormal data and visually displaying the maintenance strategy when the data of each device contains abnormal data.
In a third aspect, an embodiment of the present invention further provides a device for detecting a failure of a physical device, where the device for detecting a failure of a physical device includes:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for detecting a failure of a physical device according to any embodiment of the present invention.
In a fourth aspect, embodiments of the present invention further provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method for fault detection of a physical device according to any one of the embodiments of the present invention.
The method comprises the steps of obtaining a plurality of device data corresponding to target physical devices and stored in a database cluster by responding to a fault query instruction of the target physical devices; analyzing the data of each device, and determining whether the data of each device contains abnormal data; when the data of each device contains abnormal data, a maintenance strategy corresponding to the target physical device is generated according to the abnormal data, the maintenance strategy is visually displayed, faults of the physical device can be rapidly detected, the maintenance strategy is generated in time, and a large amount of labor cost is saved.
Drawings
Fig. 1 is a flowchart of a method for detecting a failure of a physical device according to a first embodiment of the present invention;
fig. 2 is a flowchart of a method for detecting a failure of a physical device according to a second embodiment of the present invention;
fig. 3 is a flowchart of a method for detecting a failure of a physical device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a fault detection apparatus of a physical device according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of a fault detection device of a physical device in a fifth embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad invention. It should be further noted that, for convenience of description, only some structures, not all structures, relating to the embodiments of the present invention are shown in the drawings.
Example one
Fig. 1 is a flowchart of a method for detecting a failure of a physical device in an embodiment of the present invention, where the embodiment is applicable to a case of detecting a failure of a physical device, and the method may be executed by a failure detection apparatus of a physical device, where the apparatus may be implemented in a software and/or hardware manner and integrated in a failure detection device of a physical device, and in this embodiment, the failure detection device of a physical device may be a computer, a server, a tablet computer, or the like. Specifically, referring to fig. 1, the method specifically includes the following steps:
The target physical device may be any working device in industrial production, such as a manufacturing robot, a warehouse management device, a three-phase motor, or a voltage sensor, which is not limited in this embodiment.
In an optional implementation manner of this embodiment, when a fault query instruction of a target physical device is received, multiple device data corresponding to the target physical device may be obtained in a database cluster, where the multiple device data corresponding to the target physical device are historical data generated by the target physical device at a historical time, for example, data such as a work log, boot time, or working duration.
It should be noted that, in this embodiment, the database cluster stores the device data of all the physical devices, and the device data generated by the target physical device can be acquired at any time according to the uniform identification code of the target physical device.
And step 120, analyzing the data of each device to determine whether the data of each device contains abnormal data.
In an optional implementation manner of this embodiment, after a plurality of pieces of device data corresponding to the target physical device are acquired, each piece of acquired device data may be further analyzed, and whether the acquired device data includes abnormal data or not may be determined according to an analysis result.
In an optional implementation manner of this embodiment, the acquired device data may be sequentially compared with the standard data, and the abnormal data may be determined according to the comparison result; for example, if the difference between a piece of equipment data and standard data is greater than a certain value, the piece of equipment data can be determined to be abnormal data; the standard data may be an average value or a median value of a plurality of abnormal-free device data, and the like, which is not limited in this embodiment.
And step 130, when the data of each device contains abnormal data, generating a maintenance strategy corresponding to the target physical device according to the abnormal data, and visually displaying the maintenance strategy.
In an optional implementation manner of this embodiment, if it is determined that the acquired device data includes abnormal data, a maintenance policy corresponding to the target physical device may be generated according to the abnormal data; for example, if the abnormal data is trojan data, a maintenance strategy for completely deleting the data can be generated; if the number of the abnormal data exceeds a set threshold value, for example, 100, an instruction for suspending the operation of the target physical device may be generated, and the target physical device may be started after the maintenance of the related personnel.
Furthermore, after the maintenance strategy is generated, the maintenance strategy can be visually displayed in the display area, so that related personnel can be prompted to perform subsequent operation, and a large amount of time is saved.
According to the scheme of the embodiment, a plurality of pieces of equipment data corresponding to the target physical equipment and stored in the database cluster are acquired by responding to the fault query instruction of the target physical equipment; analyzing the data of each device, and determining whether the data of each device contains abnormal data; when the data of each device contains abnormal data, a maintenance strategy corresponding to the target physical device is generated according to the abnormal data, the maintenance strategy is visually displayed, faults of the physical device can be rapidly detected, the maintenance strategy is generated in time, and a large amount of labor cost is saved.
Example two
Fig. 2 is a flowchart of a method for detecting a failure of a physical device in a second embodiment of the present invention, which is a further refinement of the above technical solutions, and the technical solution in this embodiment may be combined with various alternatives in one or more of the above embodiments. As shown in fig. 2, the method for detecting a failure of a physical device may include the steps of:
and step 210, dynamically deploying the database cluster.
The database cluster stores all the equipment data generated by the historical time of the physical equipment.
In an implementation manner of this embodiment, before responding to the fault query instruction of the target physical device, the database cluster may be further dynamically deployed; optionally, dynamically deploying the database cluster may include: when the quantity of the equipment data stored in the current database server is larger than a set storage threshold value, deploying a new database server through a pre-deployed middleware; the newly generated device data is stored by the new database server.
The storage threshold may be set to be 1 hundred million, 2 hundred million, or 5 million, which is related to the storage capacity of the database server, and the specific value is not limited in this embodiment.
In an optional implementation manner of this embodiment, the device data stored in the current database may be detected in real time, and when the number of the device data stored in the current database server is greater than the set storage threshold, a new database server may be deployed through a pre-deployed middleware, and further, newly generated device data may be stored through the new database server.
The method has the advantages that space storage of newly generated equipment data can be guaranteed, the phenomenon of data overflow is avoided, a large number of database servers do not need to be deployed in advance, and waste of server resources is avoided.
In a specific example of this embodiment, according to the operation condition of the current plant equipment, 3 database servers are initially deployed, cluster deployment is implemented by using the Mycat middleware, and the 3 MySQL databases are dynamically configured and managed. Due to the increase of factory equipment, the data volume collected and stored every day is very large, after a period of time, the storage space of 3 servers is found to be full quickly, server expansion needs to be carried out dynamically, but the current operation service cannot be influenced, the data collection and storage process cannot be interrupted, and at the moment, the database server can be increased by utilizing the dynamic expansion configuration of Mycat.
The specific deployment flow in the above example may be: 1. deploying a physical MySQL database; 2. installing Mycat middleware; 3. and configuring the Mycat middleware, wherein the Mycat middleware can configure nodes and set fragment rules for the database server, such as modulus fragments and range fragments.
It should be noted that, in this embodiment, the program or service is not directly connected to the database server, but is connected to the logical database configured by the Mycat middleware, the database server is transparent to the program or service, the management, configuration, storage, and the like of the database are managed by the Mycat middleware, and the program or service does not need to pay attention to the details read by the database server.
In an optional implementation manner of this embodiment, the obtaining, in response to a fault query instruction of a target physical device, a plurality of device data stored in a database cluster and corresponding to the target physical device may include: determining attribute information of the target physical device contained in the query instruction, wherein the attribute information includes at least one of the following items: the unified identification code of the target physical device, the time for generating device data and the type of the device data; and acquiring a plurality of pieces of equipment data corresponding to the attribute information and stored in the database cluster.
Exemplarily, when a fault query instruction of a target physical device is obtained, a uniform identification code of the target physical device included in the fault query instruction and a time for generating device data may be determined; further, all the device data corresponding to the unified identification code of the target physical device are obtained in the database, and the device data corresponding to the time of the generated device data are obtained through screening in the device data and serve as the device data corresponding to the fault query instruction of the target physical device.
And step 230, inputting the data of each device into a machine learning model trained in advance, and outputting abnormal data.
Wherein, the machine learning model is obtained by training a plurality of abnormal-free equipment data.
In an optional implementation manner of this embodiment, abnormal-free device data generated by each physical device may be input into the neural network model, so as to obtain the machine learning model involved in this embodiment; the neural network model may be a classification model or other neural network models, which is not limited in this embodiment.
In an optional implementation manner of this embodiment, after a plurality of pieces of device data corresponding to a target physical device stored in a database cluster are acquired, each piece of device data may be input into a machine learning model trained in advance, and then abnormal data is output through the machine model; in this embodiment, the machine learning model may directly output the serial number of the abnormal data, or may output the abnormal data itself, which is not limited in this embodiment.
And 240, when the data of each device contains abnormal data, generating a maintenance strategy corresponding to the target physical device according to the abnormal data, and visually displaying the maintenance strategy.
In this embodiment, when the device data includes abnormal data, that is, when the machine learning model outputs abnormal data, a maintenance policy corresponding to the target physical device may be generated according to the abnormal data output by the machine learning model, and the generated maintenance policy may be visually displayed in the display area.
In an optional implementation manner of this embodiment, a maintenance policy corresponding to the target physical device may be generated according to the attribute feature of the abnormal data; wherein the attribute features include: type, size, or generation time; the maintenance strategy comprises the following steps: and suspending the work of the target physical equipment and prompting related personnel to carry out maintenance in the visual interface, or keeping the target physical equipment to continue working and prompting the target physical equipment to have fault risk in the visual interface.
For example, if the type of the detected abnormal data is attack data, the generation of the maintenance strategy corresponding to the abnormal data may be to suspend the work of the target physical device and prompt the relevant personnel to perform maintenance in the visual interface; if the type of the detected abnormal data is non-attack data and the generated time is working time, generating a maintenance strategy corresponding to the abnormal data can be used for keeping the target physical device to continue working and prompting the target physical device to have fault risk in a visual interface.
According to the scheme of the embodiment, the maintenance strategy corresponding to the target physical equipment is generated according to the attribute characteristics of the abnormal data, the maintenance strategy can be generated quickly, the time for maintaining the equipment fault is reduced, and the economic benefit of a factory caused by the equipment fault is reduced.
On the basis of the technical scheme, after a plurality of pieces of equipment data corresponding to the target physical equipment and stored in a database cluster are acquired, each piece of equipment data can be input into the machine learning model to predict the fault of the target physical equipment.
It should be noted that the machine learning model in this embodiment may not only determine whether the device data contains abnormal data; the failure of the target physical device can also be predicted according to the input device data, for example, the probability of failure of the target physical device in the next few days can be predicted, so as to remind relevant personnel to prepare for maintenance in advance.
The method has the advantages that the fault of the physical equipment can be predicted in advance, the fault of the physical equipment can be solved in advance, and a large amount of time and economic resources are saved.
EXAMPLE III
Fig. 3 is a flowchart of a method for detecting a failure of a physical device in a third embodiment of the present invention, where this embodiment is a further refinement of the above technical solutions, and the technical solution in this embodiment may be combined with various alternatives in one or more of the above embodiments. As shown in fig. 3, the method for detecting a failure of a physical device may include the steps of:
and step 310, dynamically deploying the database cluster.
And step 320, responding to the fault query instruction of the target physical device, and acquiring a plurality of device data corresponding to the target physical device and stored in the database cluster.
And 330, comparing the data of each device with a pre-established baseline respectively, and determining that the data of the target device is abnormal data when the distance between the data of the target device and the baseline is greater than a set threshold value.
Wherein the baseline is determined by modeling a plurality of anomaly-free device data.
In an optional implementation manner of this embodiment, the abnormal-device-free data generated by each physical device may be input into the objective function for calculation, so as to obtain the baseline involved in this embodiment; the objective function may be any function, which is not limited in this embodiment.
In an optional implementation manner of this embodiment, after a plurality of pieces of device data corresponding to the target physical device and stored in the database cluster are acquired, each piece of device data may be sequentially compared with the baseline, and when a distance between the target device data and the baseline is greater than a set threshold, it may be determined that the target device data is abnormal data; the set threshold may be 1, 5, 20, or the like, and is not limited in this embodiment.
And 340, when the data of each device contains abnormal data, generating a maintenance strategy corresponding to the target physical device according to the abnormal data, and visually displaying the maintenance strategy.
According to the scheme of the embodiment, the data of each device can be compared with the pre-established baseline, when the distance between the data of the target device and the baseline is larger than the set threshold, the data of the target device is determined to be abnormal data, the abnormal data can be rapidly determined, and a basis is provided for detecting the fault of the target physical device.
Example four
Fig. 4 is a schematic structural diagram of a fault detection apparatus of a physical device in a fourth embodiment of the present invention, which may execute the fault detection method of the physical device in each of the embodiments. Referring to fig. 4, the apparatus includes: a device data acquisition module 410, a device data analysis module 420, and a maintenance policy generation module 430.
The device data acquiring module 410 is configured to acquire, in response to a fault query instruction of a target physical device, a plurality of device data corresponding to the target physical device, which are stored in a database cluster;
the device data analysis module 420 is configured to analyze each piece of device data to determine whether each piece of device data includes abnormal data;
and a maintenance policy generation module 430, configured to, when each piece of device data includes abnormal data, generate a maintenance policy corresponding to the target physical device according to the abnormal data, and visually display the maintenance policy.
According to the scheme of the embodiment, a device data acquisition module responds to a fault query instruction of target physical devices to acquire a plurality of device data corresponding to the target physical devices and stored in a database cluster; analyzing each piece of equipment data through an equipment data analysis module, and determining whether each piece of equipment data contains abnormal data; the maintenance strategy corresponding to the target physical equipment is generated according to the abnormal data through the maintenance strategy generation module, and the maintenance strategy is visually displayed, so that the fault of the physical equipment can be quickly detected, the maintenance strategy is generated in time, and a large amount of labor cost is saved.
In an optional implementation manner of this embodiment, the apparatus for detecting a failure of a physical device further includes: the database cluster deployment module is used for dynamically deploying the database cluster; the database cluster stores equipment data generated by historical time of all physical equipment;
the database cluster deployment module is specifically used for deploying a new database server through a pre-deployed middleware when the quantity of the equipment data stored in the current database server is greater than a set storage threshold; the newly generated device data is stored by the new database server.
In an optional implementation manner of this embodiment, the device data obtaining module 410 is specifically configured to determine attribute information of the target physical device included in the query instruction, where the attribute information includes at least one of the following items: the unified identification code of the target physical device, the time for generating device data and the type of the device data; and acquiring a plurality of pieces of equipment data corresponding to the attribute information and stored in the database cluster.
In an optional implementation manner of this embodiment, the device data analysis module 420 is specifically configured to input each device data into a machine learning model trained in advance, and output abnormal data; wherein, the machine learning model is obtained by training a plurality of abnormal-free equipment data.
In an optional implementation manner of this embodiment, the apparatus for detecting a failure of a physical device further includes: and the prediction module is used for inputting the data of each device into the machine learning model and predicting the fault of the target physical device.
In an optional implementation manner of this embodiment, the device data analysis module 420 is further specifically configured to compare each piece of device data with a pre-established baseline, and determine that the target device data is abnormal data when a distance between the target device data and the baseline is greater than a set threshold; wherein the baseline is determined by modeling a plurality of anomaly-free device data.
In an optional implementation manner of this embodiment, the maintenance policy generation module 430 generates, by the specific voice, a maintenance policy corresponding to the target physical device according to the attribute feature of the abnormal data;
wherein the attribute features include: type, size, or generation time;
the maintenance strategy comprises: and suspending the work of the target physical equipment and prompting related personnel to maintain in a visual interface, or keeping the target physical equipment to continue working and prompting the target physical equipment to have fault risk in the visual interface.
The fault detection device of the physical equipment provided by the embodiment of the invention can execute the fault detection method of the physical equipment provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
EXAMPLE five
Fig. 5 is a schematic structural diagram of a fault detection device of a physical device according to a fifth embodiment of the present invention, and as shown in fig. 5, the fault detection device of the physical device includes a processor 50, a memory 51, an input device 52, and an output device 53; the number of processors 50 in the fault detection device of the physical device may be one or more, and one processor 50 is taken as an example in fig. 5; the processor 50, the memory 51, the input device 52 and the output device 53 in the fault detection device of the physical device may be connected by a bus or other means, and the connection by the bus is exemplified in fig. 5.
The memory 51 is used as a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the fault detection method of the physical device in the embodiment of the present invention (for example, the device data acquisition module 410, the device data analysis module 420, and the maintenance policy generation module 430 in the fault detection apparatus of the physical device). The processor 50 executes various functional applications and data processing of the fault detection device of the physical device by executing software programs, instructions and modules stored in the memory 51, that is, implements the fault detection method of the physical device described above.
The memory 51 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 51 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 51 may further include memory located remotely from processor 50, which may be connected to the failure detection device of the physical device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 52 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the failure detection apparatus of the physical apparatus. The output device 53 may include a display device such as a display screen.
EXAMPLE six
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a method for fault detection of a physical device, the method including:
responding to a fault query instruction of a target physical device, and acquiring a plurality of device data which are stored in a database cluster and correspond to the target physical device;
analyzing the equipment data to determine whether the equipment data contain abnormal data;
and when the equipment data contain abnormal data, generating a maintenance strategy corresponding to the target physical equipment according to the abnormal data, and visually displaying the maintenance strategy.
Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in the fault detection method for the physical device provided by any embodiments of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the fault detection apparatus for physical devices, each unit and each module included in the fault detection apparatus are only divided according to functional logic, but are not limited to the above division, as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method of fault detection for a physical device, comprising:
responding to a fault query instruction of a target physical device, and acquiring a plurality of device data which are stored in a database cluster and correspond to the target physical device;
analyzing the equipment data to determine whether the equipment data contain abnormal data;
and when the equipment data contain abnormal data, generating a maintenance strategy corresponding to the target physical equipment according to the abnormal data, and visually displaying the maintenance strategy.
2. The method of claim 1, further comprising, prior to querying the instructions in response to a failure of the target physical device:
dynamically deploying the database cluster; the database cluster stores equipment data generated by historical time of all physical equipment;
the dynamically deploying the database cluster includes:
when the quantity of the equipment data stored in the current database server is larger than a set storage threshold value, deploying a new database server through a pre-deployed middleware;
the newly generated device data is stored by the new database server.
3. The method of claim 1, wherein the retrieving, in response to the failure query instruction of the target physical device, a plurality of device data stored in a database cluster corresponding to the target physical device comprises:
determining attribute information of the target physical device contained in the query instruction, wherein the attribute information includes at least one of the following items: the unified identification code of the target physical device, the time for generating device data and the type of the device data;
and acquiring a plurality of pieces of equipment data corresponding to the attribute information and stored in the database cluster.
4. The method of claim 1, wherein analyzing each of the device data to determine whether each of the device data includes abnormal data comprises:
inputting the data of each device into a machine learning model trained in advance, and outputting abnormal data;
wherein, the machine learning model is obtained by training a plurality of abnormal-free equipment data.
5. The method of claim 4, further comprising, after obtaining the plurality of device data corresponding to the target physical device stored in the database cluster:
and inputting the data of each device into the machine learning model, and predicting the fault of the target physical device.
6. The method of claim 1, wherein analyzing each of the device data to determine whether each of the device data includes abnormal data comprises:
respectively comparing the equipment data with a pre-established baseline, and determining that the target equipment data are abnormal data when the distance between the target equipment data and the baseline is greater than a set threshold value;
wherein the baseline is determined by modeling a plurality of anomaly-free device data.
7. The method according to claim 1, wherein when each of the device data includes abnormal data, generating a maintenance policy corresponding to the target physical device according to the abnormal data, and visually displaying the maintenance policy, includes:
generating a maintenance strategy corresponding to the target physical equipment according to the attribute characteristics of the abnormal data;
wherein the attribute features include: type, size, or generation time;
the maintenance strategy comprises: and suspending the work of the target physical equipment and prompting related personnel to maintain in a visual interface, or keeping the target physical equipment to continue working and prompting the target physical equipment to have fault risk in the visual interface.
8. A failure detection apparatus of a physical device, comprising:
the device data acquisition module is used for responding to a fault query instruction of a target physical device and acquiring a plurality of device data which are stored in a database cluster and correspond to the target physical device;
the device data analysis module is used for analyzing each device data and determining whether each device data contains abnormal data;
and the maintenance strategy generation module is used for generating a maintenance strategy corresponding to the target physical device according to the abnormal data and visually displaying the maintenance strategy when the data of each device contains abnormal data.
9. A failure detection device of a physical device, characterized in that the failure detection device of the physical device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method of fault detection for a physical device as claimed in any one of claims 1 to 7.
10. A storage medium containing computer-executable instructions for performing the fault detection device method of a physical device as claimed in any one of claims 1 to 7 when executed by a computer processor.
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