CN110738352A - Maintenance dispatching management method, device, equipment and medium based on fault big data - Google Patents
Maintenance dispatching management method, device, equipment and medium based on fault big data Download PDFInfo
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- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
Abstract
The invention provides maintenance management methods, devices, equipment and media based on fault big data, which comprise the steps of monitoring equipment data in real time, obtaining equipment maintenance records and equipment operation data, obtaining network equipment operation fault data, judging the equipment operation data according to the network equipment operation fault data, estimating the operation data with faults, predicting times of fault occurrence time of the equipment according to historical time of the equipment faults, obtaining prediction time, generating a maintenance list according to the prediction time and the operation data with the faults, arranging maintenance personnel according to the maintenance list, and immediately informing the maintenance personnel to maintain the equipment when the equipment faults are monitored.
Description
Technical Field
The invention relates to the field of invoice management, in particular to maintenance dispatching management methods, devices, equipment and media based on fault big data.
Background
At present, when a problem occurs in a device, a typical repair and maintenance process is that when a certain device fails, a user informs the logistics unit of the name and the location of the failed device by means of a telephone or a network platform, and also informs the maintenance unit of surface failure conditions such as a blue screen for computer startup, no response to computer startup, sudden black screen of a computer during use, and the like, and then the time of maintenance and the contact way and the contact person are left, and the contact person only needs to wait for the maintenance person .
However, many devices at the present stage lack the capability of predicting faults, the faults cannot be predicted in advance, and corresponding personnel can not be arranged to wait, and when the faults occur, the personnel can not be reasonably arranged to maintain the devices in time.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
In view of this, the invention provides maintenance dispatching management methods, devices, equipment and media based on fault big data, and aims to solve the technical problem that in the prior art, workers cannot be timely and reasonably arranged to maintain the equipment.
The technical scheme of the invention is realized as follows:
, the invention provides maintenance dispatching management methods based on fault big data, which comprises the following steps:
s1, real-time monitoring is carried out on the equipment data, and equipment maintenance records and equipment operation data are obtained, wherein the equipment maintenance records comprise: historical time of equipment failure;
s2, obtaining operation fault data of the network equipment, judging the operation data of the equipment according to the operation fault data of the network equipment, estimating the operation data with faults, predicting the next times of fault occurrence time of the equipment according to the historical time of the fault occurrence of the equipment, obtaining the predicted time, and generating a maintenance order according to the predicted time and the operation data with faults;
and S3, arranging maintenance personnel according to the maintenance order, and immediately informing the maintenance personnel to carry out maintenance when the equipment is detected to be out of order.
On the basis of the above technical solution, preferably, in step S2, obtaining operation failure data of the network device, determining the operation data of the device according to the operation failure data of the network device, and estimating the operation data with a failure, and further including the steps of obtaining operation failure data of the network device, setting a data failure determination range according to the operation failure data of the network device, determining the operation data of the device according to the data failure determination range, and determining that the operation data of the device has a failure when the operation data of the device is within the data failure determination range; and when the equipment operation data is not in the data fault judgment range, judging that the equipment operation data has no fault.
Based on the above technical solution, preferably, in step S2, the next times of failure occurrence time of the device is predicted according to the history time of the device failure occurrence to obtain the predicted time, and the method further includes the steps of obtaining the history time of the device failure occurrence from the device maintenance record, calculating the history time of the device failure occurrence two by two, obtaining the time interval of the failure occurrence time of the device, calculating the average time interval of the time interval, and predicting the next times of failure occurrence time of the device according to the average time interval to obtain the predicted time.
Based on the above technical solution, preferably, in step S2, a repair order is generated according to the predicted time and the operation data with faults, and the method further includes the steps of acquiring the time corresponding to the operation data with faults, comparing the time corresponding to the operation data with faults with the predicted time, when the time corresponding to the operation data with faults does not meet the predicted time, re-acquiring the time corresponding to the next operation data with faults, comparing the time corresponding to the operation data with faults with the predicted time, and when the time corresponding to the operation data with faults meets the predicted time, marking the predicted time and the operation data with faults simultaneously and generating the repair order.
On the basis of the above technical solution, preferably, in step S1, the device data is monitored in real time, and a device maintenance record and device operation data are obtained, where the device maintenance record further includes: the equipment information data, the equipment repair fault data and the equipment actual fault data.
On the basis of the above technical solution, preferably, in step S3, a maintenance person is arranged according to the maintenance order, and after the maintenance person is notified immediately to perform maintenance when it is monitored that the equipment has a fault, the method further includes the steps of setting a time threshold for the arrival of the maintenance person, arranging the maintenance person according to the maintenance order, notifying the maintenance person to perform maintenance on the equipment when it is monitored that the equipment has a fault, and simultaneously acquiring position information of the maintenance person in real time, acquiring time spent by the maintenance person when the maintenance person reaches the position of the equipment, comparing the time with the time threshold, and performing overtime warning on the maintenance person when the time is greater than the time threshold; and when the time is less than the time threshold, acquiring the information data of the equipment which is repaired.
On the basis of the above technical solution, preferably, when the time is less than the time threshold, after the equipment data of the maintenance completion is acquired, the method further includes the steps of acquiring the equipment information data, the equipment repair failure data, and the equipment actual failure data of the same equipment in the equipment maintenance record according to the equipment information data of the maintenance completion, comparing the equipment information data of the maintenance completion with the equipment information data in the equipment maintenance record, and when the equipment information data of the maintenance completion is different from the equipment information data in the equipment maintenance record, acquiring the equipment information data of the maintenance completion, the equipment repair failure data, and the equipment actual failure data, and storing the equipment information data, the equipment repair failure data, and the equipment actual failure data in the equipment maintenance record.
Further , the repair dispatch management device based on big data of failure preferably includes:
the acquisition module is used for monitoring the equipment data in real time, acquiring the equipment maintenance record and the equipment operation data, wherein the equipment maintenance record comprises: historical time of equipment failure;
the maintenance order generation module is used for acquiring operation fault data of the network equipment, judging the equipment operation data according to the operation fault data of the network equipment, estimating the operation data with faults, predicting the fault occurrence time of times of the equipment according to the historical time of the equipment faults, acquiring the predicted time, and generating a maintenance order according to the predicted time and the operation data with faults;
and the order sending module is used for arranging maintenance personnel according to the maintenance order, and immediately informing the maintenance personnel to carry out maintenance when the equipment is monitored to have a fault.
In a second aspect, the maintenance dispatching management method based on big data of faults further comprises kinds of equipment, wherein the equipment comprises a memory, a processor and a maintenance dispatching management method program based on big data of faults, the maintenance dispatching management method program based on big data of faults is stored in the memory and can run on the processor, and the maintenance dispatching management method program based on big data of faults is configured to realize the steps of the maintenance dispatching management method based on big data of faults.
In a third aspect, the maintenance dispatching management method based on big data of failure further includes media, where the media are computer media, the computer media stores a maintenance dispatching management method program based on big data of failure, and the maintenance dispatching management method program based on big data of failure realizes the steps of the maintenance dispatching management method based on big data of failure as described above when executed by a processor.
Compared with the prior art, the maintenance dispatching management method based on fault big data has the following beneficial effects:
(1) the method comprises the steps of obtaining operation fault data of various equipment from a network, monitoring the equipment data in real time, obtaining the operation data of the equipment, predicting the operation data of the equipment according to the operation fault data of the various equipment, obtaining the operation data of the equipment with faults, determining the time interval of the equipment faults according to the historical fault occurrence time of the equipment, and determining times of fault occurrence time of the equipment according to the time interval, so that predictions can be carried out on the time and the data of the equipment which are likely to have faults, maintenance personnel can be arranged on the equipment timely and reasonably, and the equipment can be maintained at th time;
(2) by the method, the equipment can be maintained at the th time, user experience is improved, and meanwhile, the situation that the equipment is not maintained by people due to unreasonable arrangement of the personnel and no person due to the fact that the equipment fails is avoided.
Drawings
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, it is obvious that the drawings in the following description are only embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic diagram of an apparatus in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram illustrating a repair dispatch management method according to an embodiment of the present invention;
fig. 3 is a functional block diagram of a repair dispatch management method according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely in the following description of the embodiments of the present invention, and it is obvious that the described embodiments are only partial embodiments of of the present invention, rather than all embodiments.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the device, and that in actual implementations the device may include more or less components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, the storage 1005, which is media, may include therein an operating system, a network communication module, a user interface module, and a repair dispatch management method program based on failure big data.
In the device shown in fig. 1, the network interface 1004 is mainly used for establishing a communication connection between the device and a server storing all data required in the maintenance management method system based on failure big data; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the maintenance dispatching management method device based on the fault big data can be arranged in the maintenance dispatching management method device based on the fault big data, the maintenance dispatching management method device based on the fault big data calls the maintenance dispatching management method program based on the fault big data stored in the memory 1005 through the processor 1001, and the maintenance dispatching management method based on the fault big data provided by the implementation of the invention is executed.
Referring to fig. 2, fig. 2 is a schematic flow chart of a repair dispatch management method according to the present invention.
In this embodiment, the maintenance dispatching management method based on the fault big data includes the following steps:
s10: carry out real time monitoring to equipment data to acquire equipment maintenance record and equipment operation data, equipment maintenance record includes: historical time of device failure.
It should be understood that the monitoring of the equipment is performed while the operation data of the equipment and the maintenance record of the equipment are obtained in real time, and the maintenance record of the equipment comprises: the device information data, the history time of the device failure, the device repair failure data and the actual device failure data, wherein the device repair failure data refers to the failure data reported by the device user, such as: blue screen, dead halt and black screen, and the actual fault data of the equipment refers to fault data filled after being checked by a maintenance person, such as: hard disk damage, poor interface contact, and plug drop.
And S20, acquiring operation fault data of the network equipment, judging the operation data of the equipment according to the operation fault data of the network equipment, estimating the operation data with faults, predicting the next times of fault occurrence time of the equipment according to the historical time of the fault occurrence of the equipment, acquiring the predicted time, and generating a maintenance order according to the predicted time and the operation data with faults.
It should be understood that, firstly, the operation data of the device is judged, whether the operation data of the device has a fault is judged, the specific judgment process is as follows, the operation fault data of the network device is obtained, the data judgment range of the operation fault of the device is set according to the operation fault data of each device in the network, if the data in the range is the data, the device is indicated to have a fault, then the operation data of the device is judged according to the data judgment range, when the operation data of the device is in the data judgment range, the device is indicated to have a fault, when the operation data of the device is not in the data fault judgment range, the operation data of the device is judged to have no fault, by this way, the operation data of various devices can be judged to have a fault, and the fault data of the device can be identified at th time, thereby saving the time of maintenance personnel.
It should be understood that, the time interval of the equipment failure is obtained according to the historical time of the equipment failure, then the average time interval of the equipment failure is obtained according to the time interval, and the time point of the equipment failure is predicted according to the average time interval, and it should be noted that the time point of the equipment failure is predicted to be a plurality of time points, so that the time point of the equipment failure can be predicted, and the condition that a maintenance person cannot repair the equipment at th time when the equipment failure occurs can be effectively prevented.
It should be understood that, at this time, a maintenance order is generated according to the predicted time point of the equipment failure and the data of the equipment failure, and the specific method is implemented as follows, according to the predicted time point of the equipment failure, data corresponding to the time point is searched, if the data corresponding to the time point is failure operation data, the maintenance order is generated by combining the two, and if the data corresponding to the time point is not failure operation data, the equipment operation data of the next predicted time points are searched again, and by this way, the maintenance condition of the equipment can be effectively managed.
S30: and arranging maintenance personnel according to the maintenance list, and immediately informing the maintenance personnel to maintain when the equipment is monitored to be out of order.
It should be understood that, according to the maintenance order, the maintenance personnel is scheduled, when the equipment is detected to have a fault, the maintenance personnel is immediately notified to perform maintenance, and the maintenance personnel is also examined to see whether the maintenance personnel can perform maintenance on the equipment at time . specifically, in the following examination method, when the system detects that the equipment has the fault, arrival time thresholds are set, then the system obtains the arrival time of the maintenance personnel at the equipment to be maintained, compares the arrival time with the arrival time threshold, if the arrival time is greater than the arrival time threshold, it indicates that the maintenance personnel does not arrive at the site at time to perform maintenance on the equipment, and in the subsequent examination process, the maintenance personnel is warned, so as to improve the work efficiency of the maintenance personnel, and ensure that the equipment can perform maintenance at time when the fault occurs, thereby improving the user experience.
It should be understood that when a maintenance person arrives at the site within a limited time and completes the maintenance of the equipment, the system obtains the operation data of the equipment after the maintenance is completed, compares the operation data of the equipment with the equipment data in the equipment maintenance record, deletes the operation data of the equipment after the maintenance is completed if the data are the same, and adds the operation data of the equipment after the maintenance is completed into the equipment maintenance record if the data are not the same. By the method, the equipment operation data in the equipment maintenance record is continuously updated, and the equipment fault can be prevented from being processed more efficiently.
The above description is only for illustrative purposes and does not limit the technical solutions of the present application in any way.
According to the method, the equipment data are monitored in real time, the equipment maintenance records and the equipment operation data are obtained, the network equipment operation fault data are obtained, the equipment operation data are judged according to the network equipment operation fault data, the operation data with faults are estimated, the next times of fault occurrence time of the equipment is predicted according to the historical time of the equipment faults, the prediction time is obtained, a maintenance order is generated according to the prediction time and the operation data with faults, maintenance personnel are arranged according to the maintenance order, when the equipment faults are monitored, the maintenance personnel are immediately notified to carry out maintenance, the equipment is subjected to fault prediction by obtaining fault big data from the network, the time and the mode of the equipment possibly faults are predicted in advance, and the maintenance personnel are arranged in advance to stand by so as to guarantee that the equipment can be maintained at the time.
In addition, the embodiment of the invention also provides maintenance dispatching management devices based on fault big data, as shown in fig. 3, the maintenance dispatching management device based on fault big data comprises an obtaining module 10, a maintenance list generating module 20 and a dispatching module 30.
The obtaining module 10 is configured to perform real-time monitoring on device data, and obtain a device maintenance record and device operation data, where the device maintenance record includes: historical time of equipment failure;
the maintenance order generation module 20 is configured to obtain operation failure data of the network device, judge the operation data of the device according to the operation failure data of the network device, estimate operation data with a failure, predict time of occurrence of times of failures of the device according to historical time of occurrence of the failure of the device, obtain predicted time, and generate a maintenance order according to the predicted time and the operation data with the failure;
and the order sending module 30 is used for arranging maintenance personnel according to the maintenance order, and immediately informing the maintenance personnel to carry out maintenance when the equipment is monitored to have a fault.
In addition, it should be noted that the above-described embodiments of the apparatus are merely illustrative, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of the modules to implement the purpose of the embodiments according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment can be referred to the maintenance dispatch management method based on the fault big data provided in any embodiment of the present invention, and are not described herein again.
In addition, an kinds of media are also provided in an embodiment of the present invention, where the media are computer media, and the computer media store a maintenance dispatching management method program based on big failure data, and when executed by a processor, the maintenance dispatching management method program based on big failure data implements the following operations:
s1, real-time monitoring is carried out on the equipment data, and equipment maintenance records and equipment operation data are obtained, wherein the equipment maintenance records comprise: historical time of equipment failure;
s2, obtaining operation fault data of the network equipment, judging the operation data of the equipment according to the operation fault data of the network equipment, estimating the operation data with faults, predicting the next times of fault occurrence time of the equipment according to the historical time of the fault occurrence of the equipment, obtaining the predicted time, and generating a maintenance order according to the predicted time and the operation data with faults;
and S3, arranging maintenance personnel according to the maintenance order, and immediately informing the maintenance personnel to carry out maintenance when the equipment is detected to be out of order.
, the repair dispatch management method program based on big data of failure further realizes the following operations when executed by the processor:
acquiring network equipment operation fault data, setting a data fault judgment range according to the network equipment operation fault data, judging equipment operation data through the data fault judgment range, and judging that the equipment operation data has a fault when the equipment operation data is in the data fault judgment range; and when the equipment operation data is not in the data fault judgment range, judging that the equipment operation data has no fault.
, the repair dispatch management method program based on big data of failure further realizes the following operations when executed by the processor:
and the slave equipment maintenance record acquires the historical time of equipment failure, calculates the historical time of equipment failure pairwise, acquires the time interval of the failure occurrence time of the equipment, calculates the average time interval of the time interval, and predicts the failure occurrence time of the equipment for times according to the average time interval to acquire the predicted time.
, the repair dispatch management method program based on big data of failure further realizes the following operations when executed by the processor:
acquiring time corresponding to the operation data with faults, comparing the time corresponding to the operation data with the prediction time, when the time corresponding to the operation data with faults does not meet the prediction time, acquiring time corresponding to the next operation data with faults again, comparing the time corresponding to the operation data with the prediction time, and when the time corresponding to the operation data with faults meets the prediction time, marking the prediction time and the operation data with faults at the same time and generating a maintenance order.
, the repair dispatch management method program based on big data of failure further realizes the following operations when executed by the processor:
in step S1, the device data is monitored in real time, and a device maintenance record and device operation data are obtained, where the device maintenance record further includes: the equipment information data, the equipment repair fault data and the equipment actual fault data.
, the repair dispatch management method program based on big data of failure further realizes the following operations when executed by the processor:
setting a time threshold for arrival of maintenance personnel, arranging the maintenance personnel according to a maintenance order, informing the maintenance personnel to maintain the equipment when the equipment is monitored to be out of order, simultaneously acquiring position information of the maintenance personnel in real time, acquiring the time spent by the maintenance personnel when the maintenance personnel arrives at the position of the equipment, comparing the time with the time threshold, and giving overtime warning to the maintenance personnel when the time is greater than the time threshold; and when the time is less than the time threshold, acquiring the information data of the equipment which is repaired.
, the repair dispatch management method program based on big data of failure further realizes the following operations when executed by the processor:
and when the maintained equipment information data is different from the equipment information data in the equipment maintenance record, acquiring the maintained equipment information data, the equipment repair reporting fault data and the actual equipment fault data, and storing the acquired equipment information data, the equipment repair reporting fault data and the actual equipment fault data in the equipment maintenance record.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1, maintenance dispatching management method based on fault big data, which is characterized in that the method comprises the following steps;
s1, real-time monitoring is carried out on the equipment data, and equipment maintenance records and equipment operation data are obtained, wherein the equipment maintenance records comprise: historical time of equipment failure;
s2, obtaining operation fault data of the network equipment, judging the operation data of the equipment according to the operation fault data of the network equipment, estimating the operation data with faults, predicting the next times of fault occurrence time of the equipment according to the historical time of the fault occurrence of the equipment, obtaining the predicted time, and generating a maintenance order according to the predicted time and the operation data with faults;
and S3, arranging maintenance personnel according to the maintenance order, and immediately informing the maintenance personnel to carry out maintenance when the equipment is detected to be out of order.
2. The maintenance dispatch management method based on big data of failure as claimed in claim 1, characterized in that: in step S2, acquiring network device operation failure data, determining device operation data according to the network device operation failure data, and estimating the operation data with failure, and further including the steps of acquiring network device operation failure data, setting a data failure determination range according to the network device operation failure data, determining the device operation data according to the data failure determination range, and determining that the device operation data has failure when the device operation data is within the data failure determination range; and when the equipment operation data is not in the data fault judgment range, judging that the equipment operation data has no fault.
3. The maintenance dispatching management method based on the fault big data as claimed in claim 2, wherein in step S2, the next times of fault occurrence time of the equipment is predicted according to the historical time of the equipment fault occurrence to obtain the predicted time, and further comprising the steps of recording the historical time of the equipment fault occurrence from the equipment maintenance record, calculating the historical time of the equipment fault occurrence two by two to obtain the time interval of the fault occurrence time of the equipment, calculating the average time interval of the time interval, and predicting the next times of fault occurrence time of the equipment according to the average time interval to obtain the predicted time.
4. The maintenance dispatching management method based on the big data of the fault as claimed in claim 3, wherein in step S2, a maintenance order is generated according to the predicted time and the operation data with the fault, further comprising the steps of obtaining the time corresponding to the operation data with the fault, comparing the time corresponding to the operation data with the predicted time, when the time corresponding to the operation data with the fault does not meet the predicted time, obtaining the time corresponding to the next operation data with the fault again, comparing the time corresponding to the operation data with the predicted time, and when the time corresponding to the operation data with the fault meets the predicted time, marking the predicted time and the operation data with the fault at the same time and generating the maintenance order.
5. The maintenance dispatch management method based on big data of failure as claimed in claim 1, characterized in that: in step S1, the device data is monitored in real time, and a device maintenance record and device operation data are obtained, where the device maintenance record further includes: the equipment information data, the equipment repair fault data and the equipment actual fault data.
6. The maintenance dispatch management method based on big data of failure as claimed in claim 5, characterized in that: step S3, arranging maintenance personnel according to the maintenance list, immediately informing the maintenance personnel to maintain after monitoring that the equipment has a fault, setting a time threshold for the maintenance personnel to reach, arranging the maintenance personnel according to the maintenance list, informing the maintenance personnel to maintain the equipment when monitoring that the equipment has a fault, simultaneously acquiring the position information of the maintenance personnel in real time, acquiring the time spent by the maintenance personnel when the maintenance personnel reaches the position of the equipment, comparing the time with the time threshold, and giving an overtime warning to the maintenance personnel when the time is more than the time threshold; and when the time is less than the time threshold, acquiring the information data of the equipment which is repaired.
7. The maintenance dispatch management method based on big data of failure as claimed in claim 6, characterized in that: when the time is less than the time threshold, after the equipment data of the maintenance completion is obtained, the method further comprises the following steps of obtaining the equipment information data, the equipment repair failure data and the equipment actual failure data of the same equipment in the equipment maintenance record according to the equipment information data of the maintenance completion, comparing the equipment information data of the maintenance completion with the equipment information data in the equipment maintenance record, and obtaining the equipment information data of the maintenance completion, the equipment repair failure data and the equipment actual failure data and storing the equipment information data, the equipment repair failure data and the equipment actual failure data in the equipment maintenance record when the equipment information data of the maintenance completion is different from the equipment information data in the equipment maintenance record.
8, maintenance dispatching management device based on big data of trouble, its characterized in that, maintenance dispatching management device based on big data of trouble includes:
the acquisition module is used for monitoring the equipment data in real time, acquiring the equipment maintenance record and the equipment operation data, wherein the equipment maintenance record comprises: historical time of equipment failure;
the maintenance order generation module is used for acquiring operation fault data of the network equipment, judging the equipment operation data according to the operation fault data of the network equipment, estimating the operation data with faults, predicting the fault occurrence time of times of the equipment according to the historical time of the equipment faults, acquiring the predicted time, and generating a maintenance order according to the predicted time and the operation data with faults;
and the order sending module is used for arranging maintenance personnel according to the maintenance order, and immediately informing the maintenance personnel to carry out maintenance when the equipment is monitored to have a fault.
9, kinds of equipment, characterized in that the equipment comprises a memory, a processor and a big data failure based repair dispatch method program stored on the memory and operable on the processor, the big data failure based repair dispatch method program configured to implement the steps of the big data failure based repair dispatch method as claimed in any of claims 1 to 7 and .
10, kinds of media, characterized in that the media is a computer media, the computer media stores thereon a maintenance dispatching management method program based on big data of failure, the maintenance dispatching management method program based on big data of failure realizes the steps of the maintenance dispatching management method based on big data of failure as stated in any of claims 1 to 7 and when being executed by a processor.
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| CN112257423A (en) * | 2020-10-21 | 2021-01-22 | 北京工业大数据创新中心有限公司 | Equipment symptom information acquisition method and device and equipment operation and maintenance system |
| CN112395178A (en) * | 2020-11-18 | 2021-02-23 | 河南辉煌城轨科技有限公司 | Equipment fault prediction method |
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| CN115860278B (en) * | 2023-02-27 | 2023-04-28 | 深圳市利和兴股份有限公司 | Motor assembly production management method and system based on data analysis |
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