CN117974098A - Maintenance scheme generation method, device, equipment and medium based on maintenance time - Google Patents
Maintenance scheme generation method, device, equipment and medium based on maintenance time Download PDFInfo
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Abstract
The application relates to the technical field of equipment maintenance, and particularly discloses a maintenance scheme generation method, device, equipment and medium based on maintenance time. The method comprises the following steps: acquiring operation data of equipment; processing the operation data based on an abnormality detection model to obtain an operation state of the equipment; when the running state is abnormal, searching a predicted maintenance time length of the equipment in a preset maintenance time length prediction table based on the equipment type of the equipment; and generating a recommended maintenance scheme of the equipment based on the running state and the predicted maintenance duration. According to the method, the running state of the equipment is detected through the abnormality detection model, when the equipment runs abnormally, the predicted maintenance time of the equipment is searched according to the equipment type, and the recommended maintenance scheme of the equipment is generated by combining the predicted maintenance time of the equipment, so that maintenance personnel can clearly maintain time, maintenance work is reasonably arranged, time utilization efficiency is improved, and equipment maintenance efficiency is further improved.
Description
Technical Field
The present application relates to the field of equipment maintenance technologies, and in particular, to a maintenance scheme generating method, apparatus, device, and medium based on maintenance time.
Background
The digital operation and maintenance platform needs to fully utilize the leading edge technologies such as big data, artificial intelligence and the like to construct a one-stop intelligent big data platform, and provides comprehensive, prospective and active intelligent management and service. The existing operation and maintenance work lacks big data technical support, maintenance personnel only develop low-efficiency equipment maintenance work, and maintenance time cannot be reasonably arranged, so that equipment maintenance efficiency is reduced, and therefore how to improve the equipment maintenance efficiency becomes a problem to be solved urgently.
Disclosure of Invention
The application provides a maintenance scheme generation method, a device, equipment and a medium based on maintenance time, so as to improve the maintenance efficiency of the equipment.
In a first aspect, the present application provides a maintenance scheme generating method based on maintenance time, the method including:
Acquiring operation data of equipment;
processing the operation data based on an abnormality detection model to obtain an operation state of the equipment;
When the running state is abnormal, searching a predicted maintenance time length of the equipment in a preset maintenance time length prediction table based on the equipment type of the equipment;
And generating a recommended maintenance scheme of the equipment based on the running state and the predicted maintenance duration.
Further, when the running state is abnormal running, based on the equipment type of the equipment, before searching the predicted maintenance time of the equipment in a preset maintenance time prediction table, the method further includes:
based on the historical inspection information of the equipment, obtaining the predicted inspection duration of the equipment;
based on the historical maintenance information of the equipment, obtaining the predicted maintenance duration of the equipment;
Obtaining a predicted maintenance duration of the device based on the predicted inspection duration and the predicted maintenance duration;
and generating the maintenance duration prediction table based on the equipment type of the equipment and the predicted maintenance duration.
Further, the obtaining the predicted maintenance duration of the device based on the historical maintenance information of the device includes:
Calculating and obtaining the average maintenance duration of the equipment based on the maintenance duration, the maintenance result and the weighting factor of the fault type in the historical maintenance information;
based on the identification of the device, determining the type of the device, and based on the average maintenance duration of at least one device of the type, calculating and obtaining the predicted maintenance duration of the device of the type.
Further, the processing the operation data based on the abnormality detection model, before obtaining the operation state of the device, further includes:
Acquiring historical operation data and historical operation states of at least one device;
processing the historical operation data to generate a training data set;
and training a pre-training model based on the historical running state and the training data set to obtain the abnormality detection model.
Further, the processing the operation data based on the abnormality detection model, after obtaining the operation state of the device, further includes:
and when the running state is abnormal running, acquiring the abnormal information of the equipment, comparing a preset fault rule with the abnormal information, and determining the fault type of the equipment.
Further, the generating a recommended maintenance solution for the device based on the running state and the predicted maintenance duration includes:
Searching an initial maintenance scheme corresponding to the fault type in a historical maintenance scheme table based on the fault type;
determining maintenance start-stop time based on the predicted maintenance duration;
And generating the recommended maintenance scheme based on the initial maintenance scheme and the maintenance start-stop time.
Further, the processing the operation data based on the anomaly detection model to obtain an operation state of the device includes:
processing the operation data based on the abnormality detection model to obtain the abnormality times of the equipment in a preset time period;
and comparing the abnormal times with a preset abnormal times threshold value to obtain the running state.
In a second aspect, the present application further provides a maintenance scheme generating device based on maintenance time, where the device includes:
The operation data acquisition module is used for acquiring operation data of the equipment;
The running state obtaining module is used for processing the running data based on the abnormality detection model to obtain the running state of the equipment;
The predicted maintenance time length obtaining module is used for searching the predicted maintenance time length of the equipment in a preset maintenance time length prediction table based on the equipment type of the equipment when the running state is abnormal;
and the maintenance scheme generation module is used for generating a recommended maintenance scheme of the equipment based on the running state and the predicted maintenance duration.
In a third aspect, the present application also provides a computer device comprising a memory and a processor; the memory is used for storing a computer program; the processor is configured to execute the computer program and implement the maintenance scheme generating method based on maintenance time as described above when executing the computer program.
In a fourth aspect, the present application also provides a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to implement a maintenance scheme generation method based on maintenance time as described above.
The application discloses a maintenance scheme generation method, a device, equipment and a medium based on maintenance time, and operation data of the equipment are acquired; processing the operation data based on an abnormality detection model to obtain an operation state of the equipment; when the running state is abnormal, searching a predicted maintenance time length of the equipment in a preset maintenance time length prediction table based on the equipment type of the equipment; and generating a recommended maintenance scheme of the equipment based on the running state and the predicted maintenance duration. According to the method, the running state of the equipment is detected through the abnormality detection model, when the equipment runs abnormally, the predicted maintenance time of the equipment is searched according to the equipment type, and the recommended maintenance scheme of the equipment is generated by combining the predicted maintenance time of the equipment, so that maintenance personnel can clearly maintain time, maintenance work is reasonably arranged, time utilization efficiency is improved, and equipment maintenance efficiency is further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a first embodiment of a maintenance scenario generation method based on maintenance time provided by an embodiment of the present application;
FIG. 2 is a flow chart illustrating a second embodiment of a maintenance scenario generation method based on a maintenance time provided by an embodiment of the present application;
FIG. 3 is a flow chart illustrating a third embodiment of a maintenance scenario generation method based on a maintenance time provided by an embodiment of the present application;
FIG. 4 is a schematic block diagram of a maintenance scheme generating device based on maintenance time according to an embodiment of the present application;
Fig. 5 is a schematic block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
The embodiment of the application provides a maintenance scheme generation method, device, equipment and medium based on maintenance time. The maintenance scheme generating method based on the maintenance time can be applied to a server, and the recommended maintenance scheme of the equipment is generated by combining the predicted maintenance time of the equipment, so that the equipment maintenance efficiency is improved. The server may be an independent server or a server cluster.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic flowchart of a maintenance scheme generating method based on maintenance time according to an embodiment of the present application. The maintenance scheme generating method based on the maintenance time can be applied to a server and is used for generating a recommended maintenance scheme of the equipment by combining the predicted maintenance time length of the equipment, so that the equipment maintenance efficiency is improved.
As shown in fig. 1, the maintenance scheme generating method based on the maintenance time specifically includes steps S101 to S104.
S101, acquiring operation data of equipment.
In one embodiment, the operation data can be obtained through an operation data storage module carried by the device itself; the operational data may also be obtained by an external data monitoring module connected to the device.
S102, processing the operation data based on an abnormality detection model to obtain the operation state of the equipment.
Further, the processing the operation data based on the anomaly detection model to obtain an operation state of the device includes: processing the operation data based on the abnormality detection model to obtain the abnormality times of the equipment in a preset time period; and comparing the abnormal times with a preset abnormal times threshold value to obtain the running state.
In one embodiment, the operation data is subjected to serialization and normalization processing, and is used as an input parameter of an abnormality detection model, and is input into the abnormality detection model, and the abnormality detection model obtains and records the abnormality of the equipment at the current moment and obtains the abnormality times of the equipment in a preset time period by processing and analyzing the processed operation data.
In one embodiment, the number of anomalies is compared with a preset threshold number of anomalies, and when the number of anomalies is greater than or equal to the threshold number of anomalies, the operational state of the device is determined to be operational anomalies. For example, if the daily abnormality detection model detects that the number of times of abnormality of a certain device is 3 times and the threshold value of the number of times of abnormality is 2 times, the device is regarded as an operation abnormality device.
In one embodiment, the operational state of the device is determined to be a normal state when the number of anomalies is less than the threshold number of anomalies.
Further, the processing the operation data based on the abnormality detection model, before obtaining the operation state of the device, further includes: acquiring historical operation data and historical operation states of at least one device; processing the historical operation data to generate a training data set; and training a pre-training model based on the historical running state and the training data set to obtain the abnormality detection model.
In one embodiment, historical operating data (current, voltage, power, temperature, humidity, etc.) and historical operating status (operating abnormal, operating normal) for a plurality of different types of devices are obtained from a historical record of the devices. The historical operation data are subjected to serialization and normalization processing, labels are marked for each data to distinguish different data of different tasks, a training data set is obtained, and a pre-training model is trained by adopting a reconstruction-based method (LSTM (Long short-term memory network model), GRU (Gate Recurrent Unit, gating circulating unit), auto-Encoder (self-encoder) and the like).
In one embodiment, the pre-training model obtains a predicted running state of the device according to the training data set, compares the predicted running state with a historical running state to obtain a prediction accuracy of the pre-training model, and corrects the model until the prediction accuracy reaches a preset threshold when the prediction accuracy does not reach the preset threshold, and takes the pre-training model corresponding to the prediction accuracy as the anomaly detection model.
Further, the processing the operation data based on the abnormality detection model, after obtaining the operation state of the device, further includes: and when the running state is abnormal running, acquiring the abnormal information of the equipment, comparing a preset fault rule with the abnormal information, and determining the fault type of the equipment.
In one embodiment, the operational status of the device further includes anomaly information for the device. For equipment with abnormal operation state, acquiring abnormal information of the equipment based on the operation state, such as the operated time length of the equipment, the position of the equipment, the recent inspection condition of the equipment, the historical fault record of the equipment and the like.
In one embodiment, historical inspection and historical fault records of the equipment are obtained by inquiring historical work order information of the equipment, and potential correlation between historical operation time length of the equipment and occurrence reasons of faults is obtained through analysis according to the historical inspection records and the fault records. Meanwhile, through comprehensively analyzing the equipment of the same type and the equipment close to the position, the correlation between equipment inspection frequency and equipment failure frequency is obtained, and a failure rule is generated on historical worksheet data.
In one embodiment, anomaly information for the device is matched with fault rules to determine the type of fault for the device. For example, if the failure rule of the class a failure is that the operation time is greater than 24 hours, when the operation time is greater than 24 hours in the abnormality information of the device that is operating abnormally, the device may be classified as the class a failure.
S103, when the running state is abnormal, searching a predicted maintenance time length of the equipment in a preset maintenance time length prediction table based on the equipment type of the equipment.
In one embodiment, when the running state of the equipment is abnormal, acquiring the equipment type, and searching a predicted maintenance time length corresponding to the equipment type in a preset maintenance time length prediction table according to the equipment type.
It will be appreciated that the device type may be obtained by searching for the device identifier (number, name), or whether the device type of the current device exists in the device attribute.
In one embodiment, the predicted maintenance duration of a device is obtained according to the average patrol duration of the historical patrol task and the average maintenance duration of the historical maintenance task of the device. And counting the plurality of equipment types and the predicted maintenance time length of the equipment corresponding to each equipment type to generate a maintenance time length prediction table. When the equipment is subjected to abnormality detection, the equipment type of the equipment can be used for looking up a table to obtain the predicted maintenance time length corresponding to the equipment, so that the maintenance time length obtaining efficiency is improved, and the equipment maintenance efficiency is further improved.
S104, generating a recommended maintenance scheme of the equipment based on the running state and the predicted maintenance duration.
In one embodiment, abnormality information of the device is acquired according to an operation state of the device, and a fault type of the device is determined according to the abnormality information of the device.
In one embodiment, a historical maintenance scheme corresponding to a fault type of a device is determined in a historical maintenance scheme table according to the fault type of the device as an initial maintenance scheme. The history maintenance scheme comparison table comprises different fault types and history maintenance schemes corresponding to the fault types, and the history maintenance scheme table is generated by counting the maintenance schemes of a plurality of devices with different fault types.
In one embodiment, the predicted maintenance duration of the device is added to the initial maintenance scheme, and a recommended maintenance scheme of the device is obtained.
The embodiment provides a maintenance scheme generating method, a device, equipment and a medium based on maintenance time, and operation data of the equipment are obtained; processing the operation data based on an abnormality detection model to obtain an operation state of the equipment; when the running state is abnormal, searching a predicted maintenance time length of the equipment in a preset maintenance time length prediction table based on the equipment type of the equipment; and generating a recommended maintenance scheme of the equipment based on the running state and the predicted maintenance duration. According to the method, the running state of the equipment is detected through the abnormality detection model, when the equipment runs abnormally, the predicted maintenance time of the equipment is searched according to the equipment type, and the recommended maintenance scheme of the equipment is generated by combining the predicted maintenance time of the equipment, so that maintenance personnel can clearly maintain time, maintenance work is reasonably arranged, time utilization efficiency is improved, and equipment maintenance efficiency is further improved.
Referring to fig. 2, fig. 2 is a schematic flowchart of a maintenance scheme generating method based on maintenance time according to an embodiment of the present application. The maintenance scheme generating method based on the maintenance time can be applied to a server and is used for generating a recommended maintenance scheme of the equipment by combining the predicted maintenance time length of the equipment, so that the equipment maintenance efficiency is improved.
As shown in fig. 2, before the step S101 of the maintenance scheme generating method based on the maintenance time, steps S201 to S204 are specifically further included.
S201, based on historical inspection information of the equipment, obtaining predicted inspection duration of the equipment;
s202, obtaining predicted maintenance duration of the equipment based on historical maintenance information of the equipment;
S203, obtaining predicted maintenance time of the equipment based on the predicted inspection time and the predicted maintenance time;
s204, generating the maintenance duration prediction table based on the equipment type of the equipment and the predicted maintenance duration.
Further, the obtaining the predicted maintenance duration of the device based on the historical maintenance information of the device includes: calculating and obtaining the average maintenance duration of the equipment based on the maintenance duration, the maintenance result and the weighting factor of the fault type in the historical maintenance information; based on the identification of the device, determining the type of the device, and based on the average maintenance duration of at least one device of the type, calculating and obtaining the predicted maintenance duration of the device of the type.
In one embodiment, historical inspection information of the equipment is obtained, the historical inspection time length and the inspection result of the equipment are obtained by analyzing the historical inspection information of the equipment, and the average inspection time length of the equipment is obtained according to the historical inspection time length and the inspection result.
In one embodiment, the device type of the device is determined, historical inspection information of devices of the same device type is obtained, and then average inspection duration of each device is obtained. And finally, obtaining the predicted patrol duration of the similar equipment by aggregating the average patrol duration of the similar equipment.
In one embodiment, historical maintenance information of the equipment is obtained, the maintenance time, the fault type and the maintenance result of the equipment are obtained by analyzing the historical maintenance information of the equipment, and the average maintenance time of the equipment can be obtained according to the historical maintenance time and the maintenance result.
In one embodiment, weighting factors are set for faults of different fault types according to historical rule information of equipment maintenance, and average maintenance duration of the equipment is obtained through a weighting method. And determining the type of the equipment through the identification of the equipment, calculating the average maintenance duration of the equipment of the same type, and finally, aggregating the average maintenance duration of the similar equipment to obtain the predicted maintenance duration of the similar equipment.
In one embodiment, the average inspection duration and the average maintenance duration of a certain type of equipment are summed, and the obtained result is the predicted maintenance duration of the equipment.
In one embodiment, a plurality of device types and predicted maintenance time lengths of devices corresponding to the device types are counted, and a maintenance time length prediction table is generated. When the equipment is subjected to abnormality detection, the equipment type of the equipment can be used for looking up a table to obtain the predicted maintenance time length corresponding to the equipment, so that the maintenance time length obtaining efficiency is improved, and the equipment maintenance efficiency is further improved.
Referring to fig. 3, fig. 3 is a schematic flowchart of a maintenance scheme generating method based on maintenance time according to an embodiment of the present application. The maintenance scheme generating method based on the maintenance time can be applied to a server and is used for generating a recommended maintenance scheme of the equipment by combining the predicted maintenance time length of the equipment, so that the equipment maintenance efficiency is improved.
As shown in fig. 3, the step S104 of the maintenance scheme generating method based on the maintenance time specifically includes steps S301 to S303.
S301, searching an initial maintenance scheme corresponding to the fault type in a history maintenance scheme table based on the fault type;
s302, determining maintenance start-stop time based on the predicted maintenance time;
S303, generating the recommended maintenance scheme based on the initial maintenance scheme and the maintenance start-stop time.
In one embodiment, the history maintenance scheme comparison table includes different fault types and history maintenance schemes corresponding to the fault types, and the history maintenance scheme table is generated by counting the maintenance schemes of a plurality of devices with different fault types.
In one embodiment, by the fault type of the abnormal device, the maintenance scheme corresponding to the fault type is searched in the history maintenance scheme table to serve as an initial maintenance scheme, and the initial prevention scheme may include the fault type, the fault occurrence reason, the fault item, the recommended prevention days and the like.
In one embodiment, a maintenance start-stop time for a device is determined based on a predicted maintenance duration for the current device, and the maintenance start-stop time is added to an initial preventative program.
It can be understood that when a plurality of devices need to be maintained, maintenance tasks need to be arranged, after the predicted maintenance duration and the maintenance start-stop time of the devices, the scheduling efficiency of the maintenance tasks can be effectively improved, the time is fully utilized, and the maintenance efficiency of the devices is improved. For example, when the maintenance task of the first device starts at 8 am and the predicted maintenance time is 3 hours, the maintenance task start time of the second device may be set to 11 am, the maintenance personnel may start the maintenance task of the second device at 11 am, and the maintenance personnel may perform other maintenance tasks in other times, so that the time can be fully utilized and the maintenance efficiency can be improved.
Referring to fig. 4, fig. 4 is a schematic block diagram of a maintenance-time-based maintenance scheme generating apparatus for performing the aforementioned maintenance-time-based maintenance scheme generating method according to an embodiment of the present application. Wherein, the maintenance scheme generating device based on the maintenance time can be configured on a server.
As shown in fig. 4, the maintenance scheme generating apparatus 400 based on maintenance time includes:
an operation data obtaining module 401, configured to obtain operation data of a device;
An operation state obtaining module 402, configured to process the operation data based on an anomaly detection model, to obtain an operation state of the device;
a predicted maintenance duration obtaining module 403, configured to find, when the operation state is abnormal, a predicted maintenance duration of the device in a preset maintenance duration prediction table based on a device type of the device;
a maintenance scheme generating module 404, configured to generate a recommended maintenance scheme for the device based on the running state and the predicted maintenance duration.
In one embodiment, the maintenance scheme generating device 400 based on maintenance time further includes: the maintenance duration prediction table generation module comprises:
the predicted patrol duration obtaining unit is used for obtaining the predicted patrol duration of the equipment based on the historical patrol information of the equipment;
A predicted maintenance time length obtaining unit, configured to obtain a predicted maintenance time length of the apparatus based on historical maintenance information of the apparatus;
a predicted maintenance time length obtaining unit, configured to obtain a predicted maintenance time length of the device based on the predicted inspection time length and the predicted maintenance time length;
And the maintenance duration prediction table generating unit is used for generating the maintenance duration prediction table based on the equipment type of the equipment and the predicted maintenance duration.
In one embodiment, the predicted maintenance duration obtaining unit includes:
An average maintenance duration obtaining subunit, configured to calculate and obtain an average maintenance duration of the device based on the maintenance duration, the maintenance result, and the weighting factor of the fault type in the historical maintenance information;
a predicted maintenance duration obtaining subunit, configured to determine a type of the device based on the identifier of the device, and calculate, based on an average maintenance duration of at least one device of the type, a predicted maintenance duration for obtaining the device of the type.
In one embodiment, the maintenance scheme generating device 400 based on maintenance time further includes an anomaly detection model obtaining module, where the anomaly detection model obtaining module includes:
A history data obtaining unit, configured to obtain history operation data and a history operation state of at least one device;
the training data set generating unit is used for processing the historical operation data to generate a training data set;
the abnormality detection model obtaining unit is used for training a pre-training model based on the historical running state and the training data set to obtain the abnormality detection model.
In one embodiment, the maintenance scheme generating device 400 based on maintenance time further includes:
The type determining module is used for acquiring the abnormal information of the equipment when the running state is abnormal, comparing a preset fault rule with the abnormal information and determining the fault type of the equipment.
In one embodiment, the maintenance scheme generation module 404 includes:
The initial maintenance scheme searching unit is used for searching an initial maintenance scheme corresponding to the fault type in the historical maintenance scheme table based on the fault type;
a maintenance start-stop time determining unit, configured to determine a maintenance start-stop time based on the predicted maintenance duration;
and the recommended maintenance scheme generating unit is used for generating the recommended maintenance scheme based on the initial maintenance scheme and the maintenance start-stop time.
In one embodiment, the operation state obtaining module 402 includes:
The abnormal times obtaining unit is used for processing the operation data based on the abnormal detection model to obtain abnormal times of the equipment in a preset time period;
And the running state obtaining unit is used for comparing the abnormal times with a preset abnormal times threshold value to obtain the running state.
It should be noted that, for convenience and brevity of description, the specific working process of the apparatus and each module described above may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The apparatus described above may be implemented in the form of a computer program which is executable on a computer device as shown in fig. 5.
Referring to fig. 5, fig. 5 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device may be a server.
With reference to FIG. 5, the computer device includes a processor, memory, and a network interface connected by a system bus, where the memory may include a non-volatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program comprises program instructions that, when executed, cause the processor to perform any of a number of maintenance schedule generation methods based on maintenance time.
The processor is used to provide computing and control capabilities to support the operation of the entire computer device.
The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium that, when executed by a processor, causes the processor to perform any of a number of maintenance schedule generation methods based on maintenance time.
The network interface is used for network communication such as transmitting assigned tasks and the like. It will be appreciated by those skilled in the art that the structure shown in FIG. 5 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
It should be appreciated that the Processor may be a central processing unit (Central Processing Unit, CPU), it may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein in one embodiment the processor is configured to run a computer program stored in the memory to implement the steps of:
Acquiring operation data of equipment;
processing the operation data based on an abnormality detection model to obtain an operation state of the equipment;
When the running state is abnormal, searching a predicted maintenance time length of the equipment in a preset maintenance time length prediction table based on the equipment type of the equipment;
And generating a recommended maintenance scheme of the equipment based on the running state and the predicted maintenance duration.
In one embodiment, when the operation state is abnormal operation, the processor is further configured to, before searching a preset maintenance duration prediction table for a predicted maintenance duration of the device based on a device type of the device, implement:
based on the historical inspection information of the equipment, obtaining the predicted inspection duration of the equipment;
based on the historical maintenance information of the equipment, obtaining the predicted maintenance duration of the equipment;
Obtaining a predicted maintenance duration of the device based on the predicted inspection duration and the predicted maintenance duration;
and generating the maintenance duration prediction table based on the equipment type of the equipment and the predicted maintenance duration.
In one embodiment, the processor, when implementing obtaining the predicted maintenance duration of the device based on the historical maintenance information of the device, is configured to implement:
Calculating and obtaining the average maintenance duration of the equipment based on the maintenance duration, the maintenance result and the weighting factor of the fault type in the historical maintenance information;
based on the identification of the device, determining the type of the device, and based on the average maintenance duration of at least one device of the type, calculating and obtaining the predicted maintenance duration of the device of the type.
In one embodiment, before implementing the anomaly detection model, the processor is further configured to, before implementing the processing of the operation data to obtain the operation state of the device, implement:
Acquiring historical operation data and historical operation states of at least one device;
processing the historical operation data to generate a training data set;
and training a pre-training model based on the historical running state and the training data set to obtain the abnormality detection model.
In one embodiment, after implementing the anomaly detection model, the processor is further configured to, after processing the operation data to obtain an operation state of the device, implement:
and when the running state is abnormal running, acquiring the abnormal information of the equipment, comparing a preset fault rule with the abnormal information, and determining the fault type of the equipment.
In one embodiment, the processor, when implementing generating the recommended maintenance solution for the device based on the operating state and the predicted maintenance duration, is configured to implement:
Searching an initial maintenance scheme corresponding to the fault type in a historical maintenance scheme table based on the fault type;
determining maintenance start-stop time based on the predicted maintenance duration;
And generating the recommended maintenance scheme based on the initial maintenance scheme and the maintenance start-stop time.
In one embodiment, when the processor processes the operation data based on the anomaly detection model, the processor is configured to implement:
processing the operation data based on the abnormality detection model to obtain the abnormality times of the equipment in a preset time period;
and comparing the abnormal times with a preset abnormal times threshold value to obtain the running state.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, the computer program comprises program instructions, and the processor executes the program instructions to realize any maintenance scheme generation method based on maintenance time.
The computer readable storage medium may be an internal storage unit of the computer device according to the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like, which are provided on the computer device.
While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (10)
1. A maintenance scheme generation method based on maintenance time, comprising:
Acquiring operation data of equipment;
processing the operation data based on an abnormality detection model to obtain an operation state of the equipment;
When the running state is abnormal, searching a predicted maintenance time length of the equipment in a preset maintenance time length prediction table based on the equipment type of the equipment;
And generating a recommended maintenance scheme of the equipment based on the running state and the predicted maintenance duration.
2. The maintenance time-based maintenance scheme generating method according to claim 1, wherein when the operation state is abnormal operation, before searching a predicted maintenance time length of the device in a preset maintenance time length prediction table based on a device type of the device, the method further comprises:
based on the historical inspection information of the equipment, obtaining the predicted inspection duration of the equipment;
based on the historical maintenance information of the equipment, obtaining the predicted maintenance duration of the equipment;
Obtaining a predicted maintenance duration of the device based on the predicted inspection duration and the predicted maintenance duration;
and generating the maintenance duration prediction table based on the equipment type of the equipment and the predicted maintenance duration.
3. The maintenance-time-based maintenance scheme generation method according to claim 2, wherein the obtaining a predicted maintenance duration of the apparatus based on the historical maintenance information of the apparatus includes:
Calculating and obtaining the average maintenance duration of the equipment based on the maintenance duration, the maintenance result and the weighting factor of the fault type in the historical maintenance information;
based on the identification of the device, determining the type of the device, and based on the average maintenance duration of at least one device of the type, calculating and obtaining the predicted maintenance duration of the device of the type.
4. The maintenance-time-based maintenance scenario generation method according to claim 1, wherein the processing the operation data based on the abnormality detection model, before obtaining the operation state of the device, further comprises:
Acquiring historical operation data and historical operation states of at least one device;
processing the historical operation data to generate a training data set;
and training a pre-training model based on the historical running state and the training data set to obtain the abnormality detection model.
5. The maintenance-time-based maintenance scheme generation method according to claim 1, wherein the processing the operation data based on the abnormality detection model, after obtaining the operation state of the device, further comprises:
and when the running state is abnormal running, acquiring the abnormal information of the equipment, comparing a preset fault rule with the abnormal information, and determining the fault type of the equipment.
6. The maintenance-time-based maintenance scenario generation method of claim 5, wherein the generating the recommended maintenance scenario for the device based on the operating state and the predicted maintenance duration comprises:
Searching an initial maintenance scheme corresponding to the fault type in a historical maintenance scheme table based on the fault type;
determining maintenance start-stop time based on the predicted maintenance duration;
And generating the recommended maintenance scheme based on the initial maintenance scheme and the maintenance start-stop time.
7. The maintenance-time-based maintenance scenario generation method according to any one of claims 1 to 6, wherein the processing the operation data based on the abnormality detection model to obtain the operation state of the device includes:
processing the operation data based on the abnormality detection model to obtain the abnormality times of the equipment in a preset time period;
and comparing the abnormal times with a preset abnormal times threshold value to obtain the running state.
8. A maintenance schedule generating apparatus based on maintenance time, comprising:
The operation data acquisition module is used for acquiring operation data of the equipment;
The running state obtaining module is used for processing the running data based on the abnormality detection model to obtain the running state of the equipment;
The predicted maintenance time length obtaining module is used for searching the predicted maintenance time length of the equipment in a preset maintenance time length prediction table based on the equipment type of the equipment when the running state is abnormal;
and the maintenance scheme generation module is used for generating a recommended maintenance scheme of the equipment based on the running state and the predicted maintenance duration.
9. A computer device, the computer device comprising a memory and a processor;
The memory is used for storing a computer program;
the processor configured to execute the computer program and implement the maintenance-time-based maintenance scheme generation method according to any one of claims 1 to 7 when the computer program is executed.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to implement the maintenance-time-based maintenance scheme generation method according to any one of claims 1 to 7.
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