CN111369079A - Maintenance plan prediction method and device, electronic device, and storage medium - Google Patents
Maintenance plan prediction method and device, electronic device, and storage medium Download PDFInfo
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Abstract
The application provides a method and a device for predicting a maintenance plan, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring sensor information of a power device and a plurality of candidate maintenance plans, wherein the candidate maintenance plans comprise maintenance time points; inputting the sensor information and the candidate maintenance plan into the constructed prediction model to obtain the equipment life output by the prediction model and corresponding to the candidate maintenance plan; aiming at each candidate maintenance plan, calculating a feedback parameter corresponding to the candidate maintenance plan according to the equipment service life and the maintenance time point corresponding to the candidate maintenance plan; and selecting a target maintenance plan from the plurality of candidate maintenance plans according to the feedback parameters corresponding to each candidate maintenance plan. The method can balance the extra cost consumption caused by maintenance and the income caused by prolonging the service life of the equipment, thereby finding the optimal maintenance plan and improving the efficiency of the power equipment.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a method and an apparatus for predicting a maintenance plan, an electronic device, and a storage medium.
Background
The power equipment is equipment for converting, conducting and adjusting various energy semantics in nature, and comprises a motor, a generator, a transformer and the like. These devices are all common devices in industrial production, are important components in production lines, heat dissipation systems and transmission systems, and bring about serious economic loss if unplanned shutdown occurs.
In order to prevent unplanned shutdown of the power plant, regular maintenance of the power plant is required, and conventional maintenance plans are generally classified into periodic regular maintenance and irregular maintenance after abnormality detection, which are made by a human being with experience. However, the setting of the timing maintenance requires a large amount of experience accumulation, and when the frequency of the timing maintenance is high, high labor cost and production loss during maintenance are brought; when the frequency of the timing maintenance is low, the reliability of the power equipment is reduced, the equipment abnormity is caused, the usable period of the power equipment is reduced, and the production loss is increased. The untimely maintenance can bring more unstable factors to the production, reduce the stability of the production and increase the maintenance cost at the same time. How to determine a reasonable maintenance plan and determine the balance between the extra cost consumption brought by equipment maintenance and the economic benefits brought by the income brought by prolonging the service life of the equipment is an urgent problem to be solved.
Disclosure of Invention
The embodiment of the application provides a method for predicting a maintenance plan, and the method can determine a reasonable maintenance plan and improve the efficiency of power equipment.
A first aspect of an embodiment of the present application provides a method for predicting a maintenance plan, where the method includes:
acquiring sensor information of a power plant and a plurality of candidate maintenance plans, wherein the candidate maintenance plans comprise maintenance time points;
inputting the sensor information and the candidate maintenance plan into a constructed prediction model to obtain the equipment life output by the prediction model and corresponding to the candidate maintenance plan;
aiming at each candidate maintenance plan, calculating a feedback parameter corresponding to the candidate maintenance plan according to the equipment service life corresponding to the candidate maintenance plan and the maintenance time point;
and selecting a target maintenance plan from the plurality of candidate maintenance plans according to the feedback parameters corresponding to each candidate maintenance plan.
In one embodiment, the obtaining the device life output by the predictive model corresponding to the candidate maintenance plan comprises:
and obtaining a reliability curve which is output by a prediction model and corresponds to the candidate maintenance plan, and determining the service life of the equipment corresponding to the candidate maintenance plan according to the reliability curve and a preset reliability threshold.
In an embodiment, the calculating, for each candidate maintenance plan, a feedback parameter corresponding to the candidate maintenance plan according to the equipment life and the maintenance time point corresponding to the candidate maintenance plan includes:
for each candidate maintenance plan, determining plan feedback parameters according to the equipment life corresponding to the candidate maintenance plan, and determining consumption parameters according to the maintenance time point corresponding to the candidate maintenance plan;
and calculating feedback parameters of the candidate maintenance plan according to the plan feedback parameters and the consumption parameters.
In one embodiment, the determining plan feedback parameters according to the equipment life corresponding to the candidate maintenance plan includes:
acquiring unit price information of the power equipment;
determining unit price information of the power equipment according to the service life of the equipment and the unit price information;
and determining the plan feedback parameters corresponding to the unit price information according to a preset parameter model.
In an embodiment, the candidate maintenance plan further comprises: maintaining information, wherein determining consumption parameters according to the maintenance time points corresponding to the candidate maintenance plans comprises:
determining maintenance times according to the maintenance time points;
and determining the consumption parameters according to the maintenance information and the maintenance times, wherein the maintenance information comprises information of maintenance personnel and/or spare parts.
In one embodiment, the calculating the feedback parameter of the candidate maintenance plan according to the plan feedback parameter and the consumption parameter includes:
and calculating the difference between the plan feedback parameter and the consumption parameter, and determining the feedback parameter of the candidate maintenance plan.
In an embodiment, the selecting a target maintenance plan from the plurality of candidate maintenance plans according to the feedback parameter corresponding to each candidate maintenance plan includes:
and comparing the feedback parameters corresponding to each candidate maintenance plan, and selecting the candidate maintenance plan with the highest feedback parameter as the target maintenance plan.
An embodiment of the present application further provides a device for predicting a maintenance plan, where the device includes:
the system comprises a data acquisition module, a maintenance scheduling module and a maintenance scheduling module, wherein the data acquisition module is used for acquiring sensor information of the power equipment and a plurality of candidate maintenance schedules, and the candidate maintenance schedules comprise maintenance time points;
the prediction module is used for inputting the sensor information and the candidate maintenance plan into a constructed prediction model to obtain the equipment life output by the prediction model and corresponding to the candidate maintenance plan;
the operation module is used for calculating feedback parameters corresponding to the candidate maintenance plans according to the equipment service lives and the maintenance time points corresponding to the candidate maintenance plans aiming at each candidate maintenance plan;
and the selection module is used for selecting a target maintenance plan from the multiple candidate maintenance plans according to the feedback parameters corresponding to each candidate maintenance plan.
A third aspect of embodiments of the present application provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the predictive method of maintenance planning described above.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, which stores a computer program, where the computer program is executable by a processor to perform the above-mentioned method for predicting a maintenance plan.
According to the technical scheme provided by the embodiment of the application, the sensor information acquired from the power equipment and the plurality of candidate maintenance plans are input into the constructed prediction model, the equipment life corresponding to each candidate maintenance plan can be obtained, and the feedback parameter of each candidate maintenance plan is calculated according to the equipment life and the maintenance time point corresponding to each candidate maintenance plan, so that the optimal target maintenance plan is selected from the plurality of candidate maintenance plans. The extra cost consumption caused by maintenance and the benefits caused by prolonging the service life of the equipment can be balanced, so that the optimal maintenance plan is found, and the efficiency of the power equipment is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly described below.
Fig. 1 is a schematic view of an application scenario of a prediction method for a maintenance plan according to an embodiment of the present application;
FIG. 2 is a flow chart illustrating a method for predicting a maintenance plan according to an embodiment of the present disclosure;
FIG. 3 is a graph of a maintenance schedule versus reliability over time;
FIG. 4 is a graph of reliability versus time for maintenance schedule two;
FIG. 5 is a detailed flowchart of step 103 in the corresponding embodiment of FIG. 2;
FIG. 6 is a schematic flow chart diagram illustrating a method for predicting a maintenance schedule according to another embodiment of the present application;
fig. 7 is a block diagram of a prediction apparatus for a maintenance schedule according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated.
The power equipment is a common equipment in industrial production, and maintenance of the power equipment is also routine in industrial production, so a reasonable maintenance plan needs to be designed to indicate information such as when the power equipment is maintained, the time for normal operation of the equipment after maintenance and the like, so that the time for maintenance and economic benefits and consumption brought by maintenance can be comprehensively evaluated.
Fig. 1 is a schematic application scenario diagram of a prediction method of a maintenance plan according to an embodiment of the present application. As shown in fig. 1, the application scenario includes a server 100 and a power plant 200, and the power plant 200 may include a temperature sensor, a vibration sensor, a three-phase sensor, and other sensors, which may collect operation status data of the power plant. The power equipment 200 can transmit the sensor information to the server 100, and then the server 100 can execute the technical scheme provided by the embodiment of the application, and intelligently select a proper maintenance plan based on the sensor information, so that the working efficiency and the working stability of the power equipment are improved.
The embodiment of the application also provides the electronic equipment. The electronic device may be the server 100 shown in fig. 1. As shown in fig. 1, server 100 may include a processor 110 and a memory 120 for storing instructions executable by processor 110; wherein, the processor 110 is configured to execute the prediction method of the maintenance plan provided by the embodiment of the present application.
The Memory 120 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.
The present application also provides a computer-readable storage medium storing a computer program executable by the processor 110 to perform the method for predicting a maintenance plan provided by the embodiments of the present application.
Fig. 2 is a flowchart illustrating a maintenance plan prediction method according to an embodiment of the present application, which may be executed by the server 100, and as shown in fig. 2, the method may include the following steps 101 to 104.
Step 101: sensor information for a power plant and a plurality of candidate maintenance schedules are obtained, wherein the candidate maintenance schedules include maintenance time points.
In one embodiment, the power plant may be a power generation plant, such as a steam engine, a steam turbine, a gasoline engine, a diesel engine, a generator, or the like; but also power transmission/distribution equipment, such as transformers, switchboards, rectifiers, etc.; or may be a power consumer device such as an electric motor, welder, pump, etc. The power plant is not limited to the above-described exemplary plant, and should be construed as a power plant in the present patent embodiment as long as it is directly or indirectly related to the generation, delivery, use of power.
In some embodiments, the sensors include a light sensitive sensor that can detect light, a sound sensitive sensor that can receive sound, a sensor that can receive a sense of taste of gas, a chemical sensor, a temperature sensor that can measure temperature, a shock type sensor that can receive a touch such as shock, such as a pressure sensitive, temperature sensitive, fluid sensor, an electrical sensor that can detect various types of electrical circuits, such as a three phase electrical sensor, and the like. Parameters such as various structures of power equipment, electronic equipment, loss or operation data of various elements and the like can be known by using various sensors.
Further, the acquired sensor data may be all the sensor data after the start of use, or data within a certain period of time, such as 24 hours, 48 hours, 72 hours, 1 week, 1 month sensor data before the start of prediction, and the like, or may provide data at different times according to different sensors, such as 1 week data provided by a temperature sensor, 1 month data provided by a vibration sensor, and the like. In some embodiments, the data provided by the sensor includes time information corresponding to the data, for example, each time the data is acquired has its corresponding acquisition time point information.
In some embodiments, the candidate maintenance plan may be a classified maintenance plan, for example, a high-frequency maintenance plan and a low-frequency maintenance plan which are classified according to the frequency of maintenance, where the high-frequency maintenance plan corresponds to the low-frequency maintenance plan, and for some devices with frequent maintenance, the high-frequency maintenance plan may be maintained once in less than 10 days, specifically, the high-frequency maintenance plan may further include maintained once in 9 days, maintained once in 8 days, maintained once in 7 points, and the like, and further, the high-frequency maintenance plan may be maintained for the first time in 9 days, maintained for the second time in 8 days, maintained for the third time in 7 days, and the like. In this case, the low frequency maintenance schedule may be maintained for more than 10 days, and specifically may include once maintenance for 11 days, once maintenance for 15 days, once maintenance for 20 days, and so on, and may also be the first maintenance for 11 days, the second maintenance for 15 days, the third maintenance for 20 days, and so on. It is also possible to have a hybrid maintenance plan, which is one of any combination of high frequency, low frequency, regular maintenance, etc., or a regular maintenance plan, which is one maintenance for 10 days, etc.
Candidate maintenance schedules may also be categorized by the components of the equipment, such as for electric motors, the major components of which include stators, rotors, end caps, bearings, fans, and the like. According to the stator, a maintenance plan preset by the stator can be provided, such as maintenance once in 1 month, maintenance once in 2 months and the like; there may be a maintenance schedule for the fan, such as once for 10 days, once for 20 days, etc., based on the fan; alternatively, a candidate maintenance plan may be obtained by combining maintenance modes of a plurality of components, for example, once maintenance is performed for 20 days, once maintenance is performed for 10 days, once maintenance is performed for 20 days, and once maintenance is performed for 20 days.
The classification of the candidate maintenance plans may also be a classification-free maintenance plan, including receiving user input, such as a user's desire to perform maintenance at the end of each month to form a new candidate maintenance plan, or to randomly generate maintenance times to form a new candidate maintenance plan.
The determination of the candidate maintenance plan includes not only the generation method of the maintenance plan described above, but also other generation methods, and is within the scope of the embodiments of the present application as long as a specific maintenance time can be provided.
Wherein the maintenance plan includes a specific maintenance time point at each maintenance, and the specific maintenance time is known through the time point. The time point may be a clear time point, a countdown estimated time point, or the like.
Step 102: and inputting the sensor information and the candidate maintenance plan into the constructed prediction model, and obtaining the equipment life output by the prediction model and corresponding to the candidate maintenance plan.
In some embodiments, the prediction model may be an algorithm in a machine learning algorithm, specifically including a deep neural network, a particle filter algorithm, and the like, or may be a non-machine learning algorithm, such as a kalman filter algorithm, a regression algorithm, and the like. The establishment or training of the prediction model is processed in advance, and the processed prediction model is used for the prediction of the embodiment.
Further, the prediction model is predicted, the sensor information and the plurality of candidate maintenance plans acquired in step 102 are input into the prediction model, and the corresponding equipment life is obtained, where the equipment life corresponds to the input candidate maintenance plan, where the sensor information and one candidate maintenance plan are input each time to obtain the equipment life corresponding to the candidate maintenance plan, or the sensor information and the plurality of candidate maintenance plans are input each time to obtain the equipment life corresponding to each candidate maintenance plan in the plurality of candidate maintenance plans.
In some embodiments, a reliability curve output by a prediction model corresponding to the candidate maintenance plan is obtained, and the equipment life corresponding to the candidate maintenance plan is determined according to the reliability curve and a preset reliability threshold. Fig. 3 and 4 are output reliability curves along time, and as can be seen from fig. 3 and 4, the direct result of prediction by the prediction model at this time is not the lifetime of the device, but the reliability curve of the device, that is, the reliability of the device corresponding to each time point in future time, and the reliability of the device is the probability that the device will not fail at a certain time. And acquiring a reliability threshold value of the power equipment, wherein in a time period when the reliability curve of the equipment is lower than the reliability threshold value, the earliest time point is the time point when the power equipment needs to be scrapped, and the time between the time point and the formal operation of the equipment is the service life of the equipment.
Step 103: aiming at each candidate maintenance plan, calculating a feedback parameter corresponding to the candidate maintenance plan according to the equipment service life corresponding to the candidate maintenance plan and the maintenance time point;
according to the equipment life and the maintenance time point of each candidate maintenance plan, a feedback parameter corresponding to each candidate maintenance plan can be calculated, and the feedback parameter can be a parameter for feeding back the use return rate of the power equipment, such as the total income brought by the power equipment in the production activity, the total consumption brought by the power equipment in the production activity, and the like.
In some embodiments, the algorithm used to obtain the feedback parameters includes genetic algorithm, ant colony algorithm, and the like.
In some embodiments, an equipment plan feedback parameter may be calculated according to the equipment lifetime, a consumption parameter is determined according to the maintenance time point corresponding to the candidate maintenance plan, and the plan feedback parameter and the consumption parameter are calculated to obtain the feedback parameter corresponding to the candidate maintenance plan. The method of the embodiment of the present application proceeds to step 104 according to the feedback parameter.
In some embodiments, the validity of the maintenance time point in the candidate maintenance schedule is determined according to the equipment lifetime, a feedback parameter, i.e. the total consumption of the effective maintenance is predicted, is determined according to the effective maintenance time point, and the method of the embodiment of the present application proceeds to step 104 according to the feedback parameter.
Step 104: and selecting a target maintenance plan from the plurality of candidate maintenance plans according to the feedback parameters corresponding to each candidate maintenance plan.
And (4) performing mathematical screening on the feedback parameters aiming at each candidate maintenance plan generated in the step 103, and selecting the most appropriate candidate maintenance plan as a target maintenance plan, wherein the target maintenance plan is used for guiding a maintainer to maintain the power equipment at the specified time point of the target maintenance plan.
In some embodiments, the selection of the appropriate feedback parameter may be to select a candidate maintenance plan corresponding to the maximum value of the feedback parameter, or to select a candidate maintenance plan corresponding to the largest value of the feedback parameter, or to sort the values of the feedback parameter and to remove the abnormal value, and then to select a candidate maintenance plan corresponding to the maximum value of the remaining feedback parameter.
In some embodiments, after the above method is executed, the above method may also be executed in a loop, for example, the target maintenance plan is found at time T1, but at time T2 after a preset interval T, the reliability prediction result is updated according to the sensor information collected between times T1 and T2, and then a new target maintenance plan is selected, where the preset interval T may be a designated time or a maintenance time point in the next target maintenance plan.
By adopting the embodiment, the optimal target maintenance plan for the power equipment is selected, the extra cost consumption caused by maintenance and the benefit caused by prolonging the service life of the equipment can be balanced, the optimal maintenance plan is found, and the efficiency of the power equipment is improved. The use efficiency of the power plant as a whole can be maximized.
Fig. 5 is a schematic flow chart of a method for determining feedback parameters in the embodiment of the present application, and as shown in fig. 5, in an embodiment, the step 103 of determining feedback parameters may include the following steps 1031 to 1033.
And step 1031, determining plan feedback parameters according to the equipment life corresponding to the candidate maintenance plans for each candidate maintenance plan.
In one embodiment, the plan feedback parameter is a plan return for the life of the device, and the plan return may be a fixed plan return or a different fixed plan return for different devices, for example, for a low power motor, the fixed plan return is 1 ten thousand yuan per day and for a high power motor, the fixed plan return is 2 ten thousand yuan per day.
In one embodiment, unit price information of the power equipment is acquired; determining unit price information of the power equipment according to the service life of the equipment and the unit price information; and determining the plan feedback parameters corresponding to the unit price information according to a preset parameter model. The cost of the power equipment is known by acquiring the unit price information of the power equipment, the unit price information, namely the average daily cost of the equipment, is determined according to the service life of the equipment and the cost, the parameter model can be a comparison table or a fixed calculation mode, and the plan feedback parameter, namely the plan profit value, is determined according to the average daily cost of the equipment according to a preset comparison table or a fixed calculation mode. Wherein the planned profit refers to the inverse relation with the daily average cost.
Step 1032, determining a consumption parameter according to the maintenance time point corresponding to the candidate maintenance plan.
In some embodiments, a consumption parameter, which may be a maintenance cost, is determined according to the maintenance time points of the candidate maintenance plans. For example, the maintenance plan further includes maintenance personnel information, whether each maintenance time point needs maintenance is determined, if the time point is within the service life of the equipment, the maintenance personnel at each time point are determined, the maintenance personnel correspond to corresponding consumption values, and finally, the total consumption parameters are determined according to the number of the maintenance personnel and the maintenance time points; and the maintenance plan also comprises spare part information, the spare part information required to be used at each time point is determined, the spare part information corresponds to a corresponding consumption value, and finally, the total consumption parameter is determined according to the maintenance spare part information and the maintenance time point. Further, the maintenance plan further includes information of maintenance personnel and spare parts, then the information of the maintenance personnel and the spare parts at each time point is determined, and finally the total consumption parameters are determined according to the number of the maintenance personnel and the maintenance time points.
In some embodiments, the number of times of maintenance is determined according to the time point, and when the information of the maintenance personnel and/or spare parts for each maintenance is the same, the consumption parameter may be obtained according to the product of the number of times of maintenance and the consumption value brought by the information of the maintenance personnel and/or spare parts.
Step 1033, calculating a feedback parameter of the candidate maintenance plan according to the plan feedback parameter and the consumption parameter.
In an embodiment, the plan feedback parameter and the consumption parameter are obtained in step 1031 and step 1032, respectively, and the plan feedback parameter and the consumption parameter are calculated to obtain the feedback parameter corresponding to the candidate maintenance plan.
In one embodiment, a difference between the planned feedback parameter and the consumption parameter is calculated to determine the feedback parameter for the candidate maintenance plan. In some embodiments, a ratio of the planned feedback parameter and the consumption parameter is calculated to determine the feedback parameter for the candidate maintenance plan.
Through the implementation mode, the feedback parameters can be determined more accurately, and the prediction accuracy of the maintenance plan of the power equipment is improved.
Fig. 6 is a schematic flow chart of a specific prediction method according to an embodiment of the present application, and in an implementation, as shown in fig. 6, information of temperature, vibration, three-phase power, and the like of a device is obtained through a power device sensor, a device life corresponding to each maintenance plan is obtained through a reliability prediction algorithm, and a plan feedback parameter, that is, a plan benefit, of each maintenance plan can be obtained according to the device life, for example: and the gains of the maintenance plan 1, the gains of the maintenance plan 2 and the like are processed by an operational optimization algorithm, consumption parameters are substituted into the consumption parameters for calculation, and the highest-gain maintenance plan is obtained by comparing feedback parameters of all the maintenance plans.
The following are embodiments of the apparatus of the present application that may be used to implement embodiments of the predictive method of the maintenance schedule described above in the present application. For details not disclosed in the embodiments of the apparatus of the present application, refer to the embodiments of the prediction method of the maintenance schedule of the present application.
Fig. 7 is a block diagram of a maintenance plan prediction device according to an embodiment of the present application. As shown in fig. 7, the apparatus includes: a data collection module 710, a prediction module 720, an operations research module 730, and a selection module 740.
A data acquisition module 710 for obtaining sensor information of a power plant and a plurality of candidate maintenance plans, wherein the candidate maintenance plans include maintenance time points.
And the prediction module 720 is used for inputting the sensor information and the candidate maintenance plan into the constructed prediction model and obtaining the equipment life output by the prediction model and corresponding to the candidate maintenance plan.
The operational research module 730 is configured to calculate, for each candidate maintenance plan, a feedback parameter corresponding to the candidate maintenance plan according to the equipment life and the maintenance time point corresponding to the candidate maintenance plan.
The selecting module 740 is configured to select a target maintenance plan from the multiple candidate maintenance plans according to the feedback parameter corresponding to each candidate maintenance plan.
The implementation processes of the functions and actions of each module in the above device are specifically described in the implementation processes of the corresponding steps in the above prediction method of the maintenance plan, and are not described herein again.
In the embodiments provided in the present application, the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Claims (10)
1. A method of predicting a maintenance plan, the method comprising:
acquiring sensor information of a power plant and a plurality of candidate maintenance plans, wherein the candidate maintenance plans comprise maintenance time points;
inputting the sensor information and the candidate maintenance plan into a constructed prediction model to obtain the equipment life output by the prediction model and corresponding to the candidate maintenance plan;
aiming at each candidate maintenance plan, calculating a feedback parameter corresponding to the candidate maintenance plan according to the equipment service life corresponding to the candidate maintenance plan and the maintenance time point;
and selecting a target maintenance plan from the plurality of candidate maintenance plans according to the feedback parameters corresponding to each candidate maintenance plan.
2. The method of claim 1, wherein obtaining the device life output by the predictive model corresponding to the candidate maintenance plan comprises:
and obtaining a reliability curve which is output by a prediction model and corresponds to the candidate maintenance plan, and determining the service life of the equipment corresponding to the candidate maintenance plan according to the reliability curve and a preset reliability threshold.
3. The method of claim 1, wherein calculating, for each candidate maintenance plan, feedback parameters corresponding to the candidate maintenance plan based on the equipment life and the maintenance time point corresponding to the candidate maintenance plan comprises:
for each candidate maintenance plan, determining plan feedback parameters according to the equipment life corresponding to the candidate maintenance plan, and determining consumption parameters according to the maintenance time point corresponding to the candidate maintenance plan;
and calculating feedback parameters of the candidate maintenance plan according to the plan feedback parameters and the consumption parameters.
4. The method of claim 3, wherein determining a plan feedback parameter based on the equipment life for the candidate maintenance plan comprises:
acquiring unit price information of the power equipment;
determining unit price information of the power equipment according to the service life of the equipment and the unit price information;
and determining the plan feedback parameters corresponding to the unit price information according to a preset parameter model.
5. The method of claim 3, wherein the candidate maintenance plan further comprises: maintaining information, wherein determining consumption parameters according to the maintenance time points corresponding to the candidate maintenance plans comprises:
determining maintenance times according to the maintenance time points;
and determining the consumption parameters according to the maintenance information and the maintenance times, wherein the maintenance information comprises information of maintenance personnel and/or spare parts.
6. The method of claim 3, wherein calculating the feedback parameters for the candidate maintenance plan based on the plan feedback parameters and the consumption parameters comprises:
and calculating the difference between the plan feedback parameter and the consumption parameter, and determining the feedback parameter of the candidate maintenance plan.
7. The method of claim 1, wherein the selecting the target maintenance plan from the plurality of candidate maintenance plans according to the feedback parameter corresponding to each candidate maintenance plan comprises:
and comparing the feedback parameters corresponding to each candidate maintenance plan, and selecting the candidate maintenance plan with the highest feedback parameter as the target maintenance plan.
8. An apparatus for predicting a maintenance plan, the apparatus comprising:
the system comprises a data acquisition module, a maintenance scheduling module and a maintenance scheduling module, wherein the data acquisition module is used for acquiring sensor information of the power equipment and a plurality of candidate maintenance schedules, and the candidate maintenance schedules comprise maintenance time points;
the prediction module is used for inputting the sensor information and the candidate maintenance plan into a constructed prediction model to obtain the equipment life output by the prediction model and corresponding to the candidate maintenance plan;
the operation module is used for calculating feedback parameters corresponding to the candidate maintenance plans according to the equipment service lives and the maintenance time points corresponding to the candidate maintenance plans aiming at each candidate maintenance plan;
and the selection module is used for selecting a target maintenance plan from the multiple candidate maintenance plans according to the feedback parameters corresponding to each candidate maintenance plan.
9. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the method of predicting a maintenance plan of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that the storage medium stores a computer program executable by a processor to perform the method of predicting a maintenance plan according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010458063.7A CN111369079A (en) | 2020-05-27 | 2020-05-27 | Maintenance plan prediction method and device, electronic device, and storage medium |
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| CN102782318A (en) * | 2010-02-05 | 2012-11-14 | 维斯塔斯风力系统集团公司 | Method of operating a wind power plant |
| CN107358299A (en) * | 2017-06-16 | 2017-11-17 | 杭州培慕科技有限公司 | Pre-emptive maintenace closed-loop policy based on fault mode |
| CN107437135A (en) * | 2016-05-26 | 2017-12-05 | 中国电力科学研究院 | A kind of novel energy-storing selection method |
| CN109117566A (en) * | 2018-08-24 | 2019-01-01 | 中国电子科技集团公司第三十六研究所 | A kind of Combined maintenance planing method based on Survey of product life prediction model |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102782318A (en) * | 2010-02-05 | 2012-11-14 | 维斯塔斯风力系统集团公司 | Method of operating a wind power plant |
| CN107437135A (en) * | 2016-05-26 | 2017-12-05 | 中国电力科学研究院 | A kind of novel energy-storing selection method |
| CN107358299A (en) * | 2017-06-16 | 2017-11-17 | 杭州培慕科技有限公司 | Pre-emptive maintenace closed-loop policy based on fault mode |
| CN109117566A (en) * | 2018-08-24 | 2019-01-01 | 中国电子科技集团公司第三十六研究所 | A kind of Combined maintenance planing method based on Survey of product life prediction model |
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