CN117993887A - Intelligent decision method, system and medium based on optimization control - Google Patents
Intelligent decision method, system and medium based on optimization control Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000005457 optimization Methods 0.000 title claims description 46
- 238000012423 maintenance Methods 0.000 claims abstract description 421
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- 238000013468 resource allocation Methods 0.000 claims abstract description 39
- 238000011056 performance test Methods 0.000 claims description 14
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Abstract
The application provides an intelligent decision method, an intelligent decision system and an intelligent decision medium based on optimal control. The method comprises the following steps: acquiring equipment historical fault record data, fault maintenance record data and equipment daily maintenance data, acquiring equipment operation parameter data, processing according to the equipment operation parameter data, the equipment historical fault record data and the equipment daily maintenance data, acquiring equipment economic value data, processing according to the equipment economic value data, the equipment historical fault record data and the equipment fault risk prediction grade, generating equipment maintenance priority, acquiring maintenance resource data, processing according to the maintenance resource data, the equipment maintenance priority and the historical maintenance record data, acquiring maintenance resource allocation data, and generating an optimal maintenance scheme according to the fault risk prediction grade and the maintenance resource allocation data; the application achieves the purpose of improving the fault maintenance efficiency and accuracy by intelligently acquiring the optimal equipment maintenance scheme.
Description
Technical Field
The application relates to the technical field of big data and intelligent decision, in particular to an intelligent decision method, an intelligent decision system and an intelligent decision medium based on optimal control.
Background
In equipment maintenance, the generation of a fault maintenance scheme and a daily maintenance scheme is always a problem puzzling engineers and equipment administrators, and the traditional fault diagnosis method is time-consuming, labor-consuming and low in efficiency, however, the existing method for automatically generating the fault maintenance scheme and the daily maintenance scheme is often insufficient in self-adaptation capability, and the aim of self-adaptation matching according to a fault prediction result and an importance index of equipment and combining with factors such as maintenance resources to obtain an optimal fault maintenance scheme cannot be achieved.
In view of the above problems, an effective technical solution is currently needed.
Disclosure of Invention
The application aims to provide an intelligent decision method, an intelligent decision system and an intelligent decision medium based on optimal control, which are used for carrying out optimal treatment on a fault maintenance strategy, maintenance resource allocation data and a historical maintenance scheme through an optimal control technology to obtain an optimal equipment maintenance scheme, so that the purposes of improving the accuracy and the effectiveness of the fault diagnosis and the maintenance scheme and reducing the maintenance cost are achieved.
The application also provides an intelligent decision method based on the optimization control, which comprises the following steps:
Acquiring historical fault record data of equipment, fault maintenance record data and daily maintenance data of the equipment;
Acquiring equipment operation parameter data, and analyzing and processing according to the equipment operation parameter data in combination with the equipment historical fault record data and the equipment daily maintenance data to acquire a fault risk prediction grade;
Acquiring equipment economic value data, analyzing and processing according to the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction grade, and generating equipment maintenance priority;
Acquiring maintenance resource data, and processing according to the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data to acquire maintenance resource allocation data;
and generating an optimal equipment maintenance scheme according to the fault risk prediction grade and the maintenance resource allocation data.
Optionally, in the intelligent decision method based on optimization control according to the present application, the obtaining the equipment history fault record data, the fault maintenance record data and the equipment daily maintenance data includes:
Acquiring historical fault record data of equipment, fault maintenance record data and daily maintenance data of the equipment;
The equipment history fault record data comprises fault time data, fault frequency data, fault position data and fault influence degree data;
the fault maintenance record data comprises historical maintenance scheme data, maintenance cost data and maintenance efficiency data;
The equipment daily maintenance data comprise equipment daily maintenance scheme data and equipment daily maintenance record data;
The daily maintenance record data of the equipment comprises maintenance time data, maintenance sub-item data and equipment performance test data.
Optionally, in the intelligent decision method based on optimization control of the present application, the obtaining the equipment operation parameter data, and according to the equipment operation parameter data, combining the equipment historical fault record data with the equipment daily maintenance data to perform analysis processing, obtaining a fault risk prediction level includes:
Acquiring equipment operation parameter data, including equipment accuracy data, equipment operation efficiency data and equipment energy consumption data;
Inputting the equipment accuracy data, the equipment operation efficiency data and the equipment energy consumption data into a preset fault risk prediction model for analysis and processing by combining the fault time data, the fault frequency data, the fault position data and the maintenance item data and the equipment performance test data to obtain a fault risk prediction index;
and carrying out threshold comparison on the fault risk prediction index and a preset fault risk prediction index threshold value, and determining a fault risk prediction grade according to the range grade to which the threshold value comparison result belongs.
Optionally, in the intelligent decision method based on optimization control of the present application, the obtaining the economic value data of the device, and performing analysis processing according to the economic value data of the device in combination with the historical fault record data of the device and the fault risk prediction level, to generate a device maintenance priority, includes:
Acquiring equipment economic value data, and processing according to the equipment economic value data and the fault influence degree data to acquire an equipment importance index;
Threshold value comparison is carried out on the equipment importance index and a preset equipment importance index threshold value, and equipment importance grade is determined according to the range grade to which the threshold value comparison result belongs;
Processing according to the equipment importance level and the fault risk prediction level to generate equipment maintenance priority;
The program processing formula of the equipment maintenance priority is as follows:
Wherein P a is equipment maintenance priority, Q d is equipment importance level, T h is failure risk prediction level, and delta and gamma are preset characteristic coefficients.
Optionally, in the intelligent decision method based on optimization control of the present application, the obtaining maintenance resource data, processing according to the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data, obtaining maintenance resource allocation data includes:
Acquiring maintenance resource data, including maintenance personnel data, maintenance tool data and maintenance material data;
And inputting the maintenance personnel data, the maintenance tool data, the maintenance material data, the equipment maintenance priority and the maintenance cost data into a preset maintenance strategy database for matching and identifying to obtain maintenance resource allocation data.
Optionally, in the intelligent decision method based on optimization control according to the present application, the generating an optimal equipment maintenance scheme according to the fault risk prediction level and the maintenance resource allocation data includes:
Inputting the fault risk prediction grade into a preset maintenance strategy database for matching and identifying to obtain fault maintenance strategy data;
Optimizing the fault maintenance strategy data, the maintenance resource allocation data and the historical maintenance scheme data by using a preset optimal control algorithm to obtain optimal equipment maintenance scheme data;
and carrying out fault maintenance on the equipment according to the optimal equipment maintenance scheme data.
Optionally, in the intelligent decision method based on optimization control according to the present application, the method further includes:
inputting the optimal equipment maintenance scheme data and the maintenance time data and maintenance item data into a preset equipment maintenance optimal configuration model for processing to obtain equipment maintenance reset data;
resetting and updating the daily maintenance scheme data of the equipment according to the equipment maintenance reset data to obtain equipment maintenance optimization data;
and optimizing and maintaining the equipment according to the equipment maintenance optimizing data.
In a second aspect, the present application provides an intelligent decision system based on optimization control, the system comprising: the intelligent decision method based on the optimization control comprises a memory and a processor, wherein the memory comprises a program of the intelligent decision method based on the optimization control, and the program of the intelligent decision method based on the optimization control realizes the following steps when being executed by the processor:
Acquiring historical fault record data of equipment, fault maintenance record data and daily maintenance data of the equipment;
Acquiring equipment operation parameter data, and analyzing and processing according to the equipment operation parameter data in combination with the equipment historical fault record data and the equipment daily maintenance data to acquire a fault risk prediction grade;
Acquiring equipment economic value data, analyzing and processing according to the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction grade, and generating equipment maintenance priority;
Acquiring maintenance resource data, and processing according to the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data to acquire maintenance resource allocation data;
and generating an optimal equipment maintenance scheme according to the fault risk prediction grade and the maintenance resource allocation data.
Optionally, in the intelligent decision system based on optimization control according to the present application, the obtaining the equipment history fault record data, the fault maintenance record data and the equipment daily maintenance data includes:
Acquiring historical fault record data of equipment, fault maintenance record data and daily maintenance data of the equipment;
The equipment history fault record data comprises fault time data, fault frequency data, fault position data and fault influence degree data;
the fault maintenance record data comprises historical maintenance scheme data, maintenance cost data and maintenance efficiency data;
The equipment daily maintenance data comprise equipment daily maintenance scheme data and equipment daily maintenance record data;
The daily maintenance record data of the equipment comprises maintenance time data, maintenance sub-item data and equipment performance test data.
In a third aspect, the present application also provides a computer readable storage medium, in which an intelligent decision method program based on optimization control is included, which when executed by a processor, implements the steps of the intelligent decision method based on optimization control as described in any one of the above.
According to the intelligent decision method, the intelligent decision system and the intelligent decision medium based on the optimization control, disclosed by the application, the fault maintenance strategy, the maintenance resource allocation data and the historical maintenance scheme are subjected to optimization treatment through an optimization control technology, so that the optimal equipment maintenance scheme is obtained, and the purposes of improving the accuracy and the effectiveness of the fault diagnosis and the maintenance scheme and reducing the maintenance cost are realized.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an intelligent decision method based on optimization control provided by an embodiment of the application;
FIG. 2 is a flow chart of obtaining a failure risk prediction level according to an intelligent decision method based on optimization control according to an embodiment of the present application;
FIG. 3 is a flow chart of generating equipment maintenance priorities based on an intelligent decision method of optimization control provided by an embodiment of the present application;
Fig. 4 is a flowchart of obtaining maintenance resource allocation data according to an intelligent decision method based on optimization control 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 completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a flowchart of an intelligent decision method based on optimization control according to some embodiments of the application. The intelligent decision method based on the optimal control is used in terminal equipment, such as a computer, a mobile phone terminal and the like. The intelligent decision method based on the optimization control comprises the following steps:
S11, acquiring historical fault record data, fault maintenance record data and daily maintenance data of equipment;
s12, acquiring equipment operation parameter data, and analyzing and processing according to the equipment operation parameter data and combining the equipment historical fault record data and the equipment daily maintenance data to acquire a fault risk prediction grade;
S13, acquiring economic value data of equipment, and analyzing and processing according to the economic value data of the equipment in combination with the historical fault record data of the equipment and the fault risk prediction level to generate equipment maintenance priority;
s14, obtaining maintenance resource data, and processing according to the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data to obtain maintenance resource allocation data;
and S15, generating an optimal equipment maintenance scheme according to the fault risk prediction grade and the maintenance resource allocation data.
The application carries out self-adaptive matching according to the fault prediction result, the importance index of the equipment and the maintenance resource to obtain the optimal fault maintenance scheme, thereby realizing the purposes of improving the accuracy and the effectiveness of the fault diagnosis and the maintenance scheme and reducing the maintenance cost.
According to an embodiment of the present invention, the obtaining equipment history fault record data, fault maintenance record data and equipment daily maintenance data includes:
Acquiring historical fault record data of equipment, fault maintenance record data and daily maintenance data of the equipment;
The equipment history fault record data comprises fault time data, fault frequency data, fault position data and fault influence degree data;
the fault maintenance record data comprises historical maintenance scheme data, maintenance cost data and maintenance efficiency data;
The equipment daily maintenance data comprise equipment daily maintenance scheme data and equipment daily maintenance record data;
The daily maintenance record data of the equipment comprises maintenance time data, maintenance sub-item data and equipment performance test data.
In order to predict the risk of occurrence of a fault in the equipment, it is necessary to acquire equipment history fault record data, fault maintenance record data, and equipment daily maintenance data.
Referring to fig. 2, fig. 2 is a flowchart of a method for obtaining a failure risk prediction level based on an intelligent decision method of optimization control according to some embodiments of the application. According to the embodiment of the application, the acquiring the equipment operation parameter data, analyzing and processing are performed according to the equipment operation parameter data in combination with the equipment history fault record data and the equipment daily maintenance data to obtain a fault risk prediction grade, and the method comprises the following steps:
S21, acquiring equipment operation parameter data, including equipment accuracy data, equipment operation efficiency data and equipment energy consumption data;
S22, inputting the equipment accuracy data, the equipment operation efficiency data and the equipment energy consumption data into a preset fault risk prediction model by combining the fault time data, the fault frequency data, the fault position data and the maintenance item data and the equipment performance test data to be analyzed and processed to obtain a fault risk prediction index;
S23, comparing the fault risk prediction index with a preset fault risk prediction index threshold value, and determining a fault risk prediction grade according to the range grade to which the threshold value comparison result belongs.
The device accuracy data, the device operation efficiency data, the device energy consumption data are combined with fault time data, fault frequency data, fault location data, maintenance item data and device performance test data to be input into a preset fault risk prediction model for analysis and processing, so as to obtain a fault risk prediction index, wherein the preset fault risk prediction model is a model obtained by obtaining a large number of historical samples of device accuracy data, device operation efficiency data, device energy consumption data, fault time data, fault frequency data, fault location data, maintenance item data, device performance test data and fault risk prediction index training, and the corresponding output fault risk prediction index can be obtained by inputting relevant information for processing.
Referring to fig. 3, fig. 3 is a flow chart illustrating a method for generating equipment maintenance priorities based on an intelligent decision method for optimizing control according to some embodiments of the present application. According to the embodiment of the application, the acquiring the economic value data of the equipment, analyzing and processing are performed according to the economic value data of the equipment in combination with the historical fault record data of the equipment and the fault risk prediction level, and generating the equipment maintenance priority comprises the following steps:
S31, acquiring economic value data of equipment, and processing according to the economic value data of the equipment and the fault influence degree data to acquire an importance index of the equipment;
S32, comparing the equipment importance index with a preset equipment importance index threshold value in a threshold value, and determining the equipment importance level according to the range level to which the threshold value comparison result belongs;
S33, processing according to the equipment importance level and the fault risk prediction level to generate equipment maintenance priority;
The program processing formula of the equipment maintenance priority is as follows:
Wherein, P a is the equipment maintenance priority, Q d is the equipment importance level, T h is the failure risk prediction level, and δ and γ are preset characteristic coefficients (which can be obtained by querying a preset maintenance policy database).
The economic value data of the equipment is obtained, and the equipment importance index is obtained by processing the economic value data of the equipment and the fault influence degree data;
the program processing formula of the device importance index is as follows:
Wherein, W f is an equipment importance index, R m is equipment economic value data, Z n is fault influence degree data, and Φ, λ, and Δ k are preset characteristic coefficients (which can be obtained by a preset maintenance strategy database query).
Referring to fig. 4, fig. 4 is a flowchart of obtaining repair resource allocation data according to an intelligent decision method based on optimization control in some embodiments of the application. According to an embodiment of the present application, the obtaining maintenance resource data, processing according to the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data, obtaining maintenance resource allocation data includes:
S41, acquiring maintenance resource data, including maintenance personnel data, maintenance tool data and maintenance material data;
s42, inputting the maintenance personnel data, the maintenance tool data, the maintenance material data, the equipment maintenance priority and the maintenance cost data into a preset maintenance strategy database for matching and identifying, and obtaining maintenance resource allocation data.
It should be noted that, the maintenance resource allocation data is obtained according to the maintenance resource, the equipment maintenance priority and the history maintenance record processing, so that the maintenance resource is allocated better, and the maintenance cost is saved.
According to an embodiment of the present invention, the generating an optimal equipment maintenance scheme according to the failure risk prediction level and the maintenance resource allocation data includes:
Inputting the fault risk prediction grade into a preset maintenance strategy database for matching and identifying to obtain fault maintenance strategy data;
Optimizing the fault maintenance strategy data, the maintenance resource allocation data and the historical maintenance scheme data by using a preset optimal control algorithm to obtain optimal equipment maintenance scheme data;
and carrying out fault maintenance on the equipment according to the optimal equipment maintenance scheme data.
The fault maintenance strategy, maintenance resource allocation data and the historical maintenance scheme are optimally processed according to the optimal control technology, so that the optimal equipment maintenance scheme is obtained, and the purposes of improving the accuracy and the effectiveness of the fault diagnosis and the maintenance scheme and reducing the maintenance cost are achieved.
According to an embodiment of the present invention, further comprising:
inputting the optimal equipment maintenance scheme data and the maintenance time data and maintenance item data into a preset equipment maintenance optimal configuration model for processing to obtain equipment maintenance reset data;
resetting and updating the daily maintenance scheme data of the equipment according to the equipment maintenance reset data to obtain equipment maintenance optimization data;
and optimizing and maintaining the equipment according to the equipment maintenance optimizing data.
It should be noted that, the optimal equipment maintenance scheme data, the maintenance time data and the maintenance item data are input into a preset equipment maintenance optimal configuration model for processing, so as to obtain equipment maintenance reset data, and the preset equipment maintenance optimal configuration model is a model obtained by obtaining a large number of historical samples of the optimal equipment maintenance scheme data, the maintenance time data, the maintenance item data and the equipment maintenance reset data, and can obtain the correspondingly output equipment maintenance reset data by inputting relevant information for processing.
The invention also discloses an intelligent decision system based on the optimization control, which comprises a memory and a processor, wherein the memory comprises an intelligent decision method program based on the optimization control, and the intelligent decision method program based on the optimization control realizes the following steps when being executed by the processor:
Acquiring historical fault record data of equipment, fault maintenance record data and daily maintenance data of the equipment;
Acquiring equipment operation parameter data, and analyzing and processing according to the equipment operation parameter data in combination with the equipment historical fault record data and the equipment daily maintenance data to acquire a fault risk prediction grade;
Acquiring equipment economic value data, analyzing and processing according to the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction grade, and generating equipment maintenance priority;
Acquiring maintenance resource data, and processing according to the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data to acquire maintenance resource allocation data;
and generating an optimal equipment maintenance scheme according to the fault risk prediction grade and the maintenance resource allocation data.
The application carries out self-adaptive matching according to the fault prediction result, the importance index of the equipment and the maintenance resource to obtain the optimal fault maintenance scheme, thereby realizing the purposes of improving the accuracy and the effectiveness of the fault diagnosis and the maintenance scheme and reducing the maintenance cost.
According to an embodiment of the present invention, the obtaining equipment history fault record data, fault maintenance record data and equipment daily maintenance data includes:
Acquiring historical fault record data of equipment, fault maintenance record data and daily maintenance data of the equipment;
The equipment history fault record data comprises fault time data, fault frequency data, fault position data and fault influence degree data;
the fault maintenance record data comprises historical maintenance scheme data, maintenance cost data and maintenance efficiency data;
The equipment daily maintenance data comprise equipment daily maintenance scheme data and equipment daily maintenance record data;
The daily maintenance record data of the equipment comprises maintenance time data, maintenance sub-item data and equipment performance test data.
In order to predict the risk of occurrence of a fault in the equipment, it is necessary to acquire equipment history fault record data, fault maintenance record data, and equipment daily maintenance data.
According to the embodiment of the invention, the acquiring the equipment operation parameter data, analyzing and processing are performed according to the equipment operation parameter data in combination with the equipment history fault record data and the equipment daily maintenance data to obtain a fault risk prediction grade, and the method comprises the following steps:
Acquiring equipment operation parameter data, including equipment accuracy data, equipment operation efficiency data and equipment energy consumption data;
Inputting the equipment accuracy data, the equipment operation efficiency data and the equipment energy consumption data into a preset fault risk prediction model for analysis and processing by combining the fault time data, the fault frequency data, the fault position data and the maintenance item data and the equipment performance test data to obtain a fault risk prediction index;
and carrying out threshold comparison on the fault risk prediction index and a preset fault risk prediction index threshold value, and determining a fault risk prediction grade according to the range grade to which the threshold value comparison result belongs.
The device accuracy data, the device operation efficiency data, the device energy consumption data are combined with fault time data, fault frequency data, fault location data, maintenance item data and device performance test data to be input into a preset fault risk prediction model for analysis and processing, so as to obtain a fault risk prediction index, wherein the preset fault risk prediction model is a model obtained by obtaining a large number of historical samples of device accuracy data, device operation efficiency data, device energy consumption data, fault time data, fault frequency data, fault location data, maintenance item data, device performance test data and fault risk prediction index training, and the corresponding output fault risk prediction index can be obtained by inputting relevant information for processing.
According to the embodiment of the invention, the acquiring the economic value data of the equipment, analyzing and processing are performed according to the economic value data of the equipment in combination with the historical fault record data of the equipment and the fault risk prediction level, and generating the equipment maintenance priority comprises the following steps:
Acquiring equipment economic value data, and processing according to the equipment economic value data and the fault influence degree data to acquire an equipment importance index;
Threshold value comparison is carried out on the equipment importance index and a preset equipment importance index threshold value, and equipment importance grade is determined according to the range grade to which the threshold value comparison result belongs;
Processing according to the equipment importance level and the fault risk prediction level to generate equipment maintenance priority;
The program processing formula of the equipment maintenance priority is as follows:
Wherein, P a is the equipment maintenance priority, Q d is the equipment importance level, T h is the failure risk prediction level, and δ and γ are preset characteristic coefficients (which can be obtained by querying a preset maintenance policy database).
The economic value data of the equipment is obtained, and the equipment importance index is obtained by processing the economic value data of the equipment and the fault influence degree data;
the program processing formula of the device importance index is as follows:
Wherein, W f is an equipment importance index, R m is equipment economic value data, Z n is fault influence degree data, and Φ, λ, and Δ k are preset characteristic coefficients (which can be obtained by a preset maintenance strategy database query).
According to an embodiment of the present invention, the obtaining maintenance resource data, processing according to the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data, obtaining maintenance resource allocation data includes:
Acquiring maintenance resource data, including maintenance personnel data, maintenance tool data and maintenance material data;
And inputting the maintenance personnel data, the maintenance tool data, the maintenance material data, the equipment maintenance priority and the maintenance cost data into a preset maintenance strategy database for matching and identifying to obtain maintenance resource allocation data.
It should be noted that, the maintenance resource allocation data is obtained according to the maintenance resource, the equipment maintenance priority and the history maintenance record processing, so that the maintenance resource is allocated better, and the maintenance cost is saved.
According to an embodiment of the present invention, the generating an optimal equipment maintenance scheme according to the failure risk prediction level and the maintenance resource allocation data includes:
Inputting the fault risk prediction grade into a preset maintenance strategy database for matching and identifying to obtain fault maintenance strategy data;
Optimizing the fault maintenance strategy data, the maintenance resource allocation data and the historical maintenance scheme data by using a preset optimal control algorithm to obtain optimal equipment maintenance scheme data;
and carrying out fault maintenance on the equipment according to the optimal equipment maintenance scheme data.
The fault maintenance strategy, maintenance resource allocation data and the historical maintenance scheme are optimally processed according to the optimal control technology, so that the optimal equipment maintenance scheme is obtained, and the purposes of improving the accuracy and the effectiveness of the fault diagnosis and the maintenance scheme and reducing the maintenance cost are achieved.
According to an embodiment of the present invention, further comprising:
inputting the optimal equipment maintenance scheme data and the maintenance time data and maintenance item data into a preset equipment maintenance optimal configuration model for processing to obtain equipment maintenance reset data;
resetting and updating the daily maintenance scheme data of the equipment according to the equipment maintenance reset data to obtain equipment maintenance optimization data;
and optimizing and maintaining the equipment according to the equipment maintenance optimizing data.
It should be noted that, the optimal equipment maintenance scheme data, the maintenance time data and the maintenance item data are input into a preset equipment maintenance optimal configuration model for processing, so as to obtain equipment maintenance reset data, and the preset equipment maintenance optimal configuration model is a model obtained by obtaining a large number of historical samples of the optimal equipment maintenance scheme data, the maintenance time data, the maintenance item data and the equipment maintenance reset data, and can obtain the correspondingly output equipment maintenance reset data by inputting relevant information for processing.
A third aspect of the present invention provides a readable storage medium having embodied therein an intelligent decision method program based on optimization control, which when executed by a processor, implements the steps of the intelligent decision method based on optimization control as described in any one of the above.
According to the intelligent decision method, system and medium based on the optimal control, disclosed by the invention, the optimal equipment maintenance scheme is obtained by performing the optimal treatment on the fault maintenance strategy, the maintenance resource allocation data and the historical maintenance scheme through the optimal control technology, so that the purposes of improving the accuracy and the effectiveness of the fault diagnosis and the maintenance scheme and reducing the maintenance cost are realized.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or optical disk, or the like, which can store program codes.
Or the above-described integrated units of the invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
Claims (10)
1. An intelligent decision method based on optimization control is characterized by comprising the following steps:
Acquiring historical fault record data of equipment, fault maintenance record data and daily maintenance data of the equipment;
Acquiring equipment operation parameter data, and analyzing and processing according to the equipment operation parameter data in combination with the equipment historical fault record data and the equipment daily maintenance data to acquire a fault risk prediction grade;
Acquiring equipment economic value data, analyzing and processing according to the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction grade, and generating equipment maintenance priority;
Acquiring maintenance resource data, and processing according to the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data to acquire maintenance resource allocation data;
and generating an optimal equipment maintenance scheme according to the fault risk prediction grade and the maintenance resource allocation data.
2. The intelligent decision-making method based on optimized control according to claim 1, wherein said obtaining equipment history fault record data, fault maintenance record data and equipment routine maintenance data comprises:
Acquiring historical fault record data of equipment, fault maintenance record data and daily maintenance data of the equipment;
The equipment history fault record data comprises fault time data, fault frequency data, fault position data and fault influence degree data;
the fault maintenance record data comprises historical maintenance scheme data, maintenance cost data and maintenance efficiency data;
The equipment daily maintenance data comprise equipment daily maintenance scheme data and equipment daily maintenance record data;
The daily maintenance record data of the equipment comprises maintenance time data, maintenance sub-item data and equipment performance test data.
3. The intelligent decision-making method based on optimization control according to claim 2, wherein the obtaining the equipment operation parameter data, and analyzing and processing according to the equipment operation parameter data in combination with the equipment historical fault record data and the equipment daily maintenance data to obtain a fault risk prediction level, includes:
Acquiring equipment operation parameter data, including equipment accuracy data, equipment operation efficiency data and equipment energy consumption data;
Inputting the equipment accuracy data, the equipment operation efficiency data and the equipment energy consumption data into a preset fault risk prediction model for analysis and processing by combining the fault time data, the fault frequency data, the fault position data and the maintenance item data and the equipment performance test data to obtain a fault risk prediction index;
and carrying out threshold comparison on the fault risk prediction index and a preset fault risk prediction index threshold value, and determining a fault risk prediction grade according to the range grade to which the threshold value comparison result belongs.
4. The intelligent decision-making method based on optimization control according to claim 3, wherein the obtaining the economic value data of the equipment, analyzing and processing according to the economic value data of the equipment in combination with the historical fault record data of the equipment and the fault risk prediction level, and generating the equipment maintenance priority, comprises:
Acquiring equipment economic value data, and processing according to the equipment economic value data and the fault influence degree data to acquire an equipment importance index;
Threshold value comparison is carried out on the equipment importance index and a preset equipment importance index threshold value, and equipment importance grade is determined according to the range grade to which the threshold value comparison result belongs;
Processing according to the equipment importance level and the fault risk prediction level to generate equipment maintenance priority;
The program processing formula of the equipment maintenance priority is as follows:
Wherein P a is equipment maintenance priority, Q d is equipment importance level, T h is failure risk prediction level, and delta and gamma are preset characteristic coefficients.
5. The intelligent decision-making method based on optimization control according to claim 4, wherein the obtaining maintenance resource data, processing according to the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data, obtaining maintenance resource allocation data, comprises:
Acquiring maintenance resource data, including maintenance personnel data, maintenance tool data and maintenance material data;
And inputting the maintenance personnel data, the maintenance tool data, the maintenance material data, the equipment maintenance priority and the maintenance cost data into a preset maintenance strategy database for matching and identifying to obtain maintenance resource allocation data.
6. The intelligent decision-making method based on optimal control according to claim 5, wherein generating an optimal equipment maintenance solution according to the failure risk prediction level and the maintenance resource allocation data comprises:
Inputting the fault risk prediction grade into a preset maintenance strategy database for matching and identifying to obtain fault maintenance strategy data;
Optimizing the fault maintenance strategy data, the maintenance resource allocation data and the historical maintenance scheme data by using a preset optimal control algorithm to obtain optimal equipment maintenance scheme data;
and carrying out fault maintenance on the equipment according to the optimal equipment maintenance scheme data.
7. The intelligent decision-making method based on optimization control according to claim 6, further comprising:
inputting the optimal equipment maintenance scheme data and the maintenance time data and maintenance item data into a preset equipment maintenance optimal configuration model for processing to obtain equipment maintenance reset data;
resetting and updating the daily maintenance scheme data of the equipment according to the equipment maintenance reset data to obtain equipment maintenance optimization data;
and optimizing and maintaining the equipment according to the equipment maintenance optimizing data.
8. An intelligent decision system based on optimization control, comprising a memory and a processor, wherein the memory contains a program of an intelligent decision method based on optimization control, and the program of the intelligent decision method based on optimization control realizes the following steps when executed by the processor:
Acquiring historical fault record data of equipment, fault maintenance record data and daily maintenance data of the equipment;
Acquiring equipment operation parameter data, and analyzing and processing according to the equipment operation parameter data in combination with the equipment historical fault record data and the equipment daily maintenance data to acquire a fault risk prediction grade;
Acquiring equipment economic value data, analyzing and processing according to the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction grade, and generating equipment maintenance priority;
Acquiring maintenance resource data, and processing according to the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data to acquire maintenance resource allocation data;
and generating an optimal equipment maintenance scheme according to the fault risk prediction grade and the maintenance resource allocation data.
9. The intelligent decision-making system based on optimal control according to claim 8, wherein said obtaining equipment history fault record data, fault maintenance record data and equipment routine maintenance data comprises:
Acquiring historical fault record data of equipment, fault maintenance record data and daily maintenance data of the equipment;
The equipment history fault record data comprises fault time data, fault frequency data, fault position data and fault influence degree data;
the fault maintenance record data comprises historical maintenance scheme data, maintenance cost data and maintenance efficiency data;
The equipment daily maintenance data comprise equipment daily maintenance scheme data and equipment daily maintenance record data;
The daily maintenance record data of the equipment comprises maintenance time data, maintenance sub-item data and equipment performance test data.
10. A computer readable storage medium, characterized in that it comprises an intelligent decision program based on optimization control, which, when executed by a processor, implements the steps of the intelligent decision method based on optimization control according to any one of claims 1 to 7.
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