An intelligent decision-making method, system and medium based on optimal control
Through intelligent decision-making methods based on optimization control, equipment historical fault records and operating parameters are obtained, fault risk prediction and resource allocation are carried out, and the best maintenance solution is generated, which solves the problem of inefficiency of traditional methods and improves fault diagnosis and maintenance solutions. Accuracy and effectiveness reduce maintenance costs.
Patent Information
- Application Number
- CN202410072947.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-18
- Publication Date
- 2024-12-24
- Estimated Expiration
- 2044-01-18
AI Technical Summary
Traditional fault diagnosis and maintenance solution generation methods require manual intervention, which is inefficient, and the automatic generation methods cannot adaptively match the maintenance resources based on the fault prediction results and equipment importance indicators, and the best fault repair solution cannot be obtained.
Using an intelligent decision-making method based on optimization control, we use equipment historical fault records, operating parameters and maintenance resource data to obtain equipment history, fault risk prediction, equipment priority setting and resource allocation, and optimize the processing to generate the best maintenance plan.
It improves the accuracy and effectiveness of fault diagnosis and maintenance plans, reduces maintenance costs, and achieves adaptive matching with maintenance resources based on fault prediction results and equipment importance indicators.
Smart Images
Figure CN117993887B_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of big data and intelligent decision-making technology, and specifically, to an intelligent decision-making method, system and medium based on optimization control. Background Art
[0002] In equipment maintenance, the generation of fault repair plans and daily maintenance plans has always been a problem that troubles engineers and equipment managers. Traditional fault diagnosis methods often require manual intervention, which is time-consuming, labor-intensive, and inefficient. However, existing methods for automatically generating fault repair plans and daily maintenance plans often have the problem of insufficient adaptive capabilities, and cannot achieve the purpose of adaptively matching according to fault prediction results and equipment importance indicators combined with factors such as maintenance resources to obtain the best fault repair plan.
[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention
[0004] The purpose of this application is to provide an intelligent decision-making method, system and medium based on optimization control, which optimizes fault repair strategies, maintenance resource allocation data and historical maintenance plans through optimization control technology to obtain the best equipment maintenance plan, thereby improving the accuracy and effectiveness of fault diagnosis and maintenance plans and reducing repair and maintenance costs.
[0005] The present application also provides an intelligent decision-making method based on optimal control, comprising the following steps:
[0006] Obtain equipment historical fault record data, fault repair record data and equipment daily maintenance data;
[0007] Acquire equipment operating parameter data, analyze and process the equipment operating parameter data in combination with the equipment historical fault record data and the equipment daily maintenance data to obtain a fault risk prediction level;
[0008] Acquire equipment economic value data, analyze and process the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction level, and generate equipment maintenance priority;
[0009] Acquire maintenance resource data, and process the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data to obtain maintenance resource deployment data;
[0010] An optimal equipment maintenance plan is generated according to the fault risk prediction level and the maintenance resource deployment data.
[0011] Optionally, in the intelligent decision-making method based on optimal control described in the present application, the acquisition of equipment historical fault record data, fault repair record data and equipment daily maintenance data includes:
[0012] Obtain equipment historical fault record data, fault repair record data and equipment daily maintenance data;
[0013] The equipment historical fault record data includes fault time data, fault frequency data, fault location data and fault impact data;
[0014] The fault maintenance record data includes historical maintenance plan data, maintenance cost data and maintenance efficiency data;
[0015] The equipment daily maintenance data includes equipment daily maintenance plan data and equipment daily maintenance record data;
[0016] The equipment daily maintenance record data includes maintenance time data, maintenance item data and equipment performance test data.
[0017] Optionally, in the intelligent decision-making method based on optimization control described in the present application, the acquiring of equipment operation parameter data, and analyzing and processing the equipment operation parameter data in combination with the equipment historical fault record data and the equipment daily maintenance data to obtain the fault risk prediction level include:
[0018] Obtain equipment operating parameter data, including equipment accuracy data, equipment operating efficiency data, and equipment energy consumption data;
[0019] The equipment accuracy data, equipment operation efficiency data, equipment energy consumption data, combined with the fault time data, fault frequency data, fault location data, maintenance sub-item data, and equipment performance test data are input into a preset fault risk prediction model for analysis and processing to obtain a fault risk prediction index;
[0020] The fault risk prediction index is compared with a preset fault risk prediction index threshold, and the fault risk prediction level is determined according to the range level to which the threshold comparison result belongs.
[0021] Optionally, in the intelligent decision-making method based on optimization control described in the present application, the acquiring of equipment economic value data, analyzing and processing the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction level to generate equipment maintenance priority, includes:
[0022] Acquire equipment economic value data, and process the equipment economic value data and the fault impact data to obtain an equipment importance index;
[0023] Performing a threshold comparison between the device importance index and a preset device importance index threshold, and determining the device importance level according to the range level to which the threshold comparison result belongs;
[0024] Processing is performed according to the equipment importance level and the fault risk prediction level to generate equipment maintenance priority;
[0025] The program processing formula for the equipment maintenance priority is:
[0026]
[0027] Among them, P a For equipment maintenance priority, Q d is the equipment importance level, T h is the fault risk prediction level, δ and γ are the preset characteristic coefficients.
[0028] Optionally, in the intelligent decision-making method based on optimization control described in the present application, the acquisition of 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 deployment data include:
[0029] Acquire maintenance resource data, including maintenance personnel data, maintenance tool data, and maintenance material data;
[0030] The maintenance personnel data, maintenance tool data, maintenance material data, equipment maintenance priority, maintenance cost data, and maintenance efficiency data are input into a preset maintenance strategy database for matching and identification to obtain maintenance resource deployment data.
[0031] Optionally, in the intelligent decision-making method based on optimization control described in the present application, generating the best equipment maintenance plan according to the fault risk prediction level and the maintenance resource deployment data includes:
[0032] Inputting the fault risk prediction level into a preset maintenance strategy database for matching and identification to obtain fault maintenance strategy data;
[0033] Utilizing a preset optimal control algorithm to optimize the fault repair strategy data, the repair resource allocation data, and the historical repair plan data to obtain the best equipment repair plan data;
[0034] The equipment is repaired according to the optimal equipment maintenance plan data.
[0035] Optionally, the intelligent decision-making method based on optimization control described in the present application further includes:
[0036] Inputting the optimal equipment maintenance plan data, the maintenance time data, and the maintenance item data into a preset equipment maintenance optimization configuration model for processing to obtain equipment maintenance reset data;
[0037] Resetting and updating the equipment daily maintenance plan data according to the equipment maintenance reset data to obtain equipment maintenance optimization data;
[0038] The equipment is optimized and maintained according to the equipment maintenance optimization data.
[0039] In a second aspect, the present application provides an intelligent decision-making system based on optimal control, the system comprising: a memory and a processor, the memory comprising a program of an intelligent decision-making method based on optimal control, and the program of the intelligent decision-making method based on optimal control is executed by the processor to implement the following steps:
[0040] Obtain equipment historical fault record data, fault repair record data and equipment daily maintenance data;
[0041] Acquire equipment operating parameter data, analyze and process the equipment operating parameter data in combination with the equipment historical fault record data and the equipment daily maintenance data to obtain a fault risk prediction level;
[0042] Acquire equipment economic value data, analyze and process the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction level, and generate equipment maintenance priority;
[0043] Acquire maintenance resource data, and process the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data to obtain maintenance resource deployment data;
[0044] An optimal equipment maintenance plan is generated according to the fault risk prediction level and the maintenance resource deployment data.
[0045] Optionally, in the intelligent decision-making system based on optimal control described in the present application, the acquisition of equipment historical fault record data, fault repair record data and equipment daily maintenance data includes:
[0046] Obtain equipment historical fault record data, fault repair record data and equipment daily maintenance data;
[0047] The equipment historical fault record data includes fault time data, fault frequency data, fault location data and fault impact data;
[0048] The fault maintenance record data includes historical maintenance plan data, maintenance cost data and maintenance efficiency data;
[0049] The equipment daily maintenance data includes equipment daily maintenance plan data and equipment daily maintenance record data;
[0050] The equipment daily maintenance record data includes maintenance time data, maintenance item data and equipment performance test data.
[0051] In a third aspect, the present application also provides a computer-readable storage medium, which includes an intelligent decision-making method program based on optimal control. When the intelligent decision-making method program based on optimal control is executed by a processor, the steps of the intelligent decision-making method based on optimal control as described in any one of the above items are implemented.
[0052] From the above, it can be seen that the present application provides an intelligent decision-making method, system and medium based on optimization control, which optimizes fault repair strategies, maintenance resource allocation data and historical maintenance plans through optimization control technology to obtain the best equipment maintenance plan, thereby achieving the purpose of improving the accuracy and effectiveness of fault diagnosis and maintenance plans and reducing repair and maintenance costs.
[0053] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.
[0055] Figure 1 A flowchart of an intelligent decision-making method based on optimal control provided in an embodiment of the present application;
[0056] Figure 2 A flow chart of obtaining a fault risk prediction level according to an intelligent decision-making method based on optimal control provided in an embodiment of the present application;
[0057] Figure 3 A flowchart of generating equipment maintenance priority according to an intelligent decision-making method based on optimal control provided in an embodiment of the present application;
[0058] Figure 4 A flowchart of obtaining maintenance resource deployment data according to an intelligent decision-making method based on optimal control provided in an embodiment of the present application. DETAILED DESCRIPTION
[0059] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.
[0060] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.
[0061] Please refer to Figure 1 , Figure 1 This is a flow chart of an intelligent decision-making method based on optimal control in some embodiments of the present application. The intelligent decision-making method based on optimal control is used in terminal devices, such as computers, mobile phone terminals, etc. The intelligent decision-making method based on optimal control includes the following steps:
[0062] S11, obtaining equipment historical fault record data, fault repair record data and equipment daily maintenance data;
[0063] S12, obtaining equipment operation parameter data, analyzing and processing 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;
[0064] S13, obtaining equipment economic value data, analyzing and processing the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction level, and generating equipment maintenance priority;
[0065] S14, obtaining maintenance resource data, and processing the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data to obtain maintenance resource deployment data;
[0066] S15. Generate an optimal equipment maintenance plan according to the fault risk prediction level and the maintenance resource deployment data.
[0067] It should be noted that the present application performs adaptive matching based on fault prediction results, equipment importance indicators and maintenance resources to obtain the best fault repair plan, thereby achieving the purpose of improving the accuracy and effectiveness of fault diagnosis and repair plans and reducing repair and maintenance costs.
[0068] According to an embodiment of the present invention, the acquisition of equipment historical fault record data, fault repair record data and equipment daily maintenance data includes:
[0069] Obtain equipment historical fault record data, fault repair record data and equipment daily maintenance data;
[0070] The equipment historical fault record data includes fault time data, fault frequency data, fault location data and fault impact data;
[0071] The fault maintenance record data includes historical maintenance plan data, maintenance cost data and maintenance efficiency data;
[0072] The equipment daily maintenance data includes equipment daily maintenance plan data and equipment daily maintenance record data;
[0073] The equipment daily maintenance record data includes maintenance time data, maintenance item data and equipment performance test data.
[0074] It should be noted that in order to predict the risk of equipment failure, it is necessary to obtain the equipment's historical failure record data, failure repair record data and equipment daily maintenance data.
[0075] Please refer to Figure 2 , Figure 2 The flowchart of obtaining the fault risk prediction level of the intelligent decision-making method based on optimal control in some embodiments of the present application. According to the embodiment of the present invention, the acquisition of equipment operating parameter data, the analysis and processing based on the equipment operating parameter data combined with the equipment historical fault record data and the equipment daily maintenance data to obtain the fault risk prediction level includes:
[0076] S21. Obtaining equipment operation parameter data, including equipment accuracy data, equipment operation efficiency data, and equipment energy consumption data;
[0077] S22, inputting the equipment accuracy data, equipment operation efficiency data, equipment energy consumption data, the fault time data, the fault frequency data, the fault location data, the maintenance sub-item data, and the equipment performance test data into a preset fault risk prediction model for analysis and processing to obtain a fault risk prediction index;
[0078] S23, performing a threshold comparison between the fault risk prediction index and a preset fault risk prediction index threshold, and determining a fault risk prediction level according to a range level to which the threshold comparison result belongs.
[0079] It should be noted that the equipment accuracy data, equipment operation efficiency data, equipment energy consumption data, combined with the failure time data, failure frequency data, failure location data, maintenance sub-item data, and equipment performance test data are input into a preset failure risk prediction model for analysis and processing to obtain a failure risk prediction index. The preset failure risk prediction model is a model obtained by training with equipment accuracy data, equipment operation efficiency data, equipment energy consumption data, failure time data, failure frequency data, failure location data, maintenance sub-item data, equipment performance test data, and failure risk prediction index of a large number of historical samples. The corresponding output failure risk prediction index can be obtained by inputting relevant information for processing.
[0080] Please refer to Figure 3 , Figure 3 The flowchart of generating equipment maintenance priority of the intelligent decision-making method based on optimization control in some embodiments of the present application. According to the embodiment of the present invention, the acquisition of equipment economic value data, the analysis and processing based on the equipment economic value data combined with the equipment historical fault record data and the fault risk prediction level, and the generation of equipment maintenance priority include:
[0081] S31, obtaining equipment economic value data, and processing the equipment economic value data and the fault impact data to obtain an equipment importance index;
[0082] S32, performing a threshold comparison between the device importance index and a preset device importance index threshold, and determining the device importance level according to the range level to which the threshold comparison result belongs;
[0083] S33, processing according to the equipment importance level and the fault risk prediction level to generate equipment maintenance priority;
[0084] The program processing formula for the equipment maintenance priority is:
[0085]
[0086] Among them, P a For equipment maintenance priority, Q d is the equipment importance level, T h is the fault risk prediction level, δ and γ are preset characteristic coefficients (which can be obtained by querying the preset maintenance strategy database).
[0087] It should be noted that the equipment economic value data is obtained, and the equipment economic value data and the fault impact data are processed to obtain the equipment importance index;
[0088] The program processing formula of the equipment importance index is:
[0089]
[0090] Among them, W f is the equipment importance index, R m is the equipment economic value data, Z n is the fault impact data, φ, λ, Δ k is the preset characteristic coefficient (which can be obtained by querying the preset maintenance strategy database).
[0091] Please refer to Figure 4 , Figure 4 The flowchart of obtaining maintenance resource allocation data of the intelligent decision-making method based on optimization control in some embodiments of the present application. According to the embodiment of the present invention, the obtaining of maintenance resource data, processing according to the maintenance resource data combined with the equipment maintenance priority and the historical maintenance record data to obtain maintenance resource allocation data, includes:
[0092] S41, obtaining maintenance resource data, including maintenance personnel data, maintenance tool data and maintenance material data;
[0093] S42, inputting the maintenance personnel data, maintenance tool data, maintenance material data, equipment maintenance priority, maintenance cost data, and maintenance efficiency data into a preset maintenance strategy database for matching and identification, and obtaining maintenance resource deployment data.
[0094] It should be noted that maintenance resource allocation data is obtained based on maintenance resources, equipment maintenance priorities, and historical maintenance records, so as to better allocate maintenance resources and save maintenance costs.
[0095] According to an embodiment of the present invention, generating an optimal equipment maintenance plan according to the fault risk prediction level and the maintenance resource deployment data includes:
[0096] Inputting the fault risk prediction level into a preset maintenance strategy database for matching and identification to obtain fault maintenance strategy data;
[0097] Utilizing a preset optimal control algorithm to optimize the fault repair strategy data, the repair resource allocation data, and the historical repair plan data to obtain the best equipment repair plan data;
[0098] The equipment is repaired according to the optimal equipment maintenance plan data.
[0099] It should be noted that, according to the optimization control technology, the fault maintenance strategy, the maintenance resource allocation data and the historical maintenance plan are optimized to obtain the best equipment maintenance plan, so as to achieve the purpose of improving the accuracy and effectiveness of the fault diagnosis and maintenance plan and reducing the maintenance cost.
[0100] According to an embodiment of the present invention, it also includes:
[0101] Inputting the optimal equipment maintenance plan data, the maintenance time data, and the maintenance item data into a preset equipment maintenance optimization configuration model for processing to obtain equipment maintenance reset data;
[0102] Resetting and updating the equipment daily maintenance plan data according to the equipment maintenance reset data to obtain equipment maintenance optimization data;
[0103] The equipment is optimized and maintained according to the equipment maintenance optimization data.
[0104] It should be noted that the best equipment maintenance plan data and the maintenance time data and maintenance sub-item data are input into the preset equipment maintenance optimization configuration model for processing to obtain equipment maintenance reset data. The preset equipment maintenance optimization configuration model is a model obtained by training with the best equipment maintenance plan data, maintenance time data, maintenance sub-item data and equipment maintenance reset data of a large number of historical samples. The corresponding output equipment maintenance reset data can be obtained by inputting relevant information for processing.
[0105] The second aspect of the present invention further discloses an intelligent decision-making system based on optimal control, comprising a memory and a processor, wherein the memory comprises an intelligent decision-making method program based on optimal control, and when the intelligent decision-making method program based on optimal control is executed by the processor, the following steps are implemented:
[0106] Obtain equipment historical fault record data, fault repair record data and equipment daily maintenance data;
[0107] Acquire equipment operating parameter data, analyze and process the equipment operating parameter data in combination with the equipment historical fault record data and the equipment daily maintenance data to obtain a fault risk prediction level;
[0108] Acquire equipment economic value data, analyze and process the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction level, and generate equipment maintenance priority;
[0109] Acquire maintenance resource data, and process the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data to obtain maintenance resource deployment data;
[0110] An optimal equipment maintenance plan is generated according to the fault risk prediction level and the maintenance resource deployment data.
[0111] It should be noted that the present application performs adaptive matching based on fault prediction results, equipment importance indicators and maintenance resources to obtain the best fault repair plan, thereby achieving the purpose of improving the accuracy and effectiveness of fault diagnosis and repair plans and reducing repair and maintenance costs.
[0112] According to an embodiment of the present invention, the acquisition of equipment historical fault record data, fault repair record data and equipment daily maintenance data includes:
[0113] Obtain equipment historical fault record data, fault repair record data and equipment daily maintenance data;
[0114] The equipment historical fault record data includes fault time data, fault frequency data, fault location data and fault impact data;
[0115] The fault maintenance record data includes historical maintenance plan data, maintenance cost data and maintenance efficiency data;
[0116] The equipment daily maintenance data includes equipment daily maintenance plan data and equipment daily maintenance record data;
[0117] The equipment daily maintenance record data includes maintenance time data, maintenance item data and equipment performance test data.
[0118] It should be noted that in order to predict the risk of equipment failure, it is necessary to obtain the equipment's historical failure record data, failure repair record data and equipment daily maintenance data.
[0119] According to an embodiment of the present invention, the acquiring of equipment operating parameter data, analyzing and processing the equipment operating 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:
[0120] Obtain equipment operating parameter data, including equipment accuracy data, equipment operating efficiency data, and equipment energy consumption data;
[0121] The equipment accuracy data, equipment operation efficiency data, equipment energy consumption data, combined with the fault time data, fault frequency data, fault location data, maintenance sub-item data, and equipment performance test data are input into a preset fault risk prediction model for analysis and processing to obtain a fault risk prediction index;
[0122] The fault risk prediction index is compared with a preset fault risk prediction index threshold, and the fault risk prediction level is determined according to the range level to which the threshold comparison result belongs.
[0123] It should be noted that the equipment accuracy data, equipment operation efficiency data, equipment energy consumption data, combined with the failure time data, failure frequency data, failure location data, maintenance sub-item data, and equipment performance test data are input into a preset failure risk prediction model for analysis and processing to obtain a failure risk prediction index. The preset failure risk prediction model is a model obtained by training with equipment accuracy data, equipment operation efficiency data, equipment energy consumption data, failure time data, failure frequency data, failure location data, maintenance sub-item data, equipment performance test data, and failure risk prediction index of a large number of historical samples. The corresponding output failure risk prediction index can be obtained by inputting relevant information for processing.
[0124] According to an embodiment of the present invention, the acquiring of equipment economic value data, analyzing and processing the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction level to generate equipment maintenance priority, includes:
[0125] Acquire equipment economic value data, and process the equipment economic value data and the fault impact data to obtain an equipment importance index;
[0126] Performing a threshold comparison between the device importance index and a preset device importance index threshold, and determining the device importance level according to the range level to which the threshold comparison result belongs;
[0127] Processing is performed according to the equipment importance level and the fault risk prediction level to generate equipment maintenance priority;
[0128] The program processing formula for the equipment maintenance priority is:
[0129]
[0130] Among them, P a For equipment maintenance priority, Q d is the equipment importance level, T h is the fault risk prediction level, δ and γ are preset characteristic coefficients (which can be obtained by querying the preset maintenance strategy database).
[0131] It should be noted that the equipment economic value data is obtained, and the equipment economic value data and the fault impact data are processed to obtain the equipment importance index;
[0132] The program processing formula of the equipment importance index is:
[0133]
[0134] Among them, W f is the equipment importance index, R mis the equipment economic value data, Z n is the fault impact data, φ, λ, Δ k is the preset characteristic coefficient (which can be obtained by querying the preset maintenance strategy database).
[0135] According to an embodiment of the present invention, the acquisition of maintenance resource data, processing the maintenance resource data in combination with the equipment maintenance priority and the historical maintenance record data to obtain maintenance resource deployment data, includes:
[0136] Acquire maintenance resource data, including maintenance personnel data, maintenance tool data, and maintenance material data;
[0137] The maintenance personnel data, maintenance tool data, maintenance material data, equipment maintenance priority, maintenance cost data, and maintenance efficiency data are input into a preset maintenance strategy database for matching and identification to obtain maintenance resource deployment data.
[0138] It should be noted that maintenance resource allocation data is obtained based on maintenance resources, equipment maintenance priorities, and historical maintenance records, so as to better allocate maintenance resources and save maintenance costs.
[0139] According to an embodiment of the present invention, generating an optimal equipment maintenance plan according to the fault risk prediction level and the maintenance resource deployment data includes:
[0140] Inputting the fault risk prediction level into a preset maintenance strategy database for matching and identification to obtain fault maintenance strategy data;
[0141] Utilizing a preset optimal control algorithm to optimize the fault repair strategy data, the repair resource allocation data, and the historical repair plan data to obtain the best equipment repair plan data;
[0142] The equipment is repaired according to the optimal equipment maintenance plan data.
[0143] It should be noted that, according to the optimization control technology, the fault maintenance strategy, the maintenance resource allocation data and the historical maintenance plan are optimized to obtain the best equipment maintenance plan, so as to achieve the purpose of improving the accuracy and effectiveness of the fault diagnosis and maintenance plan and reducing the maintenance cost.
[0144] According to an embodiment of the present invention, it also includes:
[0145] Inputting the optimal equipment maintenance plan data, the maintenance time data, and the maintenance item data into a preset equipment maintenance optimization configuration model for processing to obtain equipment maintenance reset data;
[0146] Resetting and updating the equipment daily maintenance plan data according to the equipment maintenance reset data to obtain equipment maintenance optimization data;
[0147] The equipment is optimized and maintained according to the equipment maintenance optimization data.
[0148] It should be noted that the best equipment maintenance plan data and the maintenance time data and maintenance sub-item data are input into the preset equipment maintenance optimization configuration model for processing to obtain equipment maintenance reset data. The preset equipment maintenance optimization configuration model is a model obtained by training with the best equipment maintenance plan data, maintenance time data, maintenance sub-item data and equipment maintenance reset data of a large number of historical samples. The corresponding output equipment maintenance reset data can be obtained by inputting relevant information for processing.
[0149] A third aspect of the present invention provides a readable storage medium, which includes an intelligent decision-making method program based on optimal control. When the intelligent decision-making method program based on optimal control is executed by a processor, the steps of the intelligent decision-making method based on optimal control as described in any one of the above items are implemented.
[0150] The present invention discloses an intelligent decision-making method, system and medium based on optimization control, which optimizes fault repair strategies, repair resource allocation data and historical repair plans through optimization control technology to obtain the best equipment repair plan, thereby achieving the purpose of improving the accuracy and effectiveness of fault diagnosis and repair plans and reducing repair and maintenance costs.
[0151] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.
[0152] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.
[0153] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.
[0154] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.
[0155] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.
Claims
1. An intelligent decision-making method based on optimal control, characterized in that: The following steps are involved: Obtain equipment historical fault record data, fault repair record data and equipment daily maintenance data; Acquire equipment operating parameter data, analyze and process the equipment operating parameter data in combination with the equipment historical fault record data and the equipment daily maintenance data to obtain a fault risk prediction level; Acquire equipment economic value data, analyze and process the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction level, and generate equipment maintenance priority; Acquire maintenance resource data, and process the maintenance resource data in combination with the equipment maintenance priority and the fault maintenance record data to obtain maintenance resource deployment data; Generate an optimal equipment maintenance plan according to the fault risk prediction level and the maintenance resource deployment data; The acquisition of equipment historical fault record data, fault repair record data and equipment daily maintenance data includes: The equipment historical fault record data includes fault time data, fault frequency data, fault location data and fault impact data; The fault maintenance record data includes historical maintenance plan data, maintenance cost data and maintenance efficiency data; The equipment daily maintenance data includes equipment daily maintenance plan data and equipment daily maintenance record data; The equipment daily maintenance record data includes maintenance time data, maintenance item data and equipment performance test data; The acquiring of equipment operation parameter data, and performing analysis and processing based on 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: Obtain equipment operating parameter data, including equipment accuracy data, equipment operating efficiency data, and equipment energy consumption data; The equipment accuracy data, equipment operation efficiency data, equipment energy consumption data, combined with the fault time data, fault frequency data, fault location data, maintenance sub-item data, and equipment performance test data are input into a preset fault risk prediction model for analysis and processing to obtain a fault risk prediction index; Comparing the fault risk prediction index with a preset fault risk prediction index threshold, and determining the fault risk prediction level according to the range level to which the threshold comparison result belongs; The acquiring of equipment economic value data, analyzing and processing the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction level to generate equipment maintenance priority, includes: Acquire equipment economic value data, and process the equipment economic value data and the fault impact data to obtain an equipment importance index; Performing a threshold comparison between the device importance index and a preset device importance index threshold, and determining the device importance level according to the range level to which the threshold comparison result belongs; Processing is performed according to the equipment importance level and the fault risk prediction level to generate equipment maintenance priority; The program processing formula for the equipment maintenance priority is: in, Priority for equipment maintenance, is the equipment importance level, is the failure risk prediction level, , is the preset characteristic coefficient.
2. The intelligent decision-making method based on optimal control according to claim 1 is characterized in that: The obtaining of maintenance resource data, and processing the maintenance resource data in combination with the equipment maintenance priority and the fault maintenance record data to obtain maintenance resource deployment data, includes: Acquire maintenance resource data, including maintenance personnel data, maintenance tool data, and maintenance material data; The maintenance personnel data, maintenance tool data, maintenance material data, equipment maintenance priority, maintenance cost data, and maintenance efficiency data are input into a preset maintenance strategy database for matching and identification to obtain maintenance resource deployment data.
3. The intelligent decision-making method based on optimal control according to claim 2 is characterized in that: The generating of the optimal equipment maintenance plan according to the fault risk prediction level and the maintenance resource deployment data includes: Inputting the fault risk prediction level into a preset maintenance strategy database for matching and identification to obtain fault maintenance strategy data; Utilizing a preset optimal control algorithm to optimize the fault repair strategy data, the repair resource allocation data, and the historical repair plan data to obtain the best equipment repair plan data; The equipment is repaired according to the optimal equipment maintenance plan data.
4. The intelligent decision-making method based on optimal control according to claim 3 is characterized in that: Also includes: Inputting the optimal equipment maintenance plan data, the maintenance time data, and the maintenance item data into a preset equipment maintenance optimization configuration model for processing to obtain equipment maintenance reset data; Resetting and updating the equipment daily maintenance plan data according to the equipment maintenance reset data to obtain equipment maintenance optimization data; The equipment is optimized and maintained according to the equipment maintenance optimization data.
5. An intelligent decision-making system based on optimal control, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a program of an intelligent decision-making method based on optimal control, and when the program of the intelligent decision-making method based on optimal control is executed by the processor, the following steps are implemented: Obtain equipment historical fault record data, fault repair record data and equipment daily maintenance data; Acquire equipment operating parameter data, analyze and process the equipment operating parameter data in combination with the equipment historical fault record data and the equipment daily maintenance data to obtain a fault risk prediction level; Acquire equipment economic value data, analyze and process the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction level, and generate equipment maintenance priority; Acquire maintenance resource data, and process the maintenance resource data in combination with the equipment maintenance priority and the fault maintenance record data to obtain maintenance resource deployment data; Generate an optimal equipment maintenance plan according to the fault risk prediction level and the maintenance resource deployment data; The acquisition of equipment historical fault record data, fault repair record data and equipment daily maintenance data includes: The equipment historical fault record data includes fault time data, fault frequency data, fault location data and fault impact data; The fault maintenance record data includes historical maintenance plan data, maintenance cost data and maintenance efficiency data; The equipment daily maintenance data includes equipment daily maintenance plan data and equipment daily maintenance record data; The equipment daily maintenance record data includes maintenance time data, maintenance item data and equipment performance test data; The acquiring of equipment operation parameter data, and performing analysis and processing based on 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: Obtain equipment operating parameter data, including equipment accuracy data, equipment operating efficiency data, and equipment energy consumption data; The equipment accuracy data, equipment operation efficiency data, equipment energy consumption data, combined with the fault time data, fault frequency data, fault location data, maintenance sub-item data, and equipment performance test data are input into a preset fault risk prediction model for analysis and processing to obtain a fault risk prediction index; Comparing the fault risk prediction index with a preset fault risk prediction index threshold, and determining the fault risk prediction level according to the range level to which the threshold comparison result belongs; The acquiring of equipment economic value data, analyzing and processing the equipment economic value data in combination with the equipment historical fault record data and the fault risk prediction level to generate equipment maintenance priority, includes: Acquire equipment economic value data, and process the equipment economic value data and the fault impact data to obtain an equipment importance index; Performing a threshold comparison between the device importance index and a preset device importance index threshold, and determining the device importance level according to the range level to which the threshold comparison result belongs; Processing is performed according to the equipment importance level and the fault risk prediction level to generate equipment maintenance priority; The program processing formula for the equipment maintenance priority is: in, Priority for equipment maintenance, is the equipment importance level, is the failure risk prediction level, , is the preset characteristic coefficient.
6. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes an intelligent decision-making program based on optimal control. When the intelligent decision-making program based on optimal control is executed by a processor, the steps of the intelligent decision-making method based on optimal control as described in any one of claims 1 to 4 are implemented.