CN116579724B - Automatic safety stock management method and device for workshop spare part library - Google Patents
Automatic safety stock management method and device for workshop spare part library Download PDFInfo
- Publication number
- CN116579724B CN116579724B CN202310408662.1A CN202310408662A CN116579724B CN 116579724 B CN116579724 B CN 116579724B CN 202310408662 A CN202310408662 A CN 202310408662A CN 116579724 B CN116579724 B CN 116579724B
- Authority
- CN
- China
- Prior art keywords
- equipment
- predicted
- list
- spare parts
- spare part
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000007726 management method Methods 0.000 title abstract description 33
- 230000036541 health Effects 0.000 claims abstract description 31
- 238000012423 maintenance Methods 0.000 claims abstract description 29
- 238000000034 method Methods 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000013527 convolutional neural network Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 7
- 238000003745 diagnosis Methods 0.000 abstract description 4
- 230000007246 mechanism Effects 0.000 abstract description 4
- 230000003449 preventive effect Effects 0.000 abstract description 4
- 238000004519 manufacturing process Methods 0.000 description 30
- 238000012545 processing Methods 0.000 description 11
- 238000004590 computer program Methods 0.000 description 7
- 238000007418 data mining Methods 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000008439 repair process Effects 0.000 description 3
- 238000006467 substitution reaction Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 206010019233 Headaches Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000005266 casting Methods 0.000 description 1
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 231100000869 headache Toxicity 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000003754 machining Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000009428 plumbing Methods 0.000 description 1
- 238000003466 welding Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
- G06Q10/0875—Itemisation or classification of parts, supplies or services, e.g. bill of materials
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Data Mining & Analysis (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- General Business, Economics & Management (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Finance (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Development Economics (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Accounting & Taxation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- General Factory Administration (AREA)
Abstract
The application relates to a safety stock automatic management method and a device of a workshop spare part library, comprising the steps of acquiring a component part list of equipment to be predicted in a workshop based on an equipment tree; obtaining a spare part safety inventory list of the equipment to be predicted according to the established expert database, the received alarm data information, the health index and the component part list of the equipment to be predicted; and obtaining a spare part demand list of the spare part safety inventory according to the failure rate of the equipment, the component parts and the spare part inventory, the maintenance information of the equipment to be predicted and the spare part safety inventory. The application establishes a spare part list according to the field device list; automatically generating a safety inventory list and a suggestion of the number of spare parts and a safety inventory early warning prompt according to a system expert library, a mechanism model and equipment risk diagnosis and statistics; the system associates the expert library of equipment maintenance and preventive overhaul activities, automatically generates a spare part demand list and associates a spare part library management system, thereby realizing the dynamic association and management of spare part inventory management.
Description
Technical Field
The application belongs to the technical field of data processing, and particularly relates to a method and a device for automatically managing safety stock of a workshop spare part library.
Background
The industrial robot is a multi-joint manipulator or a multi-degree-of-freedom machine device facing the industrial field, can automatically execute work, and is a machine which realizes various functions by self power and control capability.
In the modern production industry, a professional technician can connect robots, automatic production equipment, transmission lines, control systems and the like in series together through overall planning design according to the requirements of a production process to build an automatic production line based on robot application. The automatic production line can run according to a pre-arranged program, so that automatic production is realized, and unmanned production is realized.
The automatic production line based on robot application can replace people to do some monotonous, frequent and repeated long-time operations in industrial production or operations in dangerous and severe environments, such as production process links of stamping, welding, coating, machining, casting, heat treatment, automatic assembly and the like, and replace people to realize automatic production.
As the degree of automation of automated production lines based on robotic applications increases, the number of robots and automated equipment increases, the "center of gravity" of production management changes from "plumbers" to "plumbing".
How to shorten the maintenance recovery time of abnormal fault shutdown, reduce the indirect production loss caused by long-time abnormal shutdown, how to reasonably and sufficiently plan spare part reserves, how to reduce spare part cost, avoid cost waste and the like becomes the most headache problem of the production and equipment management departments of manufacturing enterprises. Scientific, reasonable and accurate spare part management becomes the most important and difficult technology and management work in equipment management.
At present, due to the fact that no mature and perfect technology exists, a method for accurately measuring, calculating and managing daily demand of equipment spare parts of an automatic production line in a workshop is achieved, and the quick recovery of abnormal shutdown of the production line cannot be guaranteed, so that great direct and indirect production economic loss and resource waste are caused.
How to accurately predict and calculate spare part requirements of workshop automation equipment is a technical problem to be solved at present.
Disclosure of Invention
The application aims to solve the technical problem of providing a safety stock automatic management method and device for a workshop spare part library aiming at the defects of the prior art.
The technical scheme for solving the technical problems is as follows:
in a first aspect, the present application provides a method for automatically managing safety stock of a workshop spare part library, the method comprising:
acquiring a component part list of equipment to be predicted in a workshop based on an equipment tree;
Obtaining a spare part safety inventory list of the equipment to be predicted according to the established expert database, the received alarm data information and health index of the equipment to be predicted and the component part list;
And obtaining a spare part demand list of the spare part safety inventory according to the failure rate and the health index of the spare part, the component part inventory, the maintenance information of the equipment to be predicted and the spare part safety inventory, wherein the spare part demand list comprises the number of spare parts of the spare part safety inventory.
Further, the device tree-based obtaining a component part list of the device to be predicted in the workshop specifically includes:
And counting all leaf nodes in the equipment tree according to the equipment tree of the equipment to be predicted, and obtaining a component part list of the equipment to be predicted.
Further, the obtaining a spare part safety inventory of the equipment to be predicted according to the established expert database, the received alarm data information, the health index and the component part inventory of the equipment to be predicted specifically includes:
acquiring the life cycle of the equipment to be predicted and the life cycle of the components of the equipment to be predicted based on the established expert database;
Determining a spare part period of a component of the equipment to be predicted according to the life cycle of the equipment to be predicted and the life cycle of the component of the equipment to be predicted, and determining whether the spare part of the component is a safety stock according to the spare part period, alarm data information of the equipment to be predicted, a health index, an inventory model and the component part list;
if the spare parts of the parts are safety stock, the spare part information of the parts is stored in a first spare part safety stock list, otherwise, the spare part information of the parts is stored in a second spare part safety stock list.
Further, the determining the spare part period of the component of the equipment to be predicted according to the life cycle of the equipment to be predicted and the life cycle of the component of the equipment to be predicted specifically includes:
Obtaining a key period of the equipment to be predicted based on the life period of the part of the equipment to be predicted and the key coefficient of the part of the equipment to be predicted;
and dividing the life cycle of the equipment to be predicted by utilizing the key cycle of the equipment to be predicted, and obtaining the spare part cycle of the part of the equipment to be predicted.
Further, the determining whether the spare parts of the components are safety stock according to the spare parts period, the alarm data information of the equipment to be predicted, the health index, the stock model and the component part list specifically includes:
taking the life cycle of the equipment to be predicted and the life cycle of the components of the equipment to be predicted as a first parameter set;
Taking the alarm data information and the health index of the equipment to be predicted as a second parameter set;
Inputting the first parameter set and the second parameter set into an established inventory model to obtain whether spare parts of the component are safety inventory, wherein the inventory model is established based on a convolutional neural network.
Further, the method for obtaining the spare part requirement list of the spare part safety inventory according to the failure rate of the spare part, the component part list, the maintenance information of the equipment to be predicted and the spare part safety inventory, wherein the spare part requirement list comprises the number of spare parts of the spare part safety inventory, and specifically comprises the following steps:
calculating variance values of actual failure rates and preset failure rates of all the spare parts, if the variance values are larger than the preset variance values, setting the spare part rates of all the spare parts to be first preset values, otherwise, setting the spare part rates of all the spare parts to be preset values of the spare parts;
determining a key time point of the spare part and a spare part maintenance list according to the maintenance information of the equipment to be predicted;
and obtaining the spare part demand list according to the spare part rate of the spare parts, the component part list, the spare part time point of the equipment to be predicted and the spare part maintenance list.
In a second aspect, the present application provides an automatic safety stock management device for a shop spare part library, the device comprising:
The first processing module is used for acquiring a component part list of equipment to be predicted in the workshop based on the equipment tree;
the second processing module is used for obtaining a spare part safety inventory of the equipment to be predicted according to the established expert database, the received alarm data information and health index of the equipment to be predicted and the component part inventory;
And the third processing module is used for obtaining a spare part demand list of the spare part safety inventory according to the failure rate and the health index of the spare part, the component part list, the maintenance information of the equipment to be predicted and the spare part safety inventory, wherein the spare part demand list comprises the number of spare parts of the spare part safety inventory.
The beneficial effects of the application are as follows: the method comprises the steps of obtaining a component part list of equipment to be predicted in a workshop based on an equipment tree; obtaining a spare part safety inventory list of the equipment to be predicted according to the established expert database, the received alarm data information, the health index and the component part list of the equipment to be predicted; and obtaining a spare part demand list of the spare part safety inventory according to the failure rate, the health index, the component part inventory, the maintenance information of the equipment to be predicted and the spare part safety inventory, wherein the spare part demand list comprises the number of spare parts of the spare part safety inventory. The application establishes a spare part list according to the field device list; automatically generating a safety inventory list and a suggestion of the number of spare parts and a safety inventory early warning prompt according to a system expert library, a mechanism model and equipment risk diagnosis and statistics; the system associates the expert library of equipment maintenance and preventive overhaul activities, automatically generates a spare part demand list and associates a spare part library management system, thereby realizing the dynamic association and management of spare part inventory management. By the automatic safety stock management method and algorithm of the workshop spare part library, spare part demands of workshop automatic production line equipment can be effectively and accurately predicted, spare part purchasing and storing plans are reasonably arranged by production and equipment management departments of manufacturing enterprises, and intelligent decision making and data support are provided. And can achieve the following technical effects of innovation:
1. Accurately establishing equipment spare part standing accounts of the system; 2. accurately positioning and evaluating equipment fault points and risk points, and storing corresponding spare parts; 3. the small granularity, the comprehensiveness and the systemization of the demand prediction of equipment spare parts and components are realized; 4. and the safety line of equipment inventory is early warned and prompted in advance. 5. And updating and prompting the data in real time according to the equipment maintenance and repair plan.
Additional aspects of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly explain the embodiments of the present application or the drawings used in the description of the prior art, and it is obvious that the drawings described below are only some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for automatically managing safety stock of a workshop spare part library according to an embodiment of the application;
fig. 2 is a schematic block diagram of an automatic safety stock management device for a workshop spare part library according to another embodiment of the present application.
Detailed Description
In order to make the present application better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Currently, enterprises need to complete generating spare part lists of equipment in the fastest time, which enables correct and timely predictions and reliable decisions generated by the predictions to be made, and the critical elements of success of modern enterprises are called. The accurate prediction of the demand of production spare parts is a key for reliable production, the reliability of production equipment can be improved, a purchasing plan can be more scientifically formulated, and the inventory cost can be reduced as much as possible.
However, the current prediction system cannot meet the increasingly higher prediction requirements, in this case, the occurrence of data mining (DATAMINING, DM) brings about a great deal of attention to academia and industry, and the current data mining model is applied to the fields of sales inventory, supply chain inventory and the like, but the spare parts have the management requirements of the whole life cycle of products, and the spare part prediction model has great difference from the business fields of sales, supply chain and the like, and the data mining model aiming at sales inventory, supply chain inventory and the like is not suitable for the aspects of spare part prediction, and the spare part prediction cannot be realized.
As shown in fig. 1, the method for automatically managing safety stock of a spare part library in a workshop according to the embodiment of the application includes:
110. acquiring a component part list of equipment to be predicted in a workshop based on an equipment tree;
120. Obtaining a spare part safety inventory list of the equipment to be predicted according to the established expert database, the received alarm data information, the health index and the component part list of the equipment to be predicted;
130. And obtaining a spare part demand list of the spare part safety inventory according to the failure rate and the health index of the spare part, the component part inventory, the maintenance information of the equipment to be predicted and the spare part safety inventory, wherein the spare part demand list comprises the number of spare parts of the spare part safety inventory.
Based on the above embodiment, further, the obtaining, based on the device tree, a component part list of the device to be predicted in the workshop specifically includes:
And counting all leaf nodes in the equipment tree according to the equipment tree of the equipment to be predicted, and obtaining a component part list of the equipment to be predicted.
Based on the above embodiment, the obtaining a spare part safety inventory of the equipment to be predicted according to the established expert database, the received alarm data information, the health index and the component part inventory of the equipment to be predicted specifically includes:
acquiring the life cycle of the equipment to be predicted and the life cycle of the components of the equipment to be predicted based on the established expert database;
determining a spare part life cycle of a part of the equipment to be predicted according to the life cycle analysis of the equipment to be predicted and the life cycle analysis of the part of the equipment to be predicted, and determining whether the spare part of the part is a safety stock according to the spare part life cycle, alarm data information and health index of the equipment to be predicted, an inventory model and the component part list;
if the spare parts of the parts are safety stock, the spare part information of the parts is stored in a first spare part safety stock list, otherwise, the spare part information of the parts is stored in a second spare part safety stock list.
Based on the above embodiment, the determining the spare part period of the component of the device to be predicted according to the life cycle of the device to be predicted and the life cycle of the component of the device to be predicted specifically includes:
Obtaining a key period of the equipment to be predicted based on the life period of the part of the equipment to be predicted and the key coefficient of the part of the equipment to be predicted;
and dividing the life cycle of the equipment to be predicted by utilizing the key cycle of the equipment to be predicted, and obtaining the spare part cycle of the part of the equipment to be predicted.
Based on the above embodiment, the determining whether the spare part of the component is a safety inventory according to the spare part period, the alarm data information and the health index of the equipment to be predicted, the inventory model and the component part list specifically includes:
taking the life cycle of the equipment to be predicted and the life cycle of the components of the equipment to be predicted as a first parameter set;
taking the alarm data information and the health index of the equipment to be predicted as a second parameter set;
Inputting the first parameter set and the second parameter set into an established inventory model to obtain whether spare parts of the component are safety inventory, wherein the inventory model is established based on a convolutional neural network.
Based on the above embodiment, the obtaining the spare part requirement list of the spare part safety inventory according to the failure rate of the spare part, the component part list, the maintenance information of the equipment to be predicted, and the spare part safety inventory, where the spare part requirement list includes the number of spare parts of the spare part safety inventory, specifically includes:
calculating variance values of actual failure rates and preset failure rates of all the spare parts, if the variance values are larger than the preset variance values, setting the spare part rates of all the spare parts to be first preset values, otherwise, setting the spare part rates of all the spare parts to be preset values of the spare parts;
determining a key time point of the spare part and a spare part maintenance list according to the maintenance information of the equipment to be predicted;
and obtaining the spare part demand list according to the spare part rate of the spare parts, the component part list, the spare part time point of the equipment to be predicted and the spare part maintenance list.
The automatic safety stock management method based on the workshop spare part library provided by the embodiment comprises the steps of obtaining a component part list of equipment to be predicted in a workshop based on an equipment tree; obtaining a spare part safety inventory list of the equipment to be predicted according to the established expert database, the received alarm data information, the health index and the component part list of the equipment to be predicted; and obtaining a spare part demand list of the spare part safety inventory according to the failure rate, the health index, the component part inventory, the maintenance information of the equipment to be predicted and the spare part safety inventory, wherein the spare part demand list comprises the number of spare parts of the spare part safety inventory. The application establishes a spare part list according to the field device list; automatically generating a safety inventory list and a suggestion of the number of spare parts and a safety inventory early warning prompt according to a system expert library, a mechanism model and equipment risk diagnosis and statistics; the system associates the expert library of equipment maintenance and preventive overhaul activities, automatically generates a spare part demand list and associates a spare part library management system, thereby realizing the dynamic association and management of spare part inventory management. By the automatic safety stock management method and algorithm of the workshop spare part library, spare part demands of workshop automatic production line equipment can be effectively and accurately predicted, spare part purchasing and storing plans are reasonably arranged by production and equipment management departments of manufacturing enterprises, and intelligent decision making and data support are provided. And can achieve the following technical effects of innovation: 1. accurately establishing equipment spare part standing accounts of the system; 2. accurately positioning and evaluating equipment fault points and risk points, and storing corresponding spare parts; 3. the small granularity, the comprehensiveness and the systemization of the demand prediction of equipment spare parts and components are realized; 4. and the safety line of equipment inventory is early warned and prompted in advance. 5. And updating and prompting the data in real time according to the equipment maintenance and repair plan.
As shown in fig. 2, the present application provides an automatic safety stock management device for a shop spare part library, the device comprising:
The first processing module is used for acquiring a component part list of equipment to be predicted in the workshop based on the equipment tree;
the second processing module is used for obtaining a spare part safety inventory of the equipment to be predicted according to the established expert database, the received alarm data information and health index of the equipment to be predicted and the component part inventory;
And the third processing module is used for obtaining a spare part demand list of the spare part safety inventory according to the failure rate and the health index of the spare part, the component part list, the maintenance information of the equipment to be predicted and the spare part safety inventory, wherein the spare part demand list comprises the number of spare parts of the spare part safety inventory.
Further, the first processing module is specifically configured to count all leaf nodes in the equipment tree according to the equipment tree of the equipment to be predicted, so as to obtain a component part list of the equipment to be predicted.
Further, the second processing module is specifically configured to obtain a life cycle of the device to be predicted and a life cycle of a component of the device to be predicted based on the established expert database;
determining a spare part life cycle of a part of the equipment to be predicted according to the life cycle analysis of the equipment to be predicted and the life cycle analysis of the part of the equipment to be predicted, and determining whether the spare part of the part is a safety stock according to the spare part life cycle, alarm data information and health index of the equipment to be predicted, an inventory model and the component part list;
if the spare parts of the parts are safety stock, the spare part information of the parts is stored in a first spare part safety stock list, otherwise, the spare part information of the parts is stored in a second spare part safety stock list.
Further, the third processing module is specifically configured to obtain a critical period of the device to be predicted based on a life cycle of the component of the device to be predicted and a critical coefficient of the component of the device to be predicted;
and dividing the life cycle of the equipment to be predicted by utilizing the key cycle of the equipment to be predicted, and obtaining the spare part cycle of the part of the equipment to be predicted.
The automatic safety stock management device for the workshop spare part library provided on the basis of the embodiment comprises the steps of acquiring a component part list of equipment to be predicted in a workshop on the basis of an equipment tree; obtaining a spare part safety inventory list of the equipment to be predicted according to the established expert database, the received alarm data information, the health index and the component part list of the equipment to be predicted; and obtaining a spare part demand list of the spare part safety inventory according to the fault rate of the spare part, the health index, the component part inventory, the maintenance information of the equipment to be predicted and the spare part safety inventory, wherein the spare part demand list comprises the number of spare parts of the spare part safety inventory. The application establishes a spare part list according to the field device list; automatically generating a safety inventory list and a suggestion of the number of spare parts and a safety inventory early warning prompt according to a system expert library, a mechanism model and equipment risk diagnosis and statistics; the system associates the expert library of equipment maintenance and preventive overhaul activities, automatically generates a spare part demand list and associates a spare part library management system, thereby realizing the dynamic association and management of spare part inventory management. By the automatic safety stock management method and algorithm of the workshop spare part library, spare part demands of workshop automatic production line equipment can be effectively and accurately predicted, spare part purchasing and storing plans are reasonably arranged by production and equipment management departments of manufacturing enterprises, and intelligent decision making and data support are provided. And can achieve the following technical effects of innovation: 1. accurately establishing equipment spare part standing accounts of the system; 2. accurately positioning and evaluating equipment fault points and risk points, and storing corresponding spare parts; 3. the small granularity, the comprehensiveness and the systemization of the demand prediction of equipment spare parts and components are realized; 4. and the safety line of equipment inventory is early warned and prompted in advance. 5. And updating and prompting the data in real time according to the equipment maintenance and repair plan.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on 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 the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium.
Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
The present application is not limited to the above embodiments, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the present application, and these modifications and substitutions are intended to be included in the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (1)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310408662.1A CN116579724B (en) | 2023-04-17 | 2023-04-17 | Automatic safety stock management method and device for workshop spare part library |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310408662.1A CN116579724B (en) | 2023-04-17 | 2023-04-17 | Automatic safety stock management method and device for workshop spare part library |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116579724A CN116579724A (en) | 2023-08-11 |
| CN116579724B true CN116579724B (en) | 2024-11-19 |
Family
ID=87538605
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310408662.1A Active CN116579724B (en) | 2023-04-17 | 2023-04-17 | Automatic safety stock management method and device for workshop spare part library |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116579724B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112580812A (en) * | 2019-09-27 | 2021-03-30 | 北京国双科技有限公司 | Model training method, inventory safety early warning method, device, equipment and medium |
| CN115081997A (en) * | 2022-08-18 | 2022-09-20 | 庞械(天津)科技有限公司 | Equipment spare part inventory diagnostic system |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7881985B2 (en) * | 2000-02-22 | 2011-02-01 | I2 Technologies Us, Inc. | Electronic marketplace providing service parts inventory planning and management |
| US7266518B2 (en) * | 2005-12-29 | 2007-09-04 | Kimberly-Clark Worldwide, Inc. | Spare parts inventory management |
| JP5292792B2 (en) * | 2007-12-12 | 2013-09-18 | 株式会社リコー | Inventory management system, inventory management method, and inventory management program |
| CN115293377A (en) * | 2022-08-10 | 2022-11-04 | 重庆赛迪热工环保工程技术有限公司 | Method for managing service life of blades of mixing machine |
| CN115345564A (en) * | 2022-09-22 | 2022-11-15 | 启明信息技术股份有限公司 | Inventory management method based on automatic prediction |
| CN115879664A (en) * | 2022-12-02 | 2023-03-31 | 中船重工信息科技有限公司 | Intelligent operation and maintenance system and method based on industrial Internet |
-
2023
- 2023-04-17 CN CN202310408662.1A patent/CN116579724B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112580812A (en) * | 2019-09-27 | 2021-03-30 | 北京国双科技有限公司 | Model training method, inventory safety early warning method, device, equipment and medium |
| CN115081997A (en) * | 2022-08-18 | 2022-09-20 | 庞械(天津)科技有限公司 | Equipment spare part inventory diagnostic system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116579724A (en) | 2023-08-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ito et al. | Internet of things and simulation approach for decision support system in lean manufacturing | |
| CN112667739B (en) | Unified information model, digital twin and big data optimization method and production management system | |
| Wang et al. | Research on assembly quality adaptive control system for complex mechanical products assembly process under uncertainty | |
| Groba et al. | Architecture of a predictive maintenance framework | |
| US11556895B2 (en) | System and computer program for providing high delivery performance in a value chain | |
| CN116976773A (en) | Digital factory logistics scheduling system and method | |
| KR102183328B1 (en) | System for assessing smart factory and suggesting improvement method based on layout in cloud | |
| CN117196200A (en) | Industrial factory asset management system | |
| CN112418540A (en) | Intelligent MES real-time data analysis system | |
| CN114493299A (en) | An industrial Internet-based agricultural machinery management and control method, equipment, and medium | |
| Yıldız et al. | Integrating preventive and predictive maintenance policies with system dynamics: A decision table approach | |
| CN116579724B (en) | Automatic safety stock management method and device for workshop spare part library | |
| CN114418418B (en) | Work order information circulation method, system, equipment and medium based on process conversion ratio | |
| CN106557839B (en) | A method and system for optimizing equipment maintenance strategy based on big data | |
| Khan | Problem-specific heuristics for diagnosability and inventory analysis in a reconfigurable manufacturing system | |
| CN113537681B (en) | Method and system for refining enterprise equipment management informatization | |
| CN118409548A (en) | A manufacturing cloud platform based on MES system | |
| CN114077693A (en) | Factory industrial equipment data visualization management system and method based on Internet of things | |
| CN111178838A (en) | An intelligent digital factory system based on cloud computing | |
| CN115793581B (en) | Cigarette equipment management method | |
| US11675347B2 (en) | Industrial machine monitoring device | |
| Huo et al. | CMMS based reliability centered maintenance | |
| CN115032956A (en) | New industry MES system control method based on industrial internet identification analysis | |
| CN114139102A (en) | Numerical control machining quality risk early warning method | |
| CN113706093A (en) | Integrated management and control information model for metal processing production and operation process |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |