CN116757562A - Intelligent manufacturing scheduling method with self-learning capability - Google Patents

Intelligent manufacturing scheduling method with self-learning capability Download PDF

Info

Publication number
CN116757562A
CN116757562A CN202311058332.0A CN202311058332A CN116757562A CN 116757562 A CN116757562 A CN 116757562A CN 202311058332 A CN202311058332 A CN 202311058332A CN 116757562 A CN116757562 A CN 116757562A
Authority
CN
China
Prior art keywords
manufacturing
current monitoring
monitoring period
assembly line
workers
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.)
Granted
Application number
CN202311058332.0A
Other languages
Chinese (zh)
Other versions
CN116757562B (en
Inventor
衡思泽
戴亦迪
李强
顾同海
寿涛
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guolian Johnson Self Controlled Green Technology Wuxi Co ltd
Original Assignee
Guolian Johnson Self Controlled Green Technology Wuxi Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Guolian Johnson Self Controlled Green Technology Wuxi Co ltd filed Critical Guolian Johnson Self Controlled Green Technology Wuxi Co ltd
Priority to CN202311058332.0A priority Critical patent/CN116757562B/en
Publication of CN116757562A publication Critical patent/CN116757562A/en
Application granted granted Critical
Publication of CN116757562B publication Critical patent/CN116757562B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • 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
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Educational Administration (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Software Systems (AREA)
  • Development Economics (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • General Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Manufacturing & Machinery (AREA)
  • Health & Medical Sciences (AREA)
  • Game Theory and Decision Science (AREA)
  • Primary Health Care (AREA)
  • Multimedia (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Quality & Reliability (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Operations Research (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to the technical field of intelligent manufacturing scheduling methods, in particular to an intelligent manufacturing scheduling method with self-learning capability. The method has the advantages that the manufacturing videos of the manufacturing assembly lines in the factory corresponding to the current monitoring period are obtained, the number of workers and the working time of the workers of the manufacturing assembly lines corresponding to the current monitoring period are analyzed, meanwhile, the work efficiency value and the fatigue value of the workers of the manufacturing assembly lines corresponding to the current monitoring period are analyzed, the real-time state of the workers of the manufacturing assembly lines can be intuitively known, meanwhile, reliable data support is provided for the analysis of the effective evaluation value of the subsequent manufacturing assembly lines corresponding to the current monitoring period, the reliability and the accuracy of the analysis result of the effective evaluation value of the manufacturing assembly lines corresponding to the current monitoring period are improved, and the execution of the subsequent intelligent manufacturing schedule is further facilitated.

Description

Intelligent manufacturing scheduling method with self-learning capability
Technical Field
The invention relates to the technical field of intelligent manufacturing scheduling methods, in particular to an intelligent manufacturing scheduling method with self-learning capability.
Background
With the rapid development of artificial intelligence technology and data analysis technology, more and more fields gradually refer to artificial intelligence technology, and the artificial intelligence technology not only can realize automation and intellectualization of tasks, but also can reduce manual operation and intervention. Through the self-learning capability of the machine, intelligent manufacturing scheduling can be realized for the work of the factory assembly line, and the work efficiency and the production efficiency are improved.
In the process of personnel scheduling in the current factory, the situation that part of personnel are idle or part of post staff is insufficient is usually caused by improper scheduling. Meanwhile, the adjustment cannot be performed in time according to the actual condition of the factory, and the working efficiency of the factory is low.
Disclosure of Invention
In order to overcome the defects in the background art, the embodiment of the invention provides an intelligent manufacturing scheduling method with self-learning capability, which can effectively solve the problems related to the background art.
The aim of the invention can be achieved by the following technical scheme: an intelligent manufacturing scheduling method with self-learning capability comprises the following steps:
step one, acquiring production lines corresponding to a factory to obtain all production lines corresponding to the factory, and acquiring manufacturing videos of all production lines corresponding to the factory in a current monitoring period through an intelligent camera to obtain manufacturing videos of all production lines corresponding to the current monitoring period.
Analyzing basic parameters of workers corresponding to the current monitoring period of each manufacturing assembly line based on the manufacturing video corresponding to the current monitoring period of each manufacturing assembly line to obtain basic parameters of the workers corresponding to the current monitoring period of each manufacturing assembly line, and analyzing working parameters of the workers in the current monitoring period of each manufacturing assembly line to obtain working parameters of the workers corresponding to the current monitoring period of each manufacturing assembly line;
preferably, the basic parameters of workers corresponding to the current monitoring period of each manufacturing assembly line are analyzed, and the specific analysis mode is as follows:
analyzing the manufacturing video of each manufacturing assembly line corresponding to the current monitoring period to obtain manufacturing images of each manufacturing assembly line corresponding to each monitoring time point in the current monitoring period;
extracting face images of workers of each manufacturing assembly line corresponding to each monitoring time point in the current monitoring time period from the manufacturing images of each monitoring time point of each manufacturing assembly line corresponding to the current monitoring time period, comparing the face images of the workers of each monitoring time point with each other, and removing face images of the same workers from the face images to obtain face images of each worker of each manufacturing assembly line corresponding to the current monitoring time period, so that the number of workers of each manufacturing assembly line corresponding to the current monitoring time period is counted to be used as the number of workers of each manufacturing assembly line corresponding to the current monitoring time period;
acquiring face images of all working workers in the current monitoring period corresponding to all manufacturing pipelines, matching the face images of all working workers in the current monitoring period corresponding to all manufacturing pipelines with the face images of workers in the corresponding monitoring time points in the manufacturing pipelines, if the face images of a certain working worker are successfully matched with the face images of the workers in the certain monitoring time points, recording the monitoring time points as working time points, thus obtaining all working time points of all working workers in the current monitoring period corresponding to all manufacturing pipelines, integrating adjacent working time points, obtaining all working time periods of all working workers in the current monitoring period corresponding to all manufacturing pipelines, and counting the total duration of all working time periods of all working workers in the current monitoring period corresponding to all manufacturing pipelines as the working time duration of all working workers in the current monitoring period corresponding to all manufacturing pipelines;
and forming basic parameters of workers of each manufacturing assembly line corresponding to the current monitoring period by the number of workers of each manufacturing assembly line corresponding to the current monitoring period and the working time of each worker.
Preferably, the worker working parameters of each manufacturing assembly line corresponding to the current monitoring period are analyzed, and the specific analysis mode is as follows:
extracting the number of finished products of each worker in the current monitoring period corresponding to each manufacturing assembly line from the manufacturing video of each manufacturing assembly line corresponding to the current monitoring period to obtain the number of finished products of each worker in the current monitoring period corresponding to each manufacturing assembly line;
extracting the working time length of each worker in each manufacturing assembly line corresponding to the current monitoring period from the basic parameters of the worker in each manufacturing assembly line corresponding to the current monitoring period, and taking the value as GT i j ；
The work efficiency value of each worker in the current monitoring period corresponding to each manufacturing assembly line is obtained through analysis and is recorded as XL i j I is denoted as the number of each manufacturing line, i=1, 2,..n, j is denoted as the number of each worker, j=1, 2,..m;
extracting working time length tables of all workers corresponding to the monitoring periods from a database, and extracting working time length tables of all workers corresponding to the monitoring periods of all manufacturing pipelines from the working time length tables of all workers corresponding to the monitoring periods;
based on the current monitoring period, acquiring the continuous working time length of each worker corresponding to each manufacturing assembly line from the working time length table of each worker corresponding to the monitoring period of each manufacturing assembly line, obtaining the continuous working time length of each worker corresponding to each manufacturing assembly line, analyzing to obtain the fatigue value of each worker in the current monitoring period corresponding to each manufacturing assembly line, and recording as PL i j ；
And working parameters of workers of each manufacturing assembly line corresponding to the current monitoring period are formed by the work efficiency value and the fatigue value of each worker in the current monitoring period of each manufacturing assembly line.
Step three, obtaining the manufacturing output of each manufacturing assembly line corresponding to the current monitoring period, and analyzing the effective evaluation value of each manufacturing assembly line corresponding to the current monitoring period based on the manufacturing output of each manufacturing assembly line corresponding to the current monitoring period, the basic parameters of workers and the working parameters of workers, so as to obtain the effective evaluation value of each manufacturing assembly line corresponding to the current monitoring period;
preferably, the method includes analyzing the effective evaluation value of each manufacturing assembly line corresponding to the current monitoring period based on the manufacturing yield, the basic parameters of workers and the working parameters of workers of each manufacturing assembly line corresponding to the current monitoring period to obtain the effective evaluation value of each manufacturing assembly line corresponding to the current monitoring period, wherein the specific analysis mode is as follows:
extracting the value of the manufacturing yield of each manufacturing pipeline corresponding to the current monitoring period, and recording the value as ZN i ；
Extracting the numerical value of the number of workers of each manufacturing assembly line corresponding to the current monitoring period from the basic parameters of the workers of each manufacturing assembly line corresponding to the current monitoring period, and recording the numerical value as GN i The method comprises the steps of carrying out a first treatment on the surface of the Simultaneously extracting the working time length GT of each worker in the current monitoring period corresponding to each manufacturing assembly line i j ；
Extracting work efficiency value XL of each worker in each manufacturing assembly line corresponding to the current monitoring period from worker work parameters of each manufacturing assembly line corresponding to the current monitoring period i j And fatigue value PL of each worker i j ；
Calculating an effective evaluation value PZ of each manufacturing assembly line corresponding to the current monitoring period through a formula i The calculation formula is as follows:
，ZN 0 reference production yield, GN, expressed as the corresponding monitoring period stored in the database i 0 Expressed as a reference worker number, GT, corresponding to the ith manufacturing line stored in the database 0 The reference working time periods of the set worker corresponding to the monitoring periods are indicated, and a1, a2, a3, a4 and a5 are respectively indicated as set weight factors.
Analyzing the efficiency level of each manufacturing assembly line corresponding to the current monitoring period based on the efficiency evaluation value of each manufacturing assembly line corresponding to the current monitoring period to obtain the efficiency level of each manufacturing assembly line corresponding to the current monitoring period: and matching the effective evaluation value of each manufacturing assembly line corresponding to the current monitoring period with the effective evaluation value interval of each set efficiency level to obtain the efficiency level of each manufacturing assembly line corresponding to the current monitoring period, wherein the efficiency level is specifically one, two, three and the like.
If the efficiency level of a certain manufacturing assembly line corresponding to the current monitoring period is three or equal, personnel movement parameters corresponding to the manufacturing assembly line are analyzed, and if the efficiency level of the certain manufacturing assembly line corresponding to the current monitoring period is two or equal, personnel display parameters corresponding to the manufacturing assembly line are analyzed, so that execution parameters corresponding to the manufacturing assembly lines are formed;
preferably, if the efficiency level of a certain manufacturing line corresponding to the current monitoring period is equal to or three, the personnel movement parameters corresponding to the manufacturing line are analyzed in the following specific analysis modes:
if the efficiency level of a certain manufacturing assembly line corresponding to the current monitoring period is three, the efficiency evaluation value of the manufacturing assembly line corresponding to the current monitoring period is differenced with the set reference efficiency evaluation value, so that the efficiency evaluation value of the manufacturing assembly line corresponding to the current monitoring period is obtained and recorded as a value to be adjusted of the manufacturing assembly line corresponding to the current monitoring period;
if the efficiency grade of a certain manufacturing assembly line corresponding to the current monitoring period is equal, performing difference between the efficiency evaluation value of the manufacturing assembly line corresponding to the current monitoring period and the set reference efficiency evaluation value to obtain an efficiency evaluation value of the manufacturing assembly line corresponding to the current monitoring period, and recording the efficiency evaluation value as a borrowing and regulating value of the manufacturing assembly line corresponding to the current monitoring period;
screening out manufacturing pipelines with the first efficiency grade from the efficiency grades of the manufacturing pipelines corresponding to the current monitoring period, marking the manufacturing pipelines as first grade pipelines, and obtaining borrowing and regulating values of the first grade pipelines corresponding to the current monitoring period;
screening out manufacturing pipelines with the efficiency level of third class from the efficiency levels of the manufacturing pipelines corresponding to the current monitoring period, marking the manufacturing pipelines as the third class pipelines, and obtaining the value required to be adjusted of each third class pipeline corresponding to the current monitoring period;
matching the borrowing value of each first-class pipeline corresponding to the current monitoring period with the number of the mobilizable personnel corresponding to the set borrowing value to obtain the number of the mobilizable personnel of each first-class pipeline corresponding to the current monitoring period;
matching the value to be adjusted of each three-equal-pipeline corresponding to the current monitoring period with the number of people to be moved corresponding to the set value to be adjusted, and obtaining the number of people to be moved of each three-equal-pipeline corresponding to the current monitoring period.
Preferably, if the efficiency level of a certain manufacturing assembly line corresponding to the current monitoring period is equal to two, the personnel display parameters corresponding to the manufacturing assembly line are analyzed in the specific analysis mode that:
and screening out the manufacturing pipelines with the efficiency level of two levels from the efficiency levels of the manufacturing pipelines corresponding to the current monitoring period, recording the manufacturing pipelines as two-level pipelines, acquiring the number of workers of the two-level pipelines corresponding to the current monitoring period, and taking the number of workers as personnel display parameters.
And fifthly, executing corresponding processing based on the execution parameters corresponding to each manufacturing pipeline.
The invention has the beneficial effects that:
according to the invention, the manufacturing video of each manufacturing assembly line corresponding to the current monitoring period in the factory is obtained, the number of workers and the working time length of each worker in each manufacturing assembly line corresponding to the current monitoring period are analyzed, and meanwhile, the work efficiency value and the fatigue value of each worker in each manufacturing assembly line corresponding to the current monitoring period are analyzed, so that the real-time state of each worker in each manufacturing assembly line can be intuitively known, meanwhile, reliable data support is provided for the analysis of the effective evaluation value of each subsequent manufacturing assembly line corresponding to the current monitoring period, the reliability and the accuracy of the effective evaluation value analysis result of each manufacturing assembly line corresponding to the current monitoring period are improved, and the execution of the subsequent intelligent manufacturing schedule is further facilitated.
According to the invention, the efficiency of manufacturing in the manufacturing assembly line can be found in time by analyzing the efficiency evaluation value of each manufacturing assembly line corresponding to the current monitoring period based on the manufacturing yield, the basic parameters of workers and the working parameters of workers of each manufacturing assembly line corresponding to the current monitoring period, a data basis is provided for the subsequent optimized production line, the production efficiency and the production capacity are improved to a great extent, and the cost and the risk are reduced.
By analyzing the execution parameters corresponding to each manufacturing assembly line, the intelligent scheduling of the manufacturing assembly line is realized, the rationality and the utilization rate of resource allocation are improved, the production efficiency of the manufacturing assembly line is improved, the idle and waste of resources are greatly reduced, and the economic benefit of factories is improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention is an intelligent manufacturing scheduling method with self-learning capability, comprising the following steps:
step one, acquiring production lines corresponding to a factory to obtain all production lines corresponding to the factory, and acquiring manufacturing videos of all production lines corresponding to the factory in a current monitoring period through an intelligent camera to obtain manufacturing videos of all production lines corresponding to the current monitoring period.
Analyzing the basic parameters of the workers corresponding to the current monitoring period of each manufacturing assembly line based on the manufacturing video corresponding to the current monitoring period of each manufacturing assembly line to obtain the basic parameters of the workers corresponding to the current monitoring period of each manufacturing assembly line, wherein the specific analysis steps are as follows:
analyzing the manufacturing video of each manufacturing assembly line corresponding to the current monitoring period to obtain manufacturing images of each manufacturing assembly line corresponding to each monitoring time point in the current monitoring period.
Extracting face images of workers of each manufacturing assembly line corresponding to each monitoring time point in the current monitoring time period from the manufacturing images of each monitoring time point of each manufacturing assembly line corresponding to the current monitoring time period, comparing the face images of the workers of each monitoring time point with each other, removing face images of the same workers from the face images, and obtaining face images of each worker of each manufacturing assembly line corresponding to the current monitoring time period, so that the number of workers of each manufacturing assembly line corresponding to the current monitoring time period is counted, and the face images of each worker of each manufacturing assembly line corresponding to the current monitoring time period are used as the number of workers of each manufacturing assembly line corresponding to the current monitoring time period.
The face images of the working workers in the current monitoring period corresponding to the manufacturing assembly lines are obtained, the face images of the working workers in the current monitoring period corresponding to the manufacturing assembly lines are matched with the face images of the workers in the monitoring time points corresponding to the manufacturing assembly lines, if the face images of the working workers are successfully matched with the face images of the workers in the monitoring time points, the monitoring time points are recorded as working time points, the working time points of the working workers in the current monitoring period corresponding to the manufacturing assembly lines are obtained, adjacent working time points are integrated, the working time periods of the working workers in the current monitoring period corresponding to the manufacturing assembly lines are obtained, the total duration of the working time periods of the working workers in the current monitoring period corresponding to the manufacturing assembly lines is counted, and the total duration of the working time periods of the working workers in the current monitoring period corresponding to the manufacturing assembly lines is used as the working duration of the working workers in the current monitoring period corresponding to the working assembly lines.
And forming basic parameters of workers of each manufacturing assembly line corresponding to the current monitoring period by the number of workers of each manufacturing assembly line corresponding to the current monitoring period and the working time of each worker.
Analyzing the working parameters of each worker in the current monitoring period corresponding to each manufacturing assembly line to obtain the working parameters of each worker in the current monitoring period corresponding to each manufacturing assembly line, wherein the specific analyzing steps are as follows:
and extracting the number of finished products of each worker in the current monitoring period corresponding to each manufacturing assembly line from the manufacturing video of each manufacturing assembly line corresponding to the current monitoring period, and obtaining the number of finished products of each worker in the current monitoring period corresponding to each manufacturing assembly line.
Extracting the working time length of each worker in each manufacturing assembly line corresponding to the current monitoring period from the basic parameters of the worker in each manufacturing assembly line corresponding to the current monitoring period, and taking the value as GT i j 。
Matching the working time length of each worker in the current monitoring period corresponding to each manufacturing assembly line with the number of the reference finished products corresponding to the set working time length to obtain the number of the reference finished products of each worker in the current monitoring period corresponding to each manufacturing assembly line, taking the number of the reference finished products, and recording the number as WC ij 0 . i is denoted as the number of each manufacturing line, i=1, 2,..n, j is denoted as the number of each worker, j=1, 2,..m, n is denoted as the total number of manufacturing line numbers, and m is denoted as the total number of worker numbers.
According to formula XL i j =(WC i j /GT i j )×z1+(WC i j /WC ij 0 )×z2+(1/(|GT i j -GT 0 Calculating work efficiency value XL of each worker in the current monitoring period corresponding to each manufacturing line by using |+1) x z3 i j ，WC i j Expressed as the number of finished products of the ith manufacturing line corresponding to the jth worker in the current monitoring period, GT i j The working time length of the jth worker in the current monitoring period is expressed as the ith manufacturing line, GT 0 The reference working time length of the set worker corresponding to the monitoring period is represented as z1, z2 and z3, and the set weighting factors are represented as set weighting factors.
The working time length tables of all workers corresponding to the monitoring periods are extracted from the database, and the working time length tables of all workers corresponding to the monitoring periods of all manufacturing pipelines are extracted from the working time length tables of all workers corresponding to the monitoring periods.
Acquiring each worker corresponding to each manufacturing assembly line from a work time length table of each worker corresponding to the monitoring period of each manufacturing assembly line based on the current monitoring periodThe continuous working time length of each worker corresponding to each manufacturing assembly line is obtained, and the numerical value is taken and recorded as LT i j 。
And extracting the reference continuous working time length corresponding to the worker from the database, and taking the value of the reference continuous working time length as T. It should be noted that the continuous working time length refers to a working time length in a unit time period, for example, a working time length of a certain staff is from eight points in the morning to five points in the afternoon, a rest time length in the midway is not counted, the continuous working time length of the staff is nine hours, a working time length of a certain staff is from ten points in the evening to five points in the next day, a rest time length in the midway is likewise not counted, and the continuous working time length of the staff is seven hours.
According to formula PL i j =LT i j Calculating fatigue value PL of each worker in current monitoring period corresponding to each manufacturing line i j 。
And working parameters of workers of each manufacturing assembly line corresponding to the current monitoring period are formed by the work efficiency value and the fatigue value of each worker in the current monitoring period of each manufacturing assembly line.
Preferably, the method and the device acquire the manufacturing video of each manufacturing assembly line corresponding to the current monitoring period in the factory, analyze the number of workers and the working time of each worker of each manufacturing assembly line corresponding to the current monitoring period from the manufacturing video, and analyze the work efficiency value and the fatigue value of each worker of each manufacturing assembly line corresponding to the current monitoring period from the manufacturing video, so that the real-time state of each worker in each manufacturing assembly line can be intuitively known, reliable data support is provided for the analysis of the effective evaluation value of each subsequent manufacturing assembly line corresponding to the current monitoring period, the reliability and the accuracy of the effective evaluation value analysis result of each manufacturing assembly line corresponding to the current monitoring period are improved, and the execution of the subsequent intelligent manufacturing schedule is further facilitated.
Step three, obtaining the manufacturing output of each manufacturing assembly line corresponding to the current monitoring period, analyzing the effective evaluation value of each manufacturing assembly line corresponding to the current monitoring period based on the manufacturing output of each manufacturing assembly line corresponding to the current monitoring period, the basic parameters of workers and the working parameters of workers, and obtaining the effective evaluation value of each manufacturing assembly line corresponding to the current monitoring period, wherein the specific analysis steps are as follows:
extracting the value of the manufacturing yield of each manufacturing pipeline corresponding to the current monitoring period, and recording the value as ZN i 。
Extracting the numerical value of the number of workers of each manufacturing assembly line corresponding to the current monitoring period from the basic parameters of the workers of each manufacturing assembly line corresponding to the current monitoring period, and recording the numerical value as GN i The method comprises the steps of carrying out a first treatment on the surface of the Simultaneously extracting the working time length GT of each worker in the current monitoring period corresponding to each manufacturing assembly line i j 。
Extracting work efficiency value XL of each worker in each manufacturing assembly line corresponding to the current monitoring period from worker work parameters of each manufacturing assembly line corresponding to the current monitoring period i j And fatigue value PL of each worker i j 。
Calculating an effective evaluation value PZ of each manufacturing assembly line corresponding to the current monitoring period through a formula i The calculation formula is as follows:
，ZN 0 reference production yield, GN, expressed as the corresponding monitoring period stored in the database i 0 Expressed as a reference worker number, GT, corresponding to the ith manufacturing line stored in the database 0 The reference working time periods of the set worker corresponding to the monitoring periods are indicated, and a1, a2, a3, a4 and a5 are respectively indicated as set weight factors.
Preferably, the invention can timely find the manufacturing efficiency in the manufacturing assembly line by analyzing the effective evaluation value of each manufacturing assembly line corresponding to the current monitoring period based on the manufacturing yield, the basic parameters of workers and the working parameters of workers of each manufacturing assembly line corresponding to the current monitoring period, thereby providing a data basis for the subsequent optimized production line, greatly improving the production efficiency and the production capacity and reducing the cost and the risk.
Analyzing the efficiency level of each manufacturing assembly line corresponding to the current monitoring period based on the efficiency evaluation value of each manufacturing assembly line corresponding to the current monitoring period to obtain the efficiency level of each manufacturing assembly line corresponding to the current monitoring period, wherein the specific analysis mode is as follows: and matching the effective evaluation value of each manufacturing assembly line corresponding to the current monitoring period with the effective evaluation value interval of each set efficiency level to obtain the efficiency level of each manufacturing assembly line corresponding to the current monitoring period, wherein the efficiency level is specifically one, two, three and the like.
If the efficiency level of a certain manufacturing assembly line corresponding to the current monitoring period is three or equal, personnel movement parameters corresponding to the manufacturing assembly line are analyzed, and if the efficiency level of the certain manufacturing assembly line corresponding to the current monitoring period is two-level, personnel display parameters corresponding to the manufacturing assembly line are analyzed, so that execution parameters corresponding to the manufacturing assembly lines are formed, and the specific analysis mode is as follows:
if the efficiency level of a certain manufacturing assembly line corresponding to the current monitoring period is three, judging that the efficiency of the manufacturing assembly line corresponding to the current monitoring period is low, and performing difference between the efficiency evaluation value of the manufacturing assembly line corresponding to the current monitoring period and the set reference efficiency evaluation value to obtain an efficiency evaluation value of the manufacturing assembly line corresponding to the current monitoring period, and recording the efficiency evaluation value as a value to be adjusted of the manufacturing assembly line corresponding to the current monitoring period.
If the efficiency level of a certain manufacturing assembly line corresponding to the current monitoring period is equal, judging that the efficiency of the manufacturing assembly line corresponding to the current monitoring period is high, and performing difference between the efficiency evaluation value of the manufacturing assembly line corresponding to the current monitoring period and the set reference efficiency evaluation value to obtain an efficiency evaluation value of the manufacturing assembly line corresponding to the current monitoring period, and recording the efficiency evaluation value as a borrowing and regulating value of the manufacturing assembly line corresponding to the current monitoring period.
And screening the manufacturing pipelines with the first efficiency grade from the efficiency grades of the manufacturing pipelines corresponding to the current monitoring period, marking the manufacturing pipelines as first grade pipelines, and obtaining the borrowing value of each first grade pipeline corresponding to the current monitoring period.
And screening out manufacturing pipelines with the efficiency level of third grade from the efficiency levels of the manufacturing pipelines corresponding to the current monitoring period, marking the manufacturing pipelines as the third grade pipelines, and obtaining the required adjustment value of each third grade pipeline corresponding to the current monitoring period.
And matching the borrowing value of each first-class pipeline corresponding to the current monitoring period with the number of the mobilizable personnel corresponding to the set borrowing value to obtain the number of the mobilizable personnel of each first-class pipeline corresponding to the current monitoring period.
Matching the value to be adjusted of each three-equal-pipeline corresponding to the current monitoring period with the number of people to be moved corresponding to the set value to be adjusted, and obtaining the number of people to be moved of each three-equal-pipeline corresponding to the current monitoring period.
In a specific embodiment, if the number of mobilizers of a certain equal pipeline corresponding to the current monitoring period is equal to the number of mobilizers of a certain three equal pipeline corresponding to the current monitoring period, mobilizing the workers of the equal pipeline corresponding to the current monitoring period into the three equal pipeline according to the number of mobilizers of the equal pipeline so as to meet the requirement of mobilizers of the three equal pipeline.
And screening out the manufacturing pipelines with the efficiency level of two levels from the efficiency levels of the manufacturing pipelines corresponding to the current monitoring period, recording the manufacturing pipelines as two-level pipelines, acquiring the number of workers of the two-level pipelines corresponding to the current monitoring period, and taking the number of workers as personnel display parameters.
And fifthly, executing corresponding processing based on the execution parameters corresponding to each manufacturing pipeline.
In a specific embodiment, corresponding processing is performed based on the execution parameters corresponding to each manufacturing pipeline, for example, the number of the mobilizers corresponding to the current monitoring period of each first-class pipeline is summed, the number of the mobilizers corresponding to the current monitoring period of each third-class pipeline is not obtained, the number of the mobilizers corresponding to the current monitoring period of each third-class pipeline is summed, the number of the mobilizers is obtained, if the number of the mobilizers is greater than or equal to the number of the mobilizers, the total mobilizers are randomly distributed to each third-class pipeline, if the number of the mobilizers is less than the number of the mobilizers, the difference between the number of the mobilizers and the number of the mobilizers is calculated, meanwhile, the number of the three-class pipelines is obtained, if the number of the mobilizers is five, the difference between the number of the mobilizers and the number of the mobilizers is three, three people are extracted from the number of the total mobilizers, the remaining mobilizers are uniformly distributed according to the number of the mobilizers corresponding to the current monitoring period of each third-class pipeline, and the corresponding to the number of the three-class pipeline is simultaneously distributed to the number of the mobilizers.
Preferably, the invention analyzes the execution parameters corresponding to each manufacturing assembly line, thereby realizing intelligent scheduling of the manufacturing assembly line, improving the rationality and the utilization rate of resource allocation, improving the production efficiency of the manufacturing assembly line, greatly reducing the idle and waste of resources and improving the economic benefit of factories.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.

Claims (6)

1. An intelligent manufacturing scheduling method with self-learning capability is characterized by comprising the following steps:
step one, acquiring production lines corresponding to a factory to obtain each production line corresponding to the factory, and simultaneously acquiring the production video of each production line corresponding to the factory in the current monitoring period to obtain the production video of each production line corresponding to the current monitoring period;
analyzing basic parameters of workers corresponding to the current monitoring period of each manufacturing assembly line based on the manufacturing video corresponding to the current monitoring period of each manufacturing assembly line to obtain basic parameters of the workers corresponding to the current monitoring period of each manufacturing assembly line, and analyzing working parameters of the workers in the current monitoring period of each manufacturing assembly line to obtain working parameters of the workers corresponding to the current monitoring period of each manufacturing assembly line;
step three, obtaining the manufacturing output of each manufacturing assembly line corresponding to the current monitoring period, analyzing the effective evaluation value of each manufacturing assembly line corresponding to the current monitoring period based on the manufacturing output of each manufacturing assembly line corresponding to the current monitoring period, the basic parameters of workers and the working parameters of workers, and obtaining the effective evaluation value of each manufacturing assembly line corresponding to the current monitoring period, wherein the specific analysis mode is as follows:
extracting the value of the manufacturing yield of each manufacturing pipeline corresponding to the current monitoring period, and recording the value as ZN i I is denoted as the number of each manufacturing line, i=1, 2,..n;
extracting the numerical value of the number of workers of each manufacturing assembly line corresponding to the current monitoring period from the basic parameters of the workers of each manufacturing assembly line corresponding to the current monitoring period, and recording the numerical value as GN i The method comprises the steps of carrying out a first treatment on the surface of the Simultaneously extracting the working time length GT of each worker in the current monitoring period corresponding to each manufacturing assembly line i j The method comprises the steps of carrying out a first treatment on the surface of the j is denoted as the number of each worker, j=1, 2, m;
extracting the working time length of each worker in each manufacturing assembly line corresponding to the current monitoring period from the basic parameters of the worker in each manufacturing assembly line corresponding to the current monitoring period, and taking the value as GT i j ；
Extracting work efficiency value XL of each worker in each manufacturing assembly line corresponding to the current monitoring period from worker work parameters of each manufacturing assembly line corresponding to the current monitoring period i j And fatigue value PL of each worker i j ；
Calculating an effective evaluation value PZ of each manufacturing assembly line corresponding to the current monitoring period through a formula i The calculation formula is as follows:
，ZN 0 reference production yield, GN, expressed as the corresponding monitoring period stored in the database i 0 Expressed as a reference worker number corresponding to the ith manufacturing line stored in the database,GT 0 the reference working time length of the set worker corresponding to the monitoring period is represented as a1, a2, a3, a4 and a5, and the set working time length is represented as a set weight factor respectively;
analyzing the efficiency grade of each manufacturing assembly line corresponding to the current monitoring period based on the efficiency evaluation value of each manufacturing assembly line corresponding to the current monitoring period to obtain the efficiency grade of each manufacturing assembly line corresponding to the current monitoring period, analyzing personnel movement parameters corresponding to a certain manufacturing assembly line if the efficiency grade of the manufacturing assembly line corresponding to the current monitoring period is three or equal, and analyzing personnel display parameters corresponding to the manufacturing assembly line if the efficiency grade of the manufacturing assembly line corresponding to the current monitoring period is equal, thereby forming execution parameters corresponding to each manufacturing assembly line;
and fifthly, executing corresponding processing based on the execution parameters corresponding to each manufacturing pipeline.
2. The intelligent manufacturing scheduling method with self-learning capability according to claim 1, wherein the analyzing the basic parameters of the workers of each manufacturing assembly line corresponding to the current monitoring period is performed by the following specific analyzing method:
analyzing the manufacturing video of each manufacturing assembly line corresponding to the current monitoring period to obtain manufacturing images of each manufacturing assembly line corresponding to each monitoring time point in the current monitoring period;
extracting face images of workers of each manufacturing assembly line corresponding to each monitoring time point in the current monitoring time period from the manufacturing images of each monitoring time point of each manufacturing assembly line corresponding to the current monitoring time period, comparing the face images of the workers of each monitoring time point with each other, and removing face images of the same workers from the face images to obtain face images of each worker of each manufacturing assembly line corresponding to the current monitoring time period, so that the number of workers of each manufacturing assembly line corresponding to the current monitoring time period is counted to be used as the number of workers of each manufacturing assembly line corresponding to the current monitoring time period;
acquiring face images of all working workers in the current monitoring period corresponding to all manufacturing pipelines, matching the face images of all working workers in the current monitoring period corresponding to all manufacturing pipelines with the face images of workers in the corresponding monitoring time points in the manufacturing pipelines, if the face images of a certain working worker are successfully matched with the face images of the workers in the certain monitoring time points, recording the monitoring time points as working time points, thus obtaining all working time points of all working workers in the current monitoring period corresponding to all manufacturing pipelines, integrating adjacent working time points, obtaining all working time periods of all working workers in the current monitoring period corresponding to all manufacturing pipelines, and counting the total duration of all working time periods of all working workers in the current monitoring period corresponding to all manufacturing pipelines as the working time duration of all working workers in the current monitoring period corresponding to all manufacturing pipelines;
and forming basic parameters of workers of each manufacturing assembly line corresponding to the current monitoring period by the number of workers of each manufacturing assembly line corresponding to the current monitoring period and the working time of each worker.
3. The intelligent manufacturing scheduling method with self-learning capability according to claim 1, wherein the analyzing the worker working parameters of each manufacturing line corresponding to the current monitoring period is performed by the following specific analyzing method:
extracting the number of finished products of each worker in the current monitoring period corresponding to each manufacturing assembly line from the manufacturing video of each manufacturing assembly line corresponding to the current monitoring period to obtain the number of finished products of each worker in the current monitoring period corresponding to each manufacturing assembly line;
extracting the working time length of each worker in the current monitoring period corresponding to each manufacturing assembly line from the basic parameters of the workers in the current monitoring period corresponding to each manufacturing assembly line, and obtaining the work efficiency value of each worker in the current monitoring period corresponding to each manufacturing assembly line through analysis;
extracting working time length tables of all workers corresponding to the monitoring periods from a database, and extracting working time length tables of all workers corresponding to the monitoring periods of all manufacturing pipelines from the working time length tables of all workers corresponding to the monitoring periods;
based on the current monitoring period, acquiring continuous working time lengths of all working workers corresponding to all manufacturing pipelines from a working time length table of all working workers corresponding to the monitoring period of all manufacturing pipelines, obtaining continuous working time lengths of all working workers corresponding to all manufacturing pipelines, and obtaining fatigue values of all working workers in the current monitoring period corresponding to all manufacturing pipelines through analysis;
and working parameters of workers of each manufacturing assembly line corresponding to the current monitoring period are formed by the work efficiency value and the fatigue value of each worker in the current monitoring period of each manufacturing assembly line.
4. The intelligent manufacturing scheduling method with self-learning capability according to claim 1, wherein the analyzing the efficiency level of each manufacturing pipeline corresponding to the current monitoring period based on the efficiency evaluation value of each manufacturing pipeline corresponding to the current monitoring period is performed to obtain the efficiency level of each manufacturing pipeline corresponding to the current monitoring period by the following specific analysis method:
and matching the effective evaluation value of each manufacturing assembly line corresponding to the current monitoring period with the effective evaluation value interval of each set efficiency level to obtain the efficiency level of each manufacturing assembly line corresponding to the current monitoring period, wherein the efficiency level is specifically one, two, three and the like.
5. The intelligent manufacturing scheduling method with self-learning capability according to claim 1, wherein if the efficiency level of a certain manufacturing line corresponding to the current monitoring period is three or equal, the personnel movement parameters corresponding to the manufacturing line are analyzed in the following specific analysis modes:
if the efficiency level of a certain manufacturing assembly line corresponding to the current monitoring period is three, the efficiency evaluation value of the manufacturing assembly line corresponding to the current monitoring period is differenced with the set reference efficiency evaluation value, so that the efficiency evaluation value of the manufacturing assembly line corresponding to the current monitoring period is obtained and recorded as a value to be adjusted of the manufacturing assembly line corresponding to the current monitoring period;
if the efficiency grade of a certain manufacturing assembly line corresponding to the current monitoring period is equal, performing difference between the efficiency evaluation value of the manufacturing assembly line corresponding to the current monitoring period and the set reference efficiency evaluation value to obtain an efficiency evaluation value of the manufacturing assembly line corresponding to the current monitoring period, and recording the efficiency evaluation value as a borrowing and regulating value of the manufacturing assembly line corresponding to the current monitoring period;
screening out manufacturing pipelines with the first efficiency grade from the efficiency grades of the manufacturing pipelines corresponding to the current monitoring period, marking the manufacturing pipelines as first grade pipelines, and obtaining borrowing and regulating values of the first grade pipelines corresponding to the current monitoring period;
screening out manufacturing pipelines with the efficiency level of third class from the efficiency levels of the manufacturing pipelines corresponding to the current monitoring period, marking the manufacturing pipelines as the third class pipelines, and obtaining the value required to be adjusted of each third class pipeline corresponding to the current monitoring period;
matching the borrowing value of each first-class pipeline corresponding to the current monitoring period with the number of the mobilizable personnel corresponding to the set borrowing value to obtain the number of the mobilizable personnel of each first-class pipeline corresponding to the current monitoring period;
matching the value to be adjusted of each three-equal-pipeline corresponding to the current monitoring period with the number of people to be moved corresponding to the set value to be adjusted, and obtaining the number of people to be moved of each three-equal-pipeline corresponding to the current monitoring period.
6. The intelligent manufacturing scheduling method with self-learning capability according to claim 1, wherein if the efficiency level of a certain manufacturing line corresponding to the current monitoring period is equal to two, the personnel display parameters corresponding to the manufacturing line are analyzed in the following specific analysis modes:
and screening out the manufacturing pipelines with the efficiency level of two levels from the efficiency levels of the manufacturing pipelines corresponding to the current monitoring period, recording the manufacturing pipelines as two-level pipelines, acquiring the number of workers of the two-level pipelines corresponding to the current monitoring period, and taking the number of workers as personnel display parameters.
CN202311058332.0A 2023-08-22 2023-08-22 Intelligent manufacturing scheduling method with self-learning capability Active CN116757562B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311058332.0A CN116757562B (en) 2023-08-22 2023-08-22 Intelligent manufacturing scheduling method with self-learning capability

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311058332.0A CN116757562B (en) 2023-08-22 2023-08-22 Intelligent manufacturing scheduling method with self-learning capability

Publications (2)

Publication Number Publication Date
CN116757562A true CN116757562A (en) 2023-09-15
CN116757562B CN116757562B (en) 2023-12-05

Family

ID=87948253

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202311058332.0A Active CN116757562B (en) 2023-08-22 2023-08-22 Intelligent manufacturing scheduling method with self-learning capability

Country Status (1)

Country Link
CN (1) CN116757562B (en)

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003001534A (en) * 2001-06-22 2003-01-08 Fujitaka Kogyo Kk Seat assembling variable tact and signal managing system
US20050154634A1 (en) * 2003-03-08 2005-07-14 Robert Konop Human factors scheduling safety system
US20100049574A1 (en) * 2008-08-21 2010-02-25 Toyota Motor Engineering & Manufacturing North America, Inc. System and method for optimizing manufacturing workforce
US20180174090A1 (en) * 2016-12-15 2018-06-21 Hitachi, Ltd. Production Management Support Apparatus, Production Management Support Method, and Production Management Support Program
CN109460934A (en) * 2018-11-19 2019-03-12 国网四川省电力公司眉山供电公司 The affairs of facing to manufacture teams and groups plan as a whole regulation method
CN111310105A (en) * 2020-03-12 2020-06-19 深圳前海微众银行股份有限公司 Method and device for determining recruitment scheme
US20200327482A1 (en) * 2019-04-09 2020-10-15 Jtekt Corporation Personnel assignment support system
CN115829244A (en) * 2022-11-22 2023-03-21 成都念宏欣建材有限公司 Building construction worker intelligent distribution management system based on big data
CN116050788A (en) * 2023-01-16 2023-05-02 浙江元畅不锈钢科技有限公司 Industrial intelligent scheduling management system
KR20230081029A (en) * 2021-11-30 2023-06-07 한국생산기술연구원 Method for scheduling of bearing manufacturing operations
CN116307928A (en) * 2023-04-20 2023-06-23 江西中烟工业有限责任公司 Object supervision management system
CN116416574A (en) * 2023-03-31 2023-07-11 浪潮通信技术有限公司 Video-based assembly line processing operation analysis method and system

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003001534A (en) * 2001-06-22 2003-01-08 Fujitaka Kogyo Kk Seat assembling variable tact and signal managing system
US20050154634A1 (en) * 2003-03-08 2005-07-14 Robert Konop Human factors scheduling safety system
US20100049574A1 (en) * 2008-08-21 2010-02-25 Toyota Motor Engineering & Manufacturing North America, Inc. System and method for optimizing manufacturing workforce
US20180174090A1 (en) * 2016-12-15 2018-06-21 Hitachi, Ltd. Production Management Support Apparatus, Production Management Support Method, and Production Management Support Program
CN109460934A (en) * 2018-11-19 2019-03-12 国网四川省电力公司眉山供电公司 The affairs of facing to manufacture teams and groups plan as a whole regulation method
US20200327482A1 (en) * 2019-04-09 2020-10-15 Jtekt Corporation Personnel assignment support system
CN111310105A (en) * 2020-03-12 2020-06-19 深圳前海微众银行股份有限公司 Method and device for determining recruitment scheme
KR20230081029A (en) * 2021-11-30 2023-06-07 한국생산기술연구원 Method for scheduling of bearing manufacturing operations
CN115829244A (en) * 2022-11-22 2023-03-21 成都念宏欣建材有限公司 Building construction worker intelligent distribution management system based on big data
CN116050788A (en) * 2023-01-16 2023-05-02 浙江元畅不锈钢科技有限公司 Industrial intelligent scheduling management system
CN116416574A (en) * 2023-03-31 2023-07-11 浪潮通信技术有限公司 Video-based assembly line processing operation analysis method and system
CN116307928A (en) * 2023-04-20 2023-06-23 江西中烟工业有限责任公司 Object supervision management system

Also Published As

Publication number Publication date
CN116757562B (en) 2023-12-05

Similar Documents

Publication Publication Date Title
CN110109431B (en) Intelligent acquiring system for OEE information of die casting machine
CN111241755A (en) Power load prediction method
CN107544450B (en) Process industry network model construction method and system based on data
CN111311090B (en) Intelligent scheduling method and device based on big data calculation and analysis
CN114782030B (en) Intelligent management system and method based on big data project
CN113867299A (en) IPPS problem solving method under uncertain disturbance based on digital twin
CN111754125A (en) Discrete workshop production plan evaluation method based on combined weighting
CN114066262A (en) Method, system and device for estimating cause-tracing reasoning of abnormal indexes after power grid dispatching and storage medium
CN114418177B (en) New product material distribution prediction method based on digital twin workshops for generating countermeasure network
CN117311299B (en) Factory management system and method based on multi-source heterogeneous data integration
CN116757562B (en) Intelligent manufacturing scheduling method with self-learning capability
CN114118832A (en) Bank scheduling method and system based on historical data prediction
CN117709617A (en) MES-based intelligent scheduling system for production workshop
CN116562599A (en) Efficient intelligent factory scheduling system based on deep learning
CN113570118A (en) Workshop scheduling and analyzing method based on scheduling rule
CN110264009B (en) Shared automobile dispatching system and dispatching method thereof
CN116050788A (en) Industrial intelligent scheduling management system
CN115719139A (en) Dispatching self-checking system for power grid dispatching operation management
CN114218868A (en) Machine tool health degree calculation method based on life cycle benefit prediction
CN113762754A (en) Low-entropy self-adaptive scheduling method for hybrid workshop
Turgay et al. Digital Twin Based Flexible Manufacturing System Modelling with Fuzzy Approach
CN115391985B (en) Metal resource metabolic process prediction method based on system dynamics
CN117151552B (en) Digital illumination data management system and method based on Internet of things
CN111696003B (en) Intelligent identification and early warning method for abnormal period of line loss rate of distribution room under drive of mass data
CN113408760B (en) Identification method for irreversible recovery workshop key manufacturing resources based on SIR

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