CN118732633B - A method for optimizing production task scheduling in a digital workshop of intelligent manufacturing - Google Patents
A method for optimizing production task scheduling in a digital workshop of intelligent manufacturing Download PDFInfo
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Abstract
The invention discloses an intelligent manufacturing digital workshop production task optimizing and scheduling method, which belongs to the field of general control systems, and comprises the steps of importing equipment operation data of all obtained production lines into an equipment operation abnormality assessment strategy to carry out equipment operation assessment, importing the obtained production line production assessment results and the equipment operation assessment results into a task scheduling strategy to carry out production task scheduling, carrying out production fault analysis and assessment on the equipment operation data and the equipment production condition data of all the production lines, carrying out evaluation and analysis on the similar conditions of the normal production lines and the production lines of the abnormal production lines while effectively finding the abnormal production lines, and automatically distributing the residual production quantity of the abnormal production lines by integrating the production analysis and assessment results of the normal production lines, thereby effectively improving the production task optimizing and scheduling efficiency and the distribution rationality.
Description
Technical Field
The invention belongs to the field of general control systems, and particularly relates to an intelligent manufacturing digital workshop production task optimization scheduling method.
Background
The intelligent manufacturing digital workshop has the advantages of high production efficiency, low production cost, stable production quality and the like, and has become the development direction of the modern manufacturing industry. In a digital workshop, the design and implementation of the production task optimization scheduling method have important significance for improving production efficiency and reducing production cost. However, the design and implementation of a production task optimization scheduling method for a digital shop still faces many challenges;
In the process of optimizing and dispatching production tasks in a digital workshop, production fault analysis and evaluation cannot be carried out on equipment operation data and equipment production condition data of each production line, so that abnormal production lines cannot be found effectively, evaluation and analysis cannot be carried out on production line similarity conditions of normal production lines and abnormal production lines, automatic distribution of the residual production quantity of the abnormal production lines cannot be carried out by integrating production analysis and evaluation results of the normal production lines, production task optimization and dispatching efficiency and distribution rationality are low, and most of the problems exist in the prior art;
An intelligent manufacturing digital workshop production task optimization scheduling method is disclosed in, for example, china patent with the publication number CN 117472014B. The method comprises the steps of firstly obtaining all machine combination codes for processing each workpiece, obtaining selectable times of each processing machine when processing corresponding workpieces according to reliability indexes of each processing machine, further screening all machine distribution codes from all machine combination codes, obtaining the dispatching combination number of each processing machine according to the processable procedure of each processing machine in each machine distribution code, further obtaining a plurality of processing sequence codes of the dispatching combination, further obtaining all individual codes, and obtaining an optimal production task dispatching scheme by utilizing a genetic algorithm. According to the invention, the coding redundancy is reduced by combining the performance condition of workshop processing machines, the possibility of machine performance overflow and the searching calculation amount of an algorithm are reduced, and the scheduling efficiency and reliability of workshop production tasks are improved;
Meanwhile, a multi-objective scheduling optimization method, a multi-objective scheduling optimization device and a multi-objective scheduling optimization storage medium for a flexible job shop are disclosed in China patent with the application publication number of CN 117952352A. According to the method, the work and rest time of workers and the leave-on rest are taken as main constraint conditions, the number of machine types, the number of workers and the matching degree of workers, machines and tasks are considered, meanwhile, the early-stage punishment and the cost of people are minimized, and a scheduling scheme of a flexible workshop operation is solved by improving an NSGA-II algorithm. Compared with the prior art, the invention has the advantages that the constraint consideration is more comprehensive, the optimized flexible workshop scheduling scheme is more in line with the production and living reality, the accuracy and the reliability are higher, and the like;
The problems of the background technology exist in the above patents, and in order to solve the problems of the background technology, the application designs an intelligent manufacturing digital workshop production task optimization scheduling method.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an intelligent manufacturing digital workshop production task optimization scheduling method.
In order to achieve the purpose, the invention provides the following technical scheme that the intelligent manufacturing digital workshop production task optimizing and scheduling method comprises the following specific steps:
Acquiring equipment operation data and equipment production condition data of each production line through a data acquisition module, and storing the equipment operation data and the equipment production condition data in a storage module in real time;
The obtained equipment operation data and equipment production condition data of each production line are imported into a production line production evaluation model to carry out production line production evaluation;
Leading the obtained equipment operation data of each production line into an equipment operation abnormality evaluation strategy to perform equipment operation evaluation;
and importing the obtained production line production evaluation result and the obtained equipment operation evaluation result into a task scheduling strategy to perform production task scheduling.
It should be noted that, as a preferential technical scheme of the intelligent manufacturing digital workshop production task optimizing and scheduling method, the specific steps of acquiring the equipment operation data and the equipment production condition data of each production line through the data acquisition module and storing the equipment operation data and the equipment production condition data in the storage module in real time are as follows:
s11, collecting equipment operation data of each production line through an equipment data collecting terminal, and simultaneously collecting production equipment type and quantity data on the production lines, and storing the data in a first storage module;
S12, acquiring production specification data of production products of all production lines through a product production acquisition terminal, and storing the production specification data in a second storage module;
It should be noted that, as a preferred technical scheme of the intelligent manufacturing digital workshop production task optimizing and scheduling method, the method for importing the obtained equipment operation data and equipment production condition data of each production line into a production line production evaluation model to perform production line production evaluation includes the following specific steps:
S21, obtaining production equipment type and quantity data on each production line, substituting the obtained production equipment type and quantity data on each production line into a production line similarity coefficient calculation formula to calculate production line similarity coefficients of any two production lines, wherein the production line similarity coefficient calculation formula of the z-th production line and the S-th production line is as follows: Wherein T () is the number of set elements in brackets, ks is a set of the types of production apparatuses sequentially arranged in the entire flow of the s-th production line, kz is a set of the types of production apparatuses sequentially arranged in the entire flow of the z-th production line, As an intersection set of the two points,Is a union;
S22, acquiring specification and size data of production products on each production line, and leading the specification and size data into a production product similarity coefficient calculation formula to calculate production product similarity coefficients of any two production lines, wherein the calculation formulas of the production product similarity coefficients of the z-th production line and the S-th production line are as follows: Wherein lz is specification and size data of products produced by a z-th production line, and ls is specification and size data of products produced by an s-th production line;
S23, obtaining the calculated production line similarity coefficients and production product similarity coefficients of two corresponding production lines, and substituting the obtained production line similarity coefficients and the obtained production product similarity coefficients into a production line similarity abnormal value calculation formula to calculate a production line similarity abnormal value, wherein the production line similarity abnormal value calculation formula of the z-th production line and the S-th production line is as follows: wherein a is the production line similarity coefficient duty ratio.
It should be noted that, as a preferential technical scheme of the intelligent manufacturing digital workshop production task optimizing and scheduling method, the step of importing the obtained equipment operation data of each production line into an equipment operation abnormality evaluation strategy to perform equipment operation evaluation comprises the following specific steps:
s31, acquiring equipment operation data in a set monitoring period of each production line, wherein the equipment operation data comprises equipment operation real-time current data, voltage data and temperature data;
S32, substituting the obtained equipment operation data in the set monitoring period of each production line into an equipment operation abnormal evaluation value calculation formula to calculate the equipment operation abnormal evaluation value of each production line, wherein the equipment operation abnormal evaluation value calculation formula of the ith production line is as follows: Wherein Ni is the number of devices of an ith production line, M is the type of device operation data, T is the duration of a monitoring period, dt is a time integral, xjct is a specific value of the operation data of the c-th device of the j-th device at the moment T of the ith production line, xjcm is the median value of the operation data safety range of the c-th device of the j-th device of the ith production line, xjcmax is the maximum value of the operation data safety range of the c-th device of the j-th device of the ith production line, xjcmin is the minimum value of the operation data safety range of the c-th device of the j-th device of the ith production line;
S33, acquiring the calculated equipment operation abnormality evaluation values of all production lines.
It should be noted that, as a preferential technical scheme of the intelligent manufacturing digital workshop production task optimizing and scheduling method, the method for carrying out production task scheduling by importing the obtained production line production evaluation result and the obtained equipment operation evaluation result into a task scheduling strategy comprises the following specific contents:
S41, comparing the calculated equipment operation abnormality evaluation value of each production line with a set equipment operation abnormality evaluation threshold, setting the equipment operation abnormality evaluation value smaller than or equal to the set equipment operation abnormality evaluation threshold as a normal production line, setting the equipment operation abnormality evaluation value larger than the set equipment operation abnormality evaluation threshold as an abnormal production line, and broadcasting the abnormal production line to maintenance staff;
S42, obtaining the required production capacity of an abnormal production line, substituting the required production capacity into a task scheduling strategy, and scheduling production tasks;
The method is characterized in that the method is used as a priority technical scheme of an intelligent manufacturing digital workshop production task optimization scheduling method, and the task scheduling strategy comprises the following specific contents:
S421, acquiring required throughput of an abnormal production line, abnormal values similar to those of other production lines and equipment operation abnormal evaluation values of the production lines of other production lines;
s422, comparing the line similarity abnormal values of the other production lines and the abnormal production line with a set line similarity abnormal value threshold, if the line similarity abnormal value of the other production lines and the abnormal production line is greater than or equal to the set line similarity abnormal value threshold, setting the corresponding other production lines as different production lines, and if the line similarity abnormal value of the other production lines and the abnormal production line is less than the set line similarity abnormal value threshold, setting the corresponding other production lines as similar production lines;
S423, obtaining a production line similar abnormal value of a similar production line and an abnormal production line, obtaining a device operation abnormal evaluation value of the similar production line and a required production volume of the abnormal production line, substituting the obtained values into a production volume distribution calculation formula to calculate a production volume distribution value, wherein the production volume distribution calculation formula of the r-th similar production line is as follows: Where M is the number of similar lines, exp () is the power of the natural constant e, mr is the line-like anomaly value of the r-th similar line and the anomaly line, sr is the equipment-running anomaly evaluation value of the r-th similar line, wz is the required throughput;
S424, distributing the required production according to the obtained production distribution value of the similar production line.
The intelligent manufacturing digital workshop production task optimization scheduling system is realized based on the intelligent manufacturing digital workshop production task optimization scheduling method, and specifically comprises a data acquisition module, a production evaluation module, an operation evaluation module and a production task scheduling module, wherein the data acquisition module is used for acquiring equipment operation data and equipment production condition data of each production line through the data acquisition module and storing the equipment operation data and the equipment production condition data in a storage module in real time;
The production evaluation module is used for importing the obtained equipment operation data and equipment production condition data of each production line into a production line production evaluation model to carry out production line production evaluation;
the operation evaluation module is used for importing the obtained equipment operation data of each production line into an equipment operation abnormality evaluation strategy to perform equipment operation evaluation;
the production task scheduling module is used for importing the obtained production line production evaluation result and the obtained equipment operation evaluation result into a task scheduling strategy to perform production task scheduling;
the two signal output ends of the data acquisition module are respectively connected with the production evaluation module and the operation evaluation module, and the signal output ends of the production evaluation module and the operation evaluation module are both connected with the production task scheduling module.
An electronic device comprises a processor and a memory, wherein the memory stores a computer program which can be called by the processor;
the processor executes the intelligent manufacturing digital workshop production task optimization scheduling method by calling the computer program stored in the memory.
A computer readable storage medium storing instructions that when executed on a computer cause the computer to perform an intelligent manufacturing digital plant production task optimization scheduling method as described above.
Compared with the prior art, the invention has the beneficial effects that:
According to the invention, the equipment operation data and the equipment production condition data of each production line are acquired through the data acquisition module and stored in the storage module in real time, the acquired equipment operation data and equipment production condition data of each production line are imported into the production line production evaluation model for carrying out production line production evaluation, the acquired equipment operation data of each production line are imported into the equipment operation abnormality evaluation strategy for carrying out equipment operation evaluation, the acquired production line production evaluation result and the equipment operation evaluation result are imported into the task scheduling strategy for carrying out production task scheduling, and the equipment operation data and the equipment production condition data of each production line are subjected to production fault analysis evaluation, so that the abnormal production line is effectively discovered, meanwhile, the similar conditions of the production lines of the normal production line and the abnormal production line are subjected to evaluation analysis, and the residual production quantity of the abnormal production line is automatically allocated by integrating the production analysis evaluation results of the normal production line, thereby effectively improving the production task optimal scheduling efficiency and allocation rationality.
Drawings
FIG. 1 is a schematic overall flow chart of an intelligent manufacturing digital workshop production task optimization scheduling method;
FIG. 2 is a schematic diagram of a task scheduling strategy flow of an intelligent manufacturing digital workshop production task optimization scheduling method of the invention;
FIG. 3 is a schematic diagram of an overall framework of an intelligent manufacturing digital workshop production task optimization scheduling system of the invention;
FIG. 4 is a schematic diagram of a connection between a server and a plant production line according to the present invention.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the application, its application, or uses.
In order to solve the technical problems in the background art, the invention provides a preferable embodiment, wherein the scene of the embodiment is shown in fig. 4, a plurality of production lines for producing different products exist in an intelligent manufacturing digital workshop, the production lines are represented by a production line 1 and a production line 2 in fig. 4, the production data of the production lines are transmitted to a server in real time, and the server issues a scheduling instruction to the production lines through the implementation mode of the embodiment;
the specific contents of this embodiment are:
1-2, the intelligent manufacturing digital workshop production task optimization scheduling method comprises the following specific steps:
Acquiring equipment operation data and equipment production condition data of each production line through a data acquisition module, and storing the equipment operation data and the equipment production condition data in a storage module in real time;
In this embodiment, the specific steps of acquiring the equipment operation data and the equipment production condition data of each production line through the data acquisition module and storing the equipment operation data and the equipment production condition data in the storage module in real time are as follows:
s11, collecting equipment operation data of each production line through an equipment data collecting terminal, and simultaneously collecting production equipment type and quantity data on the production lines, and storing the data in a first storage module;
S12, acquiring production specification data of production products of all production lines through a product production acquisition terminal, and storing the production specification data in a second storage module;
The obtained equipment operation data and equipment production condition data of each production line are imported into a production line production evaluation model to carry out production line production evaluation;
In this embodiment, the method for performing production line production evaluation by importing the obtained equipment operation data and equipment production condition data of each production line into a production line production evaluation model includes the following specific steps:
S21, obtaining production equipment type and quantity data on each production line, substituting the obtained production equipment type and quantity data on each production line into a production line similarity coefficient calculation formula to calculate production line similarity coefficients of any two production lines, wherein the production line similarity coefficient calculation formula of the z-th production line and the S-th production line is as follows: Wherein T () is the number of set elements in brackets, ks is a set of the types of production apparatuses sequentially arranged in the entire flow of the s-th production line, kz is a set of the types of production apparatuses sequentially arranged in the entire flow of the z-th production line, As an intersection set of the two points,Is a union;
S22, acquiring specification and size data of production products on each production line, and leading the specification and size data into a production product similarity coefficient calculation formula to calculate production product similarity coefficients of any two production lines, wherein the calculation formulas of the production product similarity coefficients of the z-th production line and the S-th production line are as follows: Wherein lz is specification and size data of products produced by a z-th production line, and ls is specification and size data of products produced by an s-th production line;
S23, obtaining the calculated production line similarity coefficients and production product similarity coefficients of two corresponding production lines, and substituting the obtained production line similarity coefficients and the obtained production product similarity coefficients into a production line similarity abnormal value calculation formula to calculate a production line similarity abnormal value, wherein the production line similarity abnormal value calculation formula of the z-th production line and the S-th production line is as follows: Wherein a is the ratio of the similarity coefficient of the production line;
in the embodiment, the similarity of the production line is accurately analyzed through the type and quantity data of the production equipment and the specification and size data of the production products on the production line;
Leading the obtained equipment operation data of each production line into an equipment operation abnormality evaluation strategy to perform equipment operation evaluation;
in this embodiment, importing the obtained device operation data of each production line into a device operation anomaly evaluation policy to perform device operation evaluation includes the following specific steps:
s31, acquiring equipment operation data in a set monitoring period of each production line, wherein the equipment operation data comprises equipment operation real-time current data, voltage data and temperature data;
the real-time current data, voltage data and temperature data of the operation of the equipment are key parameters for evaluating the operation condition of the equipment. The following are analyses of these data and their application in the assessment of the functioning of the device:
Current data
The evaluation meaning that the current is an important parameter in the operation of the device and reflects the magnitude of the load of the device. A small current may mean an insufficient load, while an excessive current may indicate an excessive load or a malfunction of the device;
voltage data
The evaluation meaning that the voltage is another key parameter of the operation of the equipment and directly relates to whether the equipment can work normally or not;
temperature data
The evaluation meaning is that heat is generated when the equipment runs, and the excessive temperature can mean that the equipment is overloaded or has faults;
trend analysis the trend analysis of current, voltage and temperature data can predict possible faults and performance changes of the device.
S32, substituting the obtained equipment operation data in the set monitoring period of each production line into an equipment operation abnormal evaluation value calculation formula to calculate the equipment operation abnormal evaluation value of each production line, wherein the equipment operation abnormal evaluation value calculation formula of the ith production line is as follows: Wherein Ni is the number of devices of an ith production line, M is the type of device operation data, T is the duration of a monitoring period, dt is a time integral, xjct is a specific value of the operation data of the c-th device of the j-th device at the moment T of the ith production line, xjcm is the median value of the operation data safety range of the c-th device of the j-th device of the ith production line, xjcmax is the maximum value of the operation data safety range of the c-th device of the j-th device of the ith production line, xjcmin is the minimum value of the operation data safety range of the c-th device of the j-th device of the ith production line;
S33, acquiring calculated equipment operation abnormality evaluation values of all production lines;
It should be noted that the set monitoring period is set according to the continuous production time of the production line, preferably within 1 hour from the acquisition time;
In the embodiment, the abnormal condition of the equipment operation is accurately analyzed by the equipment operation data in the set monitoring period;
importing the obtained production line production evaluation result and the obtained equipment operation evaluation result into a task scheduling strategy to perform production task scheduling;
In this embodiment, importing the obtained production line production evaluation result and the obtained equipment operation evaluation result into a task scheduling policy to perform production task scheduling includes the following specific contents:
S41, comparing the calculated equipment operation abnormality evaluation value of each production line with a set equipment operation abnormality evaluation threshold, setting the equipment operation abnormality evaluation value smaller than or equal to the set equipment operation abnormality evaluation threshold as a normal production line, setting the equipment operation abnormality evaluation value larger than the set equipment operation abnormality evaluation threshold as an abnormal production line, and broadcasting the abnormal production line to maintenance staff;
S42, obtaining the required production capacity of an abnormal production line, substituting the required production capacity into a task scheduling strategy, and scheduling production tasks;
The task scheduling strategy comprises the following specific contents:
S421, acquiring required throughput of an abnormal production line, abnormal values similar to those of other production lines and equipment operation abnormal evaluation values of the production lines of other production lines;
s422, comparing the line similarity abnormal values of the other production lines and the abnormal production line with a set line similarity abnormal value threshold, if the line similarity abnormal value of the other production lines and the abnormal production line is greater than or equal to the set line similarity abnormal value threshold, setting the corresponding other production lines as different production lines, and if the line similarity abnormal value of the other production lines and the abnormal production line is less than the set line similarity abnormal value threshold, setting the corresponding other production lines as similar production lines;
S423, obtaining a production line similar abnormal value of a similar production line and an abnormal production line, obtaining a device operation abnormal evaluation value of the similar production line and a required production volume of the abnormal production line, substituting the obtained values into a production volume distribution calculation formula to calculate a production volume distribution value, wherein the production volume distribution calculation formula of the r-th similar production line is as follows: Where M is the number of similar lines, exp () is the power of the natural constant e, mr is the line-like anomaly value of the r-th similar line and the anomaly line, sr is the equipment-running anomaly evaluation value of the r-th similar line, wz is the required throughput;
S424, distributing the required production according to the obtained production distribution value of the similar production line.
In this embodiment, it should be noted that, in this embodiment, the values of the equipment operation anomaly evaluation threshold, the production line similarity coefficient duty ratio, and the production line similarity anomaly value threshold are obtained according to experiments by those skilled in the art;
In this embodiment, it should be noted that, compared with the prior art, the method has the advantages that the data acquisition module acquires the equipment operation data and the equipment production condition data of each production line and stores the equipment operation data and the equipment production condition data in the storage module in real time, the acquired equipment operation data and the equipment production condition data of each production line are imported into the production line production evaluation model to perform production line production evaluation, the acquired equipment operation data of each production line is imported into the equipment operation abnormal evaluation strategy to perform equipment operation evaluation, the acquired production line production evaluation result and the acquired equipment operation evaluation result are imported into the task scheduling strategy to perform production task scheduling, and the equipment operation data and the equipment production condition data of each production line are subjected to production fault analysis evaluation, so that the abnormal production line is effectively discovered, meanwhile, the similar conditions of the normal production line and the production line of the abnormal production line are evaluated and analyzed, and the residual production quantity of the abnormal production line is automatically distributed by integrating the production analysis evaluation result of the normal production line, thereby effectively improving the production task optimization scheduling efficiency and allocation rationality.
The embodiment also provides an intelligent manufacturing digital workshop production task optimization scheduling system, which is realized based on the intelligent manufacturing digital workshop production task optimization scheduling method, as shown in fig. 3, and specifically comprises a data acquisition module, a production evaluation module, an operation evaluation module and a production task scheduling module, wherein the data acquisition module is used for acquiring equipment operation data and equipment production condition data of each production line through the data acquisition module and storing the equipment operation data and the equipment production condition data in a storage module in real time;
the production evaluation module is used for importing the obtained equipment operation data and equipment production condition data of each production line into a production line production evaluation model to carry out production line production evaluation;
the operation evaluation module is used for importing the obtained equipment operation data of each production line into an equipment operation abnormality evaluation strategy to perform equipment operation evaluation;
the production task scheduling module is used for importing the obtained production line production evaluation result and the obtained equipment operation evaluation result into a task scheduling strategy to perform production task scheduling;
The two signal output ends of the data acquisition module are respectively connected with the production evaluation module and the operation evaluation module, and the signal output end of the production evaluation module and the signal output end of the operation evaluation module are both connected with the production task scheduling module;
the embodiment also provides electronic equipment, which comprises a processor and a memory, wherein the memory stores a computer program which can be called by the processor;
the processor executes the intelligent manufacturing digital workshop production task optimizing and scheduling method by calling the computer program stored in the memory.
The electronic device can generate larger difference due to different configurations or performances, and can comprise one or more processors and one or more memories, wherein at least one computer program is stored in the memories, and the computer program is loaded and executed by the processors to realize the intelligent manufacturing digital workshop production task optimization scheduling method provided by the method embodiment. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The embodiment is not described in detail herein;
the present embodiment also proposes a computer-readable storage medium having stored thereon an erasable computer program;
when the computer program runs on the computer equipment, the computer equipment is caused to execute the intelligent manufacturing digital workshop production task optimizing and scheduling method.
For example, the computer readable storage medium can be read-only memory, random-access memory, read-only optical disks, magnetic tape, floppy disk, optical data storage device, etc.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Claims (5)
1. The intelligent manufacturing digital workshop production task optimization scheduling method is characterized by comprising the following specific steps of:
Acquiring equipment operation data and equipment production condition data of each production line through a data acquisition module, and storing the equipment operation data and the equipment production condition data in a storage module in real time;
The obtained equipment operation data and equipment production condition data of each production line are imported into a production line production evaluation model to carry out production line production evaluation;
Leading the obtained equipment operation data of each production line into an equipment operation abnormality evaluation strategy to perform equipment operation evaluation;
importing the obtained production line production evaluation result and the obtained equipment operation evaluation result into a task scheduling strategy to perform production task scheduling;
The method for carrying out production line production evaluation by importing the obtained equipment operation data and equipment production condition data of each production line into a production line production evaluation model comprises the following specific steps:
obtaining production equipment type and quantity data on each production line, substituting the obtained production equipment type and quantity data on each production line into a production line similarity coefficient calculation formula to calculate production line similarity coefficients of any two production lines, wherein the production line similarity coefficient calculation formula of the z-th production line and the s-th production line is as follows: Wherein T () is the number of set elements in brackets, ks is a set of the types of production apparatuses sequentially arranged in the entire flow of the s-th production line, kz is a set of the types of production apparatuses sequentially arranged in the entire flow of the z-th production line, As an intersection set of the two points,Is a union;
The method comprises the steps of obtaining specification and size data of production products on each production line, and leading the specification and size data into a production product similarity coefficient calculation formula to calculate production product similarity coefficients of any two production lines, wherein the production product similarity coefficient calculation formula of a z-th production line and a s-th production line is as follows: Wherein lz is specification and size data of products produced by a z-th production line, and ls is specification and size data of products produced by an s-th production line;
Obtaining the production line similarity coefficient and the production product similarity coefficient of the two corresponding production lines, substituting the obtained production line similarity coefficient and the obtained production product similarity coefficient into a production line similarity abnormal value calculation formula to calculate a production line similarity abnormal value, wherein the production line similarity abnormal value calculation formula of the z-th production line and the s-th production line is as follows: Wherein a is the ratio of the similarity coefficient of the production line;
The step of importing the obtained equipment operation data of each production line into an equipment operation abnormality evaluation strategy to perform equipment operation evaluation comprises the following specific steps:
Acquiring equipment operation data in a set monitoring period of each production line, wherein the equipment operation data comprises equipment operation real-time current data, voltage data and temperature data;
Substituting the obtained equipment operation data in the set monitoring period of each production line into an equipment operation abnormal evaluation value calculation formula to calculate the equipment operation abnormal evaluation value of each production line, wherein the equipment operation abnormal evaluation value calculation formula of the ith production line is as follows: Wherein Ni is the number of devices of an ith production line, M is the type of device operation data, T is the duration of a monitoring period, dt is a time integral, xjct is a specific value of the operation data of the c-th device of the j-th device at the moment T of the ith production line, xjcm is the median value of the operation data safety range of the c-th device of the j-th device of the ith production line, xjcmax is the maximum value of the operation data safety range of the c-th device of the j-th device of the ith production line, xjcmin is the minimum value of the operation data safety range of the c-th device of the j-th device of the ith production line;
acquiring the calculated equipment operation abnormality evaluation values of all production lines;
the method for carrying out production task scheduling by importing the obtained production line production evaluation result and the obtained equipment operation evaluation result into a task scheduling strategy comprises the following specific contents:
Comparing the calculated equipment operation abnormality evaluation value of each production line with a set equipment operation abnormality evaluation threshold, setting the equipment operation abnormality evaluation value smaller than or equal to the set equipment operation abnormality evaluation threshold as a normal production line, setting the equipment operation abnormality evaluation value larger than the set equipment operation abnormality evaluation threshold as an abnormal production line, and broadcasting the abnormal production line to maintenance staff;
Obtaining the required production capacity of an abnormal production line, substituting the required production capacity into a task scheduling strategy, and scheduling production tasks;
the specific steps of acquiring the equipment operation data and the equipment production condition data of each production line through the data acquisition module and storing the equipment operation data and the equipment production condition data in the storage module in real time are as follows:
s11, collecting equipment operation data of each production line through an equipment data collecting terminal, and simultaneously collecting production equipment type and quantity data on the production lines, and storing the data in a first storage module;
S12, acquiring production specification data of production products of each production line through a product production acquisition terminal, and storing the production specification data in a second storage module.
2. The optimized scheduling method for production tasks in an intelligent manufacturing digital workshop as claimed in claim 1, wherein the task scheduling strategy comprises the following specific contents:
acquiring required throughput of an abnormal production line, abnormal values similar to those of other production lines and equipment operation abnormal evaluation values of the production lines of the other production lines;
Comparing the line similarity abnormal value of the other production lines with the set line similarity abnormal value threshold, if the line similarity abnormal value of the other production lines with the abnormal production lines is larger than or equal to the set line similarity abnormal value threshold, setting the corresponding other production lines as different production lines, and if the line similarity abnormal value of the other production lines with the abnormal production lines is smaller than the set line similarity abnormal value threshold, setting the corresponding other production lines as similar production lines.
3. The method for optimizing and scheduling production tasks in an intelligent manufacturing digital workshop according to claim 2, wherein the task scheduling strategy further comprises the following specific contents of obtaining similar abnormal values of production lines of similar production lines and abnormal production lines, obtaining equipment operation abnormal evaluation values of similar production lines and required production volumes of abnormal production lines, substituting the obtained values into a production volume distribution calculation formula to calculate a production volume distribution value, wherein the production volume distribution calculation formula of the r-th similar production line is as follows: Where M is the number of similar lines, exp () is the power of the natural constant e, mr is the line-like anomaly value of the r-th similar line and the anomaly line, sr is the equipment-running anomaly evaluation value of the r-th similar line, wz is the required throughput;
the distribution of the required production amount is performed according to the obtained production amount distribution value of the similar production line.
4. An electronic device comprises a processor and a memory, wherein the memory stores a computer program which can be called by the processor;
-characterized in that the processor executes an intelligent manufacturing digital shop production task optimization scheduling method according to any of claims 1-3 by invoking a computer program stored in the memory.
5. A computer readable storage medium storing instructions which, when executed on a computer, cause the computer to perform an intelligent manufacturing digital plant production task optimization scheduling method according to any one of claims 1-3.
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