A method for optimizing production task scheduling in a digital workshop of intelligent manufacturing

Abstract

The present invention discloses a method for optimizing and scheduling production tasks in a digital workshop of intelligent manufacturing, and belongs to the general field of control systems. The present invention imports the acquired equipment operation data of each production line into an equipment operation abnormality evaluation strategy to perform equipment operation evaluation, and imports the acquired production evaluation results of the production line and the equipment operation evaluation results into a task scheduling strategy to perform production task scheduling. Production failure analysis and evaluation are performed on the equipment operation data and equipment production situation data of each production line. While effectively discovering abnormal production lines, similar situations of normal production lines and abnormal production lines are evaluated and analyzed, and the remaining production volume of the abnormal production line is automatically allocated to the abnormal production line based on the production analysis and evaluation results of the normal production line, thereby effectively improving the efficiency of optimizing and scheduling production tasks and the rationality of allocation.

Description

Technical Field

[0001] The present invention belongs to the general field of control systems, and specifically relates to a method for optimizing production task scheduling in a digital workshop of intelligent manufacturing. Background Art

[0002] Intelligent manufacturing digital workshops have the advantages of high production efficiency, low production cost, and stable production quality, and have become the development direction of modern manufacturing. In digital workshops, the design and implementation of production task optimization scheduling methods are of great significance for improving production efficiency and reducing production costs. However, the design and implementation of production task optimization scheduling methods in digital workshops still face many challenges;

[0003] In the process of optimizing and scheduling production tasks in a digital workshop, it is impossible to analyze and evaluate production failures by using the equipment operation data and equipment production data of each production line, resulting in the inability to effectively discover abnormal production lines, and further resulting in the inability to evaluate and analyze the similarities between normal production lines and abnormal production lines, and the inability to automatically allocate the remaining production volume of abnormal production lines based on the production analysis and evaluation results of normal production lines, resulting in low efficiency and rationality of optimized scheduling of production tasks. Most of the existing technologies have the above problems;

[0004] For example, a Chinese patent with the authorization publication number CN117472014B discloses a method for optimizing the scheduling of production tasks in a digital workshop for intelligent manufacturing. The invention first obtains all the machine combination codes for processing each workpiece; based on the reliability index of each processing machine, obtains the number of options for each processing machine when processing the corresponding workpiece, and then screens out all the machine allocation codes from all the machine combination codes; in each machine allocation code, obtains the scheduling combination number of each processing machine according to the processable procedures of each processing machine, and then obtains the scheduling combination number of processing sequence codes, thereby obtaining all individual codes, and using genetic algorithms to obtain the optimal production task scheduling plan. The invention reduces coding redundancy by combining the performance of the processing machines in the workshop, reduces the possibility of machine performance overflow and the search calculation amount of the algorithm, and improves the efficiency and reliability of workshop production task scheduling;

[0005] At the same time, a Chinese patent with application publication number CN117952352A discloses a multi-objective scheduling optimization method, device and storage medium for a flexible job shop. This method takes workers' work and rest time and leave as the main constraints, taking into account the number of machine types, the number of workers and the matching degree of workers, machines and tasks, while seeking to minimize the early-delay penalty and minimize the employment cost, and solves the scheduling plan for a class of flexible workshop operations by improving the NSGA-II algorithm. Compared with the prior art, this invention has the advantages of more comprehensive constraint considerations, optimized flexible workshop scheduling plans that are more in line with production and life reality, and higher accuracy and reliability;

[0006] The above patents all have the problems raised by this background technology. In order to solve the problems raised by this background technology, this application designs a method for optimizing the scheduling of production tasks in a digital workshop of intelligent manufacturing. Summary of the invention

[0007] In view of the deficiencies of the prior art, the present invention proposes a method for optimizing the scheduling of production tasks in a digital workshop of intelligent manufacturing.

[0008] To achieve the above object, the present invention provides the following technical solution: a method for optimizing production task scheduling in a digital workshop of intelligent manufacturing, which comprises the following specific steps:

[0009] The equipment operation data and equipment production status data of each production line are obtained through the data acquisition module and stored in the storage module in real time;

[0010] Importing the acquired equipment operation data and equipment production status data of each production line into the production line production evaluation model to perform production line production evaluation;

[0011] Importing the acquired equipment operation data of each production line into the equipment operation abnormality assessment strategy to conduct equipment operation assessment;

[0012] The obtained production line production evaluation results and equipment operation evaluation results are imported into the task scheduling strategy to perform production task scheduling.

[0013] It should be noted that, as a preferred technical solution for optimizing the scheduling of production tasks in a digital workshop of intelligent manufacturing, the specific steps of obtaining the equipment operation data and equipment production status data of each production line through the data acquisition module and storing them in real time in the storage module are as follows:

[0014] S11, collecting equipment operation data of each production line through the equipment data collection terminal, and collecting the type and quantity data of production equipment on the production line, and storing them in the first storage module;

[0015] S12, collecting production specification data of products produced on each production line through the product production collection terminal, and storing it in the second storage module;

[0016] It should be noted here that as a preferred technical solution for the method of optimizing the scheduling of production tasks in a digital workshop of intelligent manufacturing, the method of importing the acquired equipment operation data and equipment production status data of each production line into the production evaluation model of the production line to perform production evaluation of the production line includes the following specific steps:

[0017] S21. Obtain the type and quantity data of production equipment on each production line, substitute the obtained type and quantity data of production equipment on each production line into the production line similarity coefficient calculation formula to calculate the production line similarity coefficient 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: , where T() is the number of set elements in the brackets, ks is the set consisting of the types of production equipment arranged in sequence for the entire process of the s-th production line, and kz is the set consisting of the types of production equipment arranged in sequence for the entire process of the z-th production line. For the intersection, is a union;

[0018] S22, obtain the specification and size data of the products produced on each production line, import them into the production product similarity coefficient calculation formula to calculate the production product similarity coefficient of any two production lines, wherein the production product similarity coefficient calculation formula of the z-th production line and the s-th production line is: , where lz is the specification and size data of the product produced by the z-th production line, and ls is the specification and size data of the product produced by the s-th production line;

[0019] S23, obtaining the calculated production line similarity coefficient and the production product similarity coefficient of the two corresponding production lines, and substituting them into the production line similarity abnormal value calculation formula to calculate the 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: , where a is the ratio of production line similarity coefficient.

[0020] It should be noted here that as a preferred technical solution for the method of optimizing the scheduling of production tasks in a digital workshop of intelligent manufacturing, the method of importing the acquired equipment operation data of each production line into the equipment operation abnormality evaluation strategy for equipment operation evaluation includes the following specific steps:

[0021] S31, obtaining equipment operation data of each production line within a set monitoring period, wherein the equipment operation data includes real-time current data, voltage data and temperature data of the equipment operation;

[0022] S32, substituting the acquired equipment operation data within the set monitoring period of each production line into the equipment operation abnormality evaluation value calculation formula to calculate the equipment operation abnormality evaluation value of each production line, wherein the equipment operation abnormality evaluation value calculation formula of the i-th production line is: , where Ni is the number of equipment on the i-th production line, M is the type of equipment operation data, T is the duration of the monitoring cycle, dt is the time integral, xjct is the specific value of the c-th type of equipment operation data of the j-th equipment on the i-th production line at time t, xjcm is the median value of the safety range of the c-th type of equipment operation data of the j-th equipment on the i-th production line, xjcmax is the maximum value of the safety range of the c-th type of equipment operation data of the j-th equipment on the i-th production line, and xjcmin is the minimum value of the safety range of the c-th type of equipment operation data of the j-th equipment on the i-th production line;

[0023] S33. Obtain the calculated equipment operation abnormality assessment values ​​of all production lines.

[0024] It should be noted here that as a preferred technical solution for the optimization scheduling method of production tasks in a smart manufacturing digital workshop, the importation of the obtained production evaluation results of the production line and the equipment operation evaluation results into the task scheduling strategy for production task scheduling includes the following specific contents:

[0025] S41, comparing the calculated equipment operation abnormality assessment value of each production line with the set equipment operation abnormality assessment threshold, setting the equipment operation abnormality assessment value less than or equal to the set equipment operation abnormality assessment threshold as a normal production line, and setting the equipment operation abnormality assessment value greater than the set equipment operation abnormality assessment threshold as an abnormal production line, and reporting the abnormal production line to the maintenance staff;

[0026] S42, obtaining the required production volume of the abnormal production line and substituting it into the task scheduling strategy to perform production task scheduling;

[0027] It should be noted that as a preferred technical solution for optimizing the scheduling of production tasks in a digital workshop of intelligent manufacturing, the task scheduling strategy includes the following specific contents:

[0028] S421, obtaining the required production volume of the abnormal production line, the production line similarity abnormality values ​​of the remaining production lines and the abnormal production line, and the equipment operation abnormality evaluation values ​​of the remaining production lines;

[0029] S422, comparing the production line similarity abnormality values ​​between the remaining production lines and the abnormal production line with the set production line similarity abnormality threshold value, if the production line similarity abnormality values ​​between the remaining production lines and the abnormal production line are greater than or equal to the set production line similarity abnormality threshold value, then the corresponding remaining production lines are set as different production lines; if the production line similarity abnormality values ​​between the remaining production lines and the abnormal production line are less than the set production line similarity abnormality threshold value, then the corresponding remaining production lines are set as similar production lines;

[0030] S423, obtaining the production line similarity abnormality values ​​of the similar production line and the abnormal production line, obtaining the equipment operation abnormality evaluation value of the similar production line and the required production volume of the abnormal production line, and substituting them into the production volume allocation calculation formula to calculate the production volume allocation value, wherein the production volume allocation calculation formula of the rth similar production line is: , where M is the number of similar production lines, exp() is the power of the natural constant e, mr is the production line similarity anomaly value of the r-th similar production line and the abnormal production line, Sr is the equipment operation anomaly assessment value of the r-th similar production line, and Wz is the required production volume;

[0031] S424. Allocate the required production volume according to the obtained production volume allocation values ​​of similar production lines.

[0032] A system for optimizing and scheduling production tasks in a digital workshop of intelligent manufacturing, which is implemented based on the above-mentioned method for optimizing and scheduling production tasks in a digital workshop of intelligent manufacturing, 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 to obtain the equipment operation data and equipment production status data of each production line through the data acquisition module, and store them in a storage module in real time;

[0033] The production evaluation module is used to import the acquired equipment operation data and equipment production status data of each production line into the production line production evaluation model to perform production line production evaluation;

[0034] The operation evaluation module is used to import the acquired equipment operation data of each production line into the equipment operation abnormality evaluation strategy to perform equipment operation evaluation;

[0035] The production task scheduling module is used to import the obtained production evaluation results of the production line and the equipment operation evaluation results into the task scheduling strategy to perform production task scheduling;

[0036] Among them, the two signal output ends of the data acquisition module are connected to the production evaluation module and the operation evaluation module respectively, and the signal output end of the production evaluation module and the signal output end of the operation evaluation module are both connected to the production task scheduling module.

[0037] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0038] The processor executes the above-mentioned method for optimizing and scheduling production tasks in a digital workshop of intelligent manufacturing by calling the computer program stored in the memory.

[0039] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method for optimizing the scheduling of production tasks in a digital workshop of intelligent manufacturing.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention obtains the equipment operation data and equipment production status data of each production line through a data acquisition module, and stores them in a storage module in real time, imports the acquired equipment operation data and equipment production status data of each production line into a production line production evaluation model to perform production line production evaluation, imports the acquired equipment operation data of each production line into an equipment operation abnormality evaluation strategy to perform equipment operation evaluation, imports the acquired production line production evaluation results and equipment operation evaluation results into a task scheduling strategy to perform production task scheduling, and performs production fault analysis and evaluation on the equipment operation data and equipment production status data of each production line, while effectively discovering abnormal production lines, evaluates and analyzes the production line similarities between normal production lines and abnormal production lines, and automatically allocates the remaining production volume of abnormal production lines based on the production analysis and evaluation results of normal production lines, effectively improving the efficiency of production task optimization scheduling and the rationality of allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the overall process of a method for optimizing and scheduling production tasks in a digital workshop of intelligent manufacturing according to the present invention;

[0043] Figure 2 A task scheduling strategy flow chart of a method for optimizing production task scheduling in a digital workshop of intelligent manufacturing according to the present invention;

[0044] Figure 3 This is a schematic diagram of the overall framework of a production task optimization scheduling system for an intelligent manufacturing digital workshop of the present invention;

[0045] Figure 4 It is a schematic diagram of the connection between the server of the present invention and the workshop production line. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present application and its application or use.

[0047] In order to solve the technical problems raised in the background technology, the present invention provides a preferred embodiment: the scenario of this embodiment is as follows Figure 4As shown in the figure, there are several production lines for different products in the intelligent manufacturing digital workshop. Figure 4 In the figure, production line 1 and production line 2 are used to represent that the production data of the production lines are transmitted to the server in real time, and the server issues scheduling instructions to the production lines through the implementation method of this embodiment;

[0048] The specific contents of this embodiment are:

[0049] like Figure 1-Figure 2 As shown, a method for optimizing production task scheduling in a digital workshop of intelligent manufacturing includes the following specific steps:

[0050] The equipment operation data and equipment production status data of each production line are obtained through the data acquisition module and stored in the storage module in real time;

[0051] In this embodiment, the specific steps of acquiring the equipment operation data and equipment production status data of each production line through the data acquisition module and storing them in the storage module in real time are as follows:

[0052] S11, collecting equipment operation data of each production line through the equipment data collection terminal, and collecting the type and quantity data of production equipment on the production line, and storing them in the first storage module;

[0053] S12, collecting production specification data of products produced on each production line through the product production collection terminal, and storing it in the second storage module;

[0054] Importing the acquired equipment operation data and equipment production status data of each production line into the production line production evaluation model to perform production line production evaluation;

[0055] In this embodiment, the acquired equipment operation data and equipment production status data of each production line are imported into the production line production evaluation model to perform production line production evaluation, which includes the following specific steps:

[0056] S21. Obtain the type and quantity data of production equipment on each production line, substitute the obtained type and quantity data of production equipment on each production line into the production line similarity coefficient calculation formula to calculate the production line similarity coefficient 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: , where T() is the number of set elements in the brackets, ks is the set consisting of the types of production equipment arranged in sequence for the entire process of the s-th production line, and kz is the set consisting of the types of production equipment arranged in sequence for the entire process of the z-th production line. For the intersection, is a union;

[0057] S22, obtain the specification and size data of the products produced on each production line, import them into the production product similarity coefficient calculation formula to calculate the production product similarity coefficient of any two production lines, wherein the production product similarity coefficient calculation formula of the z-th production line and the s-th production line is: , where lz is the specification and size data of the product produced by the z-th production line, and ls is the specification and size data of the product produced by the s-th production line;

[0058] S23, obtaining the calculated production line similarity coefficient and the production product similarity coefficient of the two corresponding production lines, and substituting them into the production line similarity abnormal value calculation formula to calculate the 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: , where a is the proportion of production line similarity coefficient;

[0059] In this embodiment, the similarity of production lines is accurately analyzed through the type and quantity data of production equipment and the specification and size data of the products produced on the production line;

[0060] Importing the acquired equipment operation data of each production line into the equipment operation abnormality assessment strategy to conduct equipment operation assessment;

[0061] In this embodiment, the acquired equipment operation data of each production line is imported into the equipment operation abnormality assessment strategy to perform equipment operation assessment, which includes the following specific steps:

[0062] S31, obtaining equipment operation data of each production line within a set monitoring period, wherein the equipment operation data includes real-time current data, voltage data and temperature data of the equipment operation;

[0063] Real-time current data, voltage data and temperature data of equipment operation are key parameters for evaluating equipment operation. The following is the analysis of these data and their application in equipment operation evaluation:

[0064] Current data

[0065] Evaluation significance: Current is an important parameter when the equipment is running, which reflects the size of the equipment load. Too small current may mean insufficient load, while too large current may indicate that the load is too heavy or the equipment is faulty;

[0066] Voltage data

[0067] Evaluation significance: Voltage is another key parameter of equipment operation, which is directly related to whether the equipment can work normally;

[0068] Temperature data

[0069] Evaluation significance: When the equipment is running, it will generate heat. Excessive temperature may mean that the equipment is overloaded or has a fault.

[0070] Trend analysis: Trend analysis of current, voltage and temperature data can predict possible equipment failures and performance changes.

[0071] S32, substituting the acquired equipment operation data within the set monitoring period of each production line into the equipment operation abnormality evaluation value calculation formula to calculate the equipment operation abnormality evaluation value of each production line, wherein the equipment operation abnormality evaluation value calculation formula of the i-th production line is: , where Ni is the number of equipment on the i-th production line, M is the type of equipment operation data, T is the duration of the monitoring cycle, dt is the time integral, xjct is the specific value of the c-th type of equipment operation data of the j-th equipment on the i-th production line at time t, xjcm is the median value of the safety range of the c-th type of equipment operation data of the j-th equipment on the i-th production line, xjcmax is the maximum value of the safety range of the c-th type of equipment operation data of the j-th equipment on the i-th production line, and xjcmin is the minimum value of the safety range of the c-th type of equipment operation data of the j-th equipment on the i-th production line;

[0072] S33, obtaining the calculated equipment operation abnormality assessment values ​​of all production lines;

[0073] It should be noted that the monitoring period is set according to the continuous production time of the production line, preferably within 1 hour to the collection time;

[0074] In this embodiment, the abnormal situation of equipment operation is accurately analyzed by the equipment operation data within the set monitoring period;

[0075] Import the obtained production line production evaluation results and equipment operation evaluation results into the task scheduling strategy to perform production task scheduling;

[0076] In this embodiment, the obtained production line production evaluation results and equipment operation evaluation results are imported into the task scheduling strategy to perform production task scheduling, including the following specific contents:

[0077] S41, comparing the calculated equipment operation abnormality assessment value of each production line with the set equipment operation abnormality assessment threshold, setting the equipment operation abnormality assessment value less than or equal to the set equipment operation abnormality assessment threshold as a normal production line, and setting the equipment operation abnormality assessment value greater than the set equipment operation abnormality assessment threshold as an abnormal production line, and reporting the abnormal production line to the maintenance staff;

[0078] S42, obtaining the required production volume of the abnormal production line and substituting it into the task scheduling strategy to perform production task scheduling;

[0079] Among them, the task scheduling strategy includes the following specific contents:

[0080] S421, obtaining the required production volume of the abnormal production line, the production line similarity abnormality values ​​of the remaining production lines and the abnormal production line, and the equipment operation abnormality evaluation values ​​of the remaining production lines;

[0081] S422, comparing the production line similarity abnormality values ​​between the remaining production lines and the abnormal production line with the set production line similarity abnormality threshold value, if the production line similarity abnormality values ​​between the remaining production lines and the abnormal production line are greater than or equal to the set production line similarity abnormality threshold value, then the corresponding remaining production lines are set as different production lines; if the production line similarity abnormality values ​​between the remaining production lines and the abnormal production line are less than the set production line similarity abnormality threshold value, then the corresponding remaining production lines are set as similar production lines;

[0082] S423, obtaining the production line similarity abnormality values ​​of the similar production line and the abnormal production line, obtaining the equipment operation abnormality evaluation value of the similar production line and the required production volume of the abnormal production line, and substituting them into the production volume allocation calculation formula to calculate the production volume allocation value, wherein the production volume allocation calculation formula of the rth similar production line is: , where M is the number of similar production lines, exp() is the power of the natural constant e, mr is the production line similarity anomaly value of the r-th similar production line and the abnormal production line, Sr is the equipment operation anomaly assessment value of the r-th similar production line, and Wz is the required production volume;

[0083] S424. Allocate the required production volume according to the obtained production volume allocation values ​​of similar production lines.

[0084] It should be noted in this embodiment that the value of the equipment operation abnormality assessment threshold, the production line similarity coefficient ratio and the production line similarity abnormality threshold in this embodiment is obtained by experiments performed by those skilled in the art;

[0085] It should be noted in the present embodiment that the advantages of the present embodiment over the prior art are as follows: the equipment operation data and equipment production status data of each production line are obtained through the data acquisition module, and stored in the storage module in real time; the equipment operation data and equipment production status data of each production line obtained are imported into the production line production evaluation model to carry out production line production evaluation; the equipment operation data of each production line obtained are imported into the equipment operation abnormality evaluation strategy to carry out equipment operation evaluation; the production line production evaluation results and equipment operation evaluation results obtained are imported into the task scheduling strategy to carry out production task scheduling; production fault analysis and evaluation are carried out on the equipment operation data and equipment production status data of each production line, while effectively discovering abnormal production lines, the production line similarities between normal production lines and abnormal production lines are evaluated and analyzed, and the remaining production volume of the abnormal production line is automatically allocated based on the production analysis and evaluation results of the normal production line, thereby effectively improving the efficiency of production task optimization scheduling and the rationality of allocation.

[0086] This embodiment also provides a smart manufacturing digital workshop production task optimization scheduling system, which is implemented based on the above-mentioned smart manufacturing digital workshop production task optimization scheduling method, such as Figure 3 As shown, it specifically includes 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 to obtain the equipment operation data and equipment production status data of each production line through the data acquisition module, and store them in the storage module in real time;

[0087] A production evaluation module, used to import the acquired equipment operation data and equipment production status data of each production line into the production line production evaluation model to perform production line production evaluation;

[0088] An operation evaluation module is used to import the acquired equipment operation data of each production line into the equipment operation abnormality evaluation strategy to perform equipment operation evaluation;

[0089] A production task scheduling module is used to import the obtained production line production evaluation results and equipment operation evaluation results into the task scheduling strategy to perform production task scheduling;

[0090] Among them, the two signal output ends of the data acquisition module are connected to the production evaluation module and the operation evaluation module respectively, and the signal output end of the production evaluation module and the signal output end of the operation evaluation module are both connected to the production task scheduling module;

[0091] This embodiment also provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0092] The processor executes the above-mentioned method for optimizing and scheduling production tasks in a digital workshop of intelligent manufacturing by calling a computer program stored in the memory.

[0093] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement a method for optimizing the scheduling of production tasks in a digital workshop of intelligent manufacturing provided in the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface for data input and output. This embodiment will not be described in detail here;

[0094] This embodiment also provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0095] When the computer program runs on a computer device, the computer device executes the above-mentioned method for optimizing and scheduling production tasks in a digital workshop of intelligent manufacturing.

[0096] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0097] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

Claims

1. A method for optimizing production task scheduling in a digital workshop of intelligent manufacturing, characterized in that: It includes the following specific steps: The equipment operation data and equipment production status data of each production line are obtained through the data acquisition module and stored in the storage module in real time; Importing the acquired equipment operation data and equipment production status data of each production line into the production line production evaluation model to perform production line production evaluation; Importing the acquired equipment operation data of each production line into the equipment operation abnormality assessment strategy to conduct equipment operation assessment; Import the obtained production line production evaluation results and equipment operation evaluation results into the task scheduling strategy to perform production task scheduling; The step of importing the acquired equipment operation data and equipment production status data of each production line into the production line production evaluation model to perform production line production evaluation includes the following specific steps: Obtain the type and quantity data of production equipment on each production line, substitute the obtained type and quantity data of production equipment on each production line into the production line similarity coefficient calculation formula to calculate the production line similarity coefficient 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: , where T() is the number of set elements in the brackets, ks is the set consisting of the types of production equipment arranged in sequence for the entire process of the s-th production line, and kz is the set consisting of the types of production equipment arranged in sequence for the entire process of the z-th production line. For the intersection, is a union; Obtain the specification and size data of the products produced on each production line, import them into the production product similarity coefficient calculation formula to calculate the production product similarity coefficient of any two production lines, where the production product similarity coefficient calculation formula of the z-th production line and the s-th production line is: , where lz is the specification and size data of the product produced by the z-th production line, and ls is the specification and size data of the product produced by the s-th production line; Obtain the calculated production line similarity coefficient and product similarity coefficient of the two corresponding production lines, and substitute them into the production line similarity anomaly value calculation formula to calculate the production line similarity anomaly value. Among them, the production line similarity anomaly value calculation formula of the z-th production line and the s-th production line is: , where a is the proportion of production line similarity coefficient; The step of importing the acquired equipment operation data of each production line into the equipment operation abnormality assessment strategy to perform equipment operation assessment includes the following specific steps: Obtaining equipment operation data within a set monitoring period of each production line, wherein the equipment operation data includes real-time current data, voltage data and temperature data of the equipment operation; Substitute the acquired equipment operation data within the set monitoring period of each production line into the equipment operation abnormality evaluation value calculation formula to calculate the equipment operation abnormality evaluation value of each production line, where the equipment operation abnormality evaluation value calculation formula of the i-th production line is: , where Ni is the number of equipment on the i-th production line, M is the type of equipment operation data, T is the duration of the monitoring cycle, dt is the time integral, xjct is the specific value of the c-th type of equipment operation data of the j-th equipment on the i-th production line at time t, xjcm is the median value of the safety range of the c-th type of equipment operation data of the j-th equipment on the i-th production line, xjcmax is the maximum value of the safety range of the c-th type of equipment operation data of the j-th equipment on the i-th production line, and xjcmin is the minimum value of the safety range of the c-th type of equipment operation data of the j-th equipment on the i-th production line; Obtain the calculated equipment operation abnormality assessment values ​​of all production lines; The method of importing the obtained production line production evaluation results and equipment operation evaluation results into the task scheduling strategy for production task scheduling includes the following specific contents: Compare the calculated equipment operation abnormality assessment value of each production line with the set equipment operation abnormality assessment threshold, set the equipment operation abnormality assessment value less than or equal to the set equipment operation abnormality assessment threshold as a normal production line, and set the equipment operation abnormality assessment value greater than the set equipment operation abnormality assessment threshold as an abnormal production line, and report the abnormal production line to the maintenance staff; Obtain the required production volume of the abnormal production line and substitute it into the task scheduling strategy to schedule the production task; The specific steps of acquiring the equipment operation data and equipment production status data of each production line through the data acquisition module and storing them in the storage module in real time are as follows: S11, collecting equipment operation data of each production line through the equipment data collection terminal, and collecting the type and quantity data of production equipment on the production line, and storing them in the first storage module; S12. Collect production specification data of products produced on each production line through the product production collection terminal, and store the data in a second storage module.

2. The method for optimizing and scheduling production tasks in a digital workshop of intelligent manufacturing according to claim 1, characterized in that: The task scheduling strategy includes the following specific contents: Obtain the required production volume of the abnormal production line, production line similar abnormal values ​​of the remaining production lines and the abnormal production line, and equipment operation abnormality evaluation values ​​of the remaining production lines; The production line similarity anomaly values ​​of the remaining production lines and the abnormal production line are compared with the set production line similarity anomaly value threshold. If the production line similarity anomaly values ​​of the remaining production lines and the abnormal production line are greater than or equal to the set production line similarity anomaly value threshold, the corresponding remaining production lines are set as different production lines. If the production line similarity anomaly values ​​of the remaining production lines and the abnormal production line are less than the set production line similarity anomaly value threshold, the corresponding remaining production lines are set as similar production lines.

3. The method for optimizing production task scheduling in a digital workshop of intelligent manufacturing according to claim 2, characterized in that: The task scheduling strategy also includes the following specific contents: obtaining the production line similarity abnormality values ​​of similar production lines and abnormal production lines, obtaining the equipment operation abnormality evaluation value of the similar production line and the required production volume of the abnormal production line, and substituting them into the production volume allocation calculation formula to calculate the production volume allocation value, wherein the production volume allocation calculation formula of the rth similar production line is: , where M is the number of similar production lines, exp() is the power of the natural constant e, mr is the production line similarity anomaly value of the r-th similar production line and the abnormal production line, Sr is the equipment operation anomaly assessment value of the r-th similar production line, and Wz is the required production volume; The required production volume is allocated according to the obtained production volume allocation values ​​of similar production lines.

4. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the method for optimizing and scheduling production tasks in a digital workshop of intelligent manufacturing as described in any one of claims 1 to 3 by calling the computer program stored in the memory.

5. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes a method for optimizing and scheduling production tasks in a digital workshop of intelligent manufacturing as described in any one of claims 1 to 3.

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