A production scheduling method and system based on intelligent workshop

Abstract

The present application discloses a production scheduling method and system based on an intelligent workshop, which belongs to the field of data management systems. The present application proposes to obtain matching production lines based on the operation data of the equipment types of the production lines and the product data of the produced products, to perform scheduling analysis of production line production tasks based on the saturation level of the production products of the matching production lines and the degree of production abnormality of the production lines, to schedule production tasks of the production lines based on the scheduling analysis results of the production tasks, to perform a comprehensive analysis of the production data and operation data of the matching production lines of the maintenance production lines through systematic data fusion, to reasonably allocate the task volume of the maintenance production lines, thereby improving the rationality of the task volume allocation of the maintenance production lines and the safety of the completion of other production line tasks.

Description

Technical Field

[0001] The present application belongs to the field of data management systems, and specifically, is a production scheduling method and system based on smart workshops. Background Art

[0002] Smart workshop is an important trend in the development of modern industry. It is a key link in the transformation and upgrading of manufacturing industry and represents the intelligent and digital level of manufacturing industry. Smart workshop refers to a modern production workshop that applies information technology, network technology, automation technology, artificial intelligence and other advanced technologies to deeply integrate and innovate each link in the production process, and realize intelligent production management, automated production process, precise product quality and efficient resource utilization. When scheduling the production tasks of smart workshop, it is necessary to use the production scheduling method based on smart workshop. However, when one of the production lines fails, the production tasks on the production line need to be scheduled to other production lines. At this time, it is necessary to schedule the production tasks reasonably.

[0003] When the existing technology dispatches the production tasks on the faulty production line to other production lines, it usually simply dispatches the production to the production line with the lowest saturation level without considering the matching of the production line and the ability of the production line to accept tasks, which easily leads to blindly increasing the production volume of other production lines and causing damage to other production lines. Most of the existing technologies have the above problems;

[0004] In order to solve the problems raised by the background technology, the present application designs a production scheduling method and system based on a smart workshop. Summary of the invention

[0005] In order to address the deficiencies in the prior art mentioned in the background technology, the present application proposes a production scheduling method and system based on an intelligent workshop. The present application proposes to obtain matching production lines based on the equipment type operation data of the production line and the product data of the produced products, to perform scheduling analysis of production line production tasks based on the saturation level of the production products of the matching production line and the degree of production abnormality of the production line, to schedule production tasks of the production line based on the scheduling analysis results of the production tasks, to conduct a comprehensive analysis of the production data and operation data of the matching production line of the maintenance production line through systematic data fusion, to reasonably allocate the task volume of the maintenance production line, thereby improving the rationality of the task volume allocation of the maintenance production line and the safety of the completion of other production line tasks.

[0006] To achieve the above objectives, the present application provides the following technical solutions: In the first aspect, the present application provides a production scheduling method based on a smart workshop, which includes the following specific steps:

[0007] Step 1: Obtain equipment type operation data and product data of each production line in the smart workshop;

[0008] Step 2: Acquire the matching production line based on the equipment type operation data of the production line and the product data of the produced products;

[0009] Step 3: Perform scheduling analysis on production line production tasks based on the saturation level of the production products of the matching production line and the abnormality level of production of the production line;

[0010] Step 4: Schedule production tasks for the production line based on the scheduling analysis results of the production tasks.

[0011] As a preferred technical solution for a production scheduling method based on an intelligent workshop, the specific content of the data acquired in step 1 is:

[0012] Step 11, obtaining the image of the product produced by the production line to be repaired in the smart workshop and the specification data of each component, and at the same time obtaining the equipment type specification data and setting parameter data of the production line to be repaired in the smart workshop, where the product image is the image data when the product is designed, and the setting parameter data is the setting parameter data of each device when the product is produced;

[0013] Step 12: Obtain images of products produced by other production lines in the smart workshop and specification data of each component, and simultaneously obtain equipment type specification data and setting parameter data of other production lines in the smart workshop;

[0014] Step 13, obtain the operating data of each device on each production line, and store the obtained data in corresponding storage components. For example, the data of the same production line can be stored in one storage component, and the same type of data of different production lines can be stored in one storage component, which is convenient for data extraction and utilization.

[0015] As a preferred technical solution of the production scheduling method based on the intelligent workshop, the acquisition of the matching production line in step 2 includes the following specific steps:

[0016] Step 21, obtaining images of products produced by the production line to be repaired in the smart workshop and specification data of each component part, and obtaining images of products produced by other production lines in the smart workshop and specification data of each component part, and analyzing product similarity coefficients of the production line to be repaired and other production lines based on the images of products produced by the production line to be repaired in the smart workshop and specification data of each component part, and the images of products produced by other production lines in the smart workshop and specification data of each component part, wherein the product similarity coefficient is used to evaluate the product similarity between two production lines, and the product similarity coefficient calculation formula between the production line to be repaired and the i-th other production line can be contour similarity or data similarity, and the preferred contour similarity calculation formula is: , where c is the weight of image similarity, M() is the volume of the image in brackets, a is the image contour of the product of the production line to be repaired, and bi is the image contour of the product of the i-th other production line. For the intersection, is a union, N() is the number of set elements in the brackets, A is the set consisting of the specification data of the components of the production products of the production line to be repaired, Bi is the set consisting of the specification data of the components of the production products of the i-th other production line, Ki is the similarity coefficient between the production line to be repaired and the i-th other production line;

[0017] Step 22: Obtain equipment type specification data and setting parameter data of the production line to be repaired in the smart workshop, and equipment type specification data and setting parameter data of other production lines to analyze the similarity of production equipment. The similarity calculation formula between the production line to be repaired and the i-th other production line is: , where Si is the number of equipment types with the same specifications as the production line to be repaired and the i-th other production line, Pi is the total number of equipment in the production line to be repaired and the i-th other production line, xjm is the function setting parameter of the j-th equipment type of the production line to be repaired, and xij is the function setting parameter of the corresponding j-th equipment type of the i-th other production line;

[0018] Step 23, obtaining the calculated product similarity coefficient and production equipment similarity, weighting and summing them to obtain a production line similarity value, wherein the production line similarity value is used to evaluate the similarity between two production lines;

[0019] Step 24, compare the obtained production line similarity value with the set production line similarity threshold. If the production line similarity value is greater than or equal to the set production line similarity threshold, the corresponding production line is set as the matching production line of the production line to be repaired. If the production line similarity value is less than the set production line similarity threshold, the corresponding production line is not set as the matching production line of the production line to be repaired.

[0020] As a preferred technical solution of the production scheduling method based on the intelligent workshop, the saturation analysis of the production products of the matching production line in step 3 includes the following specific steps:

[0021] Step 31, obtaining the production deadline, required production volume and maximum production speed of the corresponding matching production line;

[0022] Step 32: Perform saturation analysis on the production deadline, required production volume and maximum production speed of the corresponding matching production line, wherein the saturation calculation formula of the rth matching production line is: , where Mkr is the required production volume of the r-th matching production line, Tmr is the production period of the r-th matching production line, and Vmr is the maximum production speed of the r-th matching production line.

[0023] As a preferred technical solution of the production scheduling method based on the intelligent workshop, the production abnormality degree of the matching production line in step 3 includes the following specific contents: Step 33, obtaining the operation data of various corresponding production equipment matching the production line;

[0024] Step 34: Perform production abnormality analysis on the production line by matching the operation data of various corresponding production equipment within the production line detection time. The analysis formula for the production abnormality of the rth matching production line is: , Vr is the number of production equipment of the rth matching production line, T is the detection time, fct is the operating data of the cth production equipment at time t, and fcm is the standard value of the operating data of the cth production equipment at time t.

[0025] As a preferred technical solution of the production scheduling method based on the intelligent workshop, the scheduling analysis of the production task of the production line based on the saturation degree of the production products of the matching production line and the production abnormality degree of the production line in step 3 includes the following specific contents: obtaining the saturation degree of the matching production line and the production abnormality degree of the matching production line to evaluate the actual saturation value of the matching production line, wherein the actual saturation value calculation formula of the rth matching production line is: , obtain the actual saturation values ​​of all matching production lines to perform scheduling analysis of production line production tasks, where the scheduling amount of the rth matching production line is: , where R is the number of matching production lines and D is the required allocation of production lines to be repaired.

[0026] In the second aspect, the present application provides a production scheduling system based on a smart workshop, which is implemented based on the above-mentioned production scheduling method based on a smart workshop, and specifically includes a data acquisition unit, a matching production line analysis unit, a scheduling analysis unit, a task scheduling unit and a general control module; wherein the data acquisition unit is used to obtain the equipment type operation data and product data of the production products of each production line in the smart workshop; the matching production line analysis unit acquires the matching production line based on the equipment type operation data and product data of the production products of the production line; the scheduling analysis unit performs scheduling analysis of the production line production tasks based on the saturation level of the production products of the matching production line and the degree of production abnormality of the production line; the task scheduling unit schedules the production tasks of the production line based on the scheduling analysis results of the production tasks; the general control module is used to control the operation of other units in the system.

[0027] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0028] The processor executes the above-mentioned production scheduling method based on the intelligent workshop by calling the computer program stored in the memory.

[0029] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute a production scheduling method based on a smart workshop as described above.

[0030] Compared with the prior art, the beneficial effects of the present application are as follows: the present application acquires matching production lines based on the operation data of the equipment types of the production lines and the product data of the produced products, performs scheduling analysis of production line production tasks based on the saturation level of the production products of the matching production lines and the degree of production abnormality of the production lines, schedules production tasks of the production lines based on the scheduling analysis results of the production tasks, and performs a comprehensive analysis of the production data and operation data of the matching production lines of the maintenance production lines through systematic data fusion, reasonably allocates the task volume of the maintenance production lines, thereby improving the rationality of the task volume allocation of the maintenance production lines and the safety of the completion of other production line tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings;

[0032] Figure 1 This is a schematic diagram of the overall process of a production scheduling method based on an intelligent workshop in this application;

[0033] Figure 2 This is a schematic diagram of step 2 of a production scheduling method based on a smart workshop in this application;

[0034] Figure 3 This is a schematic diagram of step three of a production scheduling method based on a smart workshop in this application;

[0035] Figure 4 This is a schematic diagram of the overall framework of a production scheduling system based on a smart workshop in this application. DETAILED DESCRIPTION

[0036] In order to better understand the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present application and do not limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0037] In the accompanying drawings, the size, dimensions and shape of the elements have been slightly adjusted for ease of explanation. The accompanying drawings are only examples and are not strictly drawn to scale. As used herein, the terms "substantially", "approximately" and similar terms are used as terms to indicate approximation, not as terms to indicate degree, and are intended to illustrate the inherent deviations in measured or calculated values ​​that will be recognized by those of ordinary skill in the art. In addition, in the present application, the order in which the steps are processed does not necessarily represent the order in which these processes occur in actual operation, unless otherwise clearly defined or can be derived from the context. It should also be understood that expressions such as "including", "including", "having", "including" and / or "including" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features, rather than just modifying the individual elements in the list. In addition, when describing the embodiments of the present application, "may" is used to represent "one or more embodiments of the present application". Furthermore, the term "exemplary" is intended to refer to an example or illustration. Unless otherwise specified, all words used herein (including engineering terms and scientific and technological terms) have the same meaning as those commonly understood by those of ordinary skill in the art to which this application belongs. It should also be understood that, unless otherwise clearly stated in this application, words defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0038] Example 1

[0039] In order to solve the technical problems raised in the background technology, the present application provides a preferred embodiment: Figure 1-Figure 3 As shown, a production scheduling method based on an intelligent workshop includes the following specific steps:

[0040] Step 1: Obtain equipment type operation data and product data of each production line in the smart workshop;

[0041] Step 2: Acquire the matching production line based on the equipment type operation data of the production line and the product data of the produced products;

[0042] Step 3: Perform scheduling analysis on production line production tasks based on the saturation level of the production products of the matching production line and the abnormality level of production of the production line;

[0043] Step 4: Schedule production tasks for the production line based on the scheduling analysis results of the production tasks.

[0044] In a specific embodiment, the specific content of acquiring data in step 1 is:

[0045] Step 11, obtaining the image of the product produced by the production line to be repaired in the smart workshop and the specification data of each component, and at the same time obtaining the equipment type specification data and setting parameter data of the production line to be repaired in the smart workshop, where the product image is the image data when the product is designed, and the setting parameter data is the setting parameter data of each device when the product is produced;

[0046] Step 12: Obtain images of products produced by other production lines in the smart workshop and specification data of each component, and simultaneously obtain equipment type specification data and setting parameter data of other production lines in the smart workshop;

[0047] Step 13, obtain the operating data of each device on each production line, and store the obtained data in corresponding storage components. For example, the data of the same production line can be stored in one storage component, and the same type of data of different production lines can be stored in one storage component, which is convenient for data extraction and utilization.

[0048] In a specific embodiment, Figure 2 As shown, the acquisition of the matching production line in step 2 includes the following specific steps:

[0049] Step 21, obtaining images of products produced by the production line to be repaired in the smart workshop and specification data of each component part, and obtaining images of products produced by other production lines in the smart workshop and specification data of each component part, and analyzing product similarity coefficients of the production line to be repaired and other production lines based on the images of products produced by the production line to be repaired in the smart workshop and specification data of each component part, and the images of products produced by other production lines in the smart workshop and specification data of each component part, wherein the product similarity coefficient is used to evaluate the product similarity between two production lines, and the product similarity coefficient calculation formula between the production line to be repaired and the i-th other production line can be contour similarity or data similarity, and the preferred contour similarity calculation formula is: , where c is the weight of image similarity, M() is the volume of the image in brackets, a is the image contour of the product of the production line to be repaired, and bi is the image contour of the product of the i-th other production line. For the intersection, is a union, N() is the number of set elements in the brackets, A is the set consisting of the specification data of the components of the production products of the production line to be repaired, Bi is the set consisting of the specification data of the components of the production products of the i-th other production line, Ki is the similarity coefficient between the production line to be repaired and the i-th other production line;

[0050] Step 22: Obtain equipment type specification data and setting parameter data of the production line to be repaired in the smart workshop, and equipment type specification data and setting parameter data of other production lines to analyze the similarity of production equipment. The similarity calculation formula between the production line to be repaired and the i-th other production line is: , where Si is the number of equipment types with the same specifications as the production line to be repaired and the i-th other production line, Pi is the total number of equipment in the production line to be repaired and the i-th other production line, xjm is the function setting parameter of the j-th equipment type of the production line to be repaired, and xij is the function setting parameter of the corresponding j-th equipment type of the i-th other production line;

[0051] Step 23, obtaining the calculated product similarity coefficient and production equipment similarity, weighting and summing them to obtain a production line similarity value, wherein the production line similarity value is used to evaluate the similarity between two production lines;

[0052] Step 24, compare the obtained production line similarity value with the set production line similarity threshold. If the production line similarity value is greater than or equal to the set production line similarity threshold, the corresponding production line is set as the matching production line of the production line to be repaired. If the production line similarity value is less than the set production line similarity threshold, the corresponding production line is not set as the matching production line of the production line to be repaired.

[0053] In a specific embodiment, Figure 3 As shown, the saturation analysis of the production products of the matching production line in step 3 includes the following specific steps:

[0054] Step 31, obtaining the production deadline, required production volume and maximum production speed of the corresponding matching production line;

[0055] Step 32: Perform saturation analysis on the production deadline, required production volume and maximum production speed of the corresponding matching production line, wherein the saturation calculation formula of the rth matching production line is: , where Mkr is the required production volume of the r-th matching production line, Tmr is the production period of the r-th matching production line, and Vmr is the maximum production speed of the r-th matching production line; the production abnormality degree of the matching production line in step 3 includes the following specific contents: Step 33, obtaining the operation data of various corresponding production equipment matching the production line;

[0056] Step 34: Perform production abnormality analysis on the production line by matching the operation data of various corresponding production equipment within the production line detection time. The analysis formula for the production abnormality of the rth matching production line is: , Vr is the number of production equipment of the rth matching production line, T is the detection time, fct is the operating data of the cth production equipment at time t, and fcm is the standard value of the operating data of the cth production equipment at time t; Step 3 Finally, the saturation degree of the matching production line and the degree of production abnormality of the matching production line are obtained to evaluate the actual saturation value of the matching production line, where the actual saturation value calculation formula of the rth matching production line is: , obtain the actual saturation values ​​of all matching production lines to perform scheduling analysis of production line production tasks, where the scheduling amount of the rth matching production line is: , where R is the number of matching production lines and D is the required allocation of production lines to be repaired.

[0057] In a specific embodiment, the specific content of step four is to allocate the task amount to each matching production line according to the calculated scheduling amount of each matching production line.

[0058] Example 2

[0059] like Figure 4 As shown, a production scheduling system based on a smart workshop is implemented based on the above-mentioned production scheduling method based on a smart workshop, and specifically includes a data acquisition unit, a matching production line analysis unit, a scheduling analysis unit, a task scheduling unit and a general control module; wherein the data acquisition unit is used to acquire the equipment type operation data and the product data of the production products of each production line in the smart workshop; the matching production line analysis unit acquires the matching production line based on the equipment type operation data and the product data of the production products of the production line; the scheduling analysis unit performs scheduling analysis of the production task of the production line based on the saturation degree of the production product of the matching production line and the degree of production abnormality of the production line; the task scheduling unit schedules the production task of the production line based on the scheduling analysis result of the production task; the general control module is used to control the operation of other units in the system; at the same time, the data transmission direction of each unit in this embodiment is as follows Figure 4 As shown by the arrow direction in the figure, the specific steps of each unit in this embodiment have been described in detail in the above method embodiment and will not be repeated here.

[0060] Example 3

[0061] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0062] The processor executes the above-mentioned production scheduling method based on the intelligent workshop by calling the computer program stored in the memory.

[0063] 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 production scheduling method based on a smart workshop provided by 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 to input and output data. This embodiment will not be described in detail here.

[0064] Example 4

[0065] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0066] When the computer program runs on a computer device, the computer device executes the above-mentioned production scheduling method based on an intelligent workshop.

[0067] 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.

[0068] 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 application 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 a computer can access 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 (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0069] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0070] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A production scheduling method based on an intelligent workshop, characterized in that: It includes the following specific steps: Step 1: Obtain equipment type operation data and product data of each production line in the smart workshop; Step 2: Acquire the matching production line based on the equipment type operation data of the production line and the product data of the produced products; Step 3: Perform scheduling analysis on production line production tasks based on the saturation level of the production products of the matching production line and the abnormality level of production of the production line; The scheduling analysis of the production line production tasks based on the saturation degree of the production products of the matching production line and the abnormality degree of the production of the production line in step 3 includes the following specific contents: The saturation degree and production abnormality degree of the matching production line are obtained to evaluate the actual saturation value of the matching production line. The actual saturation values ​​of all matching production lines are obtained to perform scheduling analysis of production tasks of the production line. The scheduling amount of the rth matching production line is: Where R is the number of matching production lines, D is the required allocation of the production line to be repaired, and Wr is the actual saturation value of the rth matching production line; Step 4: Schedule production tasks for the production line based on the scheduling analysis results of the production tasks.

2. A production scheduling method based on an intelligent workshop as claimed in claim 1, characterized in that: The acquisition of the matching production line in step 2 includes the following specific steps: Acquire images of products produced by the production line to be repaired in the smart workshop and specification data of each component part, and simultaneously acquire images of products produced by other production lines in the smart workshop and specification data of each component part, and analyze the product similarity coefficient between the production line to be repaired and other production lines based on the images of products produced by the production line to be repaired in the smart workshop and specification data of each component part, and the images of products produced by other production lines in the smart workshop and specification data of each component part; Obtain equipment type specification data and setting parameter data of the production line to be repaired in the smart workshop, and equipment type specification data and setting parameter data of other production lines to analyze the similarity of production equipment; Obtain the calculated product similarity coefficient and production equipment similarity, weight them and sum them up to obtain the production line similarity value; The obtained production line similarity value is compared with the set production line similarity threshold. If the production line similarity value is greater than or equal to the set production line similarity threshold, the corresponding production line is set as the matching production line for the production line to be repaired. If the production line similarity value is less than the set production line similarity threshold, the corresponding production line is not set as the matching production line for the production line to be repaired.

3. A production scheduling method based on an intelligent workshop as claimed in claim 1, characterized in that: The saturation analysis of the production products of the matching production line in step 3 includes the following specific steps: Obtain the production deadline, required production volume and maximum production speed of the corresponding matching production line; Conduct a saturation analysis on the production deadline, required production volume and maximum production speed of the corresponding matching production line.

4. A production scheduling method based on an intelligent workshop as claimed in claim 3, characterized in that: The production abnormality degree of the matching production line in step 3 includes the following specific contents: Obtain the operating data of various corresponding production equipment matching the production line; The degree of production abnormality of the production line is analyzed by matching the operating data of various corresponding production equipment within the production line detection time.

5. A production scheduling method based on an intelligent workshop as claimed in claim 4, characterized in that: The specific content of obtaining data in step 1 is: Obtain images of products produced by the production line to be repaired in the smart workshop and specification data of each component, and at the same time obtain equipment type specification data and setting parameter data of the production line to be repaired in the smart workshop; Obtain images of products produced by other production lines in the smart workshop and specification data of each component, and at the same time obtain equipment type specification data and setting parameter data of other production lines in the smart workshop; The operating data of each device on each production line is obtained, and the obtained data is stored in the corresponding storage components respectively.

6. A production scheduling method based on an intelligent workshop as claimed in claim 5, characterized in that: The calculation formula for the similarity between the production equipment of the production line to be repaired and the products of the i-th other production line is: Among them, Si is the number of equipment types with the same specifications between the production line to be repaired and the i-th other production line, Pi is the total number of equipment in the production line to be repaired and the i-th other production line, xjm is the function setting parameter of the j-th equipment type of the production line to be repaired, and xij is the function setting parameter of the corresponding j-th equipment type of the i-th other production line.

7. A production scheduling system based on a smart workshop, which is implemented based on the production scheduling method based on a smart workshop according to any one of claims 1 to 6, characterized in that: It specifically includes a data acquisition unit, a matching production line analysis unit, a scheduling analysis unit, a task scheduling unit and a general control module; wherein the data acquisition unit is used to acquire the equipment type operation data and product data of the production products of each production line in the smart workshop; the matching production line analysis unit acquires the matching production line based on the equipment type operation data and product data of the production products of the production line; the scheduling analysis unit performs scheduling analysis of the production line production tasks based on the saturation level of the production products of the matching production line and the degree of production abnormality of the production line; the task scheduling unit schedules the production line production tasks based on the scheduling analysis results of the production tasks; the general control module is used to control the operation of other units in the system.

8. 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 production scheduling method based on the intelligent workshop as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes a production scheduling method based on an intelligent workshop as described in any one of claims 1 to 6.

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