Intelligent cutting machine cooperative control method and system based on digital twinning

Abstract

The invention relates to the technical field of intelligent cutting machine cooperative control, in particular to an intelligent cutting machine cooperative control method and system based on digital twinning, and the method comprises the steps: obtaining the product quality data, equipment fault data and work task data of all automatic cutting machines in the same production line, and inputting the data into a digital twinning platform; the digital twinborn platform analyzes importance features, fault features and work task features of the cutting machines by constructing a digital twinborn model to obtain a first importance coefficient, a second health coefficient and a third emergency coefficient, and calculates a comprehensive cutting machine priority of each cutting machine; and finally, resource allocation is carried out based on the comprehensive cutting machine priority, so that allocable resources of each cutting machine are obtained. Based on the priority distribution satisfaction, the resource utilization rate and the production efficiency, a cutting machine objective function is constructed, and optimization is carried out through a quantum annealing algorithm; the cooperative efficiency and the resource utilization rate of all the cutting machines of the production line can be effectively improved, and the production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative control of intelligent cutting machines, and specifically to a collaborative control method and system for intelligent cutting machines based on digital twins. Background Art

[0002] With the continuous development of industrialization, the manufacturing industry is transforming towards intelligence and automation. As one of the key industrial equipment, intelligent cutting machines have been widely used in many production lines.

[0003] Traditional collaborative control methods for intelligent cutting machines have not fully considered the resource allocation problem of multiple cutting machines. Most existing methods allocate resources according to task progress and lack a comprehensive and reliable comprehensive analysis method. They have not fully considered the comprehensive optimization of resource utilization, production efficiency and resource allocation, resulting in limited collaborative control efficiency, resource utilization and production efficiency of each cutting machine on the production line, and are prone to delayed delivery.

[0004] To this end, a collaborative control method and system for an intelligent cutting machine based on digital twin is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a collaborative control method and system for intelligent cutting machines based on digital twins. The present invention relates to the technical field of collaborative control of intelligent cutting machines, including: obtaining product quality data, equipment failure data and work task data of each automatic cutting machine on the same production line, and inputting them into a digital twin platform; the digital twin platform analyzes the importance characteristics, failure characteristics and work task characteristics of the cutting machine by constructing a digital twin model, obtains a first importance coefficient, a second health coefficient and a third urgency coefficient, and calculates the comprehensive cutting machine priority of each cutting machine; finally, allocates resources based on the comprehensive cutting machine priority, thereby obtaining the allocatable resources of each cutting machine; constructs a cutting machine objective function based on priority allocation satisfaction, resource utilization and production efficiency, and optimizes it according to the quantum annealing algorithm introduced in the digital twin model; the present invention can effectively improve the collaborative efficiency and resource utilization of each cutting machine on the production line, and improve production efficiency.

[0006] To achieve the above object, the present invention provides the following technical solutions: A collaborative control method for an intelligent cutting machine based on digital twins, comprising: S1. Obtain the first multi-source cutting machine data of each automatic cutting machine of the same production line, and input the first multi-source cutting machine data into the digital twin platform; S2. The digital twin platform performs feature analysis on the first multi-source cutting machine data by constructing a digital twin model to obtain the importance feature, the fault feature and the work task feature of the cutting machine, and constructs a first importance coefficient according to the importance feature; constructs a second health coefficient according to the fault feature of the cutting machine; and constructs a third urgency coefficient according to the work task feature of the cutting machine; S3. Obtaining the comprehensive cutting machine priority of each cutting machine according to the first importance coefficient, the second health coefficient and the third urgency coefficient; S4. Allocate resources according to the comprehensive cutting machine priority to obtain the first allocatable resource for each cutting machine.

[0007] S5. Obtain first priority satisfaction feedback data, second resource utilization feedback data and third production efficiency feedback data, obtain the first priority satisfaction, second resource utilization and third production efficiency according to the first priority satisfaction feedback data, the second resource utilization feedback data and the third production efficiency feedback data, and construct the first cutting machine objective function; and optimize the importance weights of the first importance coefficient, the second health coefficient and the third urgency coefficient according to the quantum annealing algorithm introduced in the digital twin model until the first cutting machine objective function meets the preset threshold.

[0008] Preferably, the first multi-source cutting machine data includes product quality reports, equipment failure logs and work task data; the product quality data includes product quality reports; the equipment failure data includes the number of failures of each device; and the work task data includes the deadline for delivery of each order.

[0009] Preferably, the importance characteristics of the cutting machine include product qualification rate and product rework rate; the failure characteristics of the cutting machine include equipment failure frequency and equipment failure times; and the work task characteristics of the cutting machine are average remaining delivery time.

[0010] Preferably, the priority of the integrated cutting machine is: ; in, Indicates The overall cutting machine priority of each cutting machine; Indicates importance weight; Indicates The first importance coefficient of a cutting machine; represents the health weight; Indicates The second health coefficient of a cutting machine; Indicates the urgency weight; Indicates The third urgency factor of a cutting machine.

[0011] Preferably, the first allocatable resource is: ; in, Indicates The first allocatable resource of a cutting machine; Indicates The overall cutting machine priority of each cutting machine; Indicates the number of cutting machines in the same production line; Indicates the total allocatable resources; Indicates rounding down.

[0012] Preferably, the first cutting machine objective function is: ; in, represents the objective function of the first cutting machine; represents the first priority satisfaction impact coefficient; Indicates first priority satisfaction; Indicates the first priority satisfaction goal; represents the second resource utilization impact coefficient; represents the utilization rate of the second resource; represents a second resource utilization target; represents the third production efficiency influence coefficient; Indicates the third production efficiency; Represents the third production efficiency target.

[0013] A collaborative control system for an intelligent cutting machine based on digital twins, comprising: a data acquisition module, used for acquiring first multi-source cutting machine data of each automatic cutting machine of the same production line, and inputting the first multi-source cutting machine data into the digital twin platform; A model analysis module is used to perform feature analysis on the first multi-source cutting machine data by constructing a digital twin model according to the digital twin platform, obtain the importance feature of the cutting machine, the fault feature of the cutting machine and the work task feature of the cutting machine, and construct a first importance coefficient according to the importance feature; construct a second health coefficient according to the fault feature of the cutting machine; and construct a third urgency coefficient according to the work task feature of the cutting machine; A cutting machine priority analysis module, used for obtaining the comprehensive cutting machine priority of each cutting machine according to the first importance coefficient, the second health coefficient and the third urgency coefficient; A comprehensive resource allocation module, used for allocating resources according to the comprehensive cutting machine priority to obtain the first allocable resources for each cutting machine; the resources include maintenance resources, operation resources and production scheduling resources; A comprehensive optimization module is used to obtain first priority satisfaction feedback data, second resource utilization feedback data and third production efficiency feedback data, obtain the first priority satisfaction, second resource utilization and third production efficiency according to the first priority satisfaction feedback data, the second resource utilization feedback data and the third production efficiency feedback data, and construct the first cutting machine objective function; and optimize the importance weights of the first importance coefficient, the second health coefficient and the third urgency coefficient according to the quantum annealing algorithm introduced in the digital twin model until the first cutting machine objective function meets the preset threshold.

[0014] Preferably, the first multi-source cutting machine data includes product quality data, equipment failure data and work task data; the product quality data includes product qualification rate and rework rate; the equipment failure data includes the number of failures of each device; and the work task data includes the remaining delivery time of each order.

[0015] Preferably, the importance characteristics of the cutting machine include product qualification rate and product rework rate; the failure characteristics of the cutting machine include equipment failure frequency and equipment failure times; and the work task characteristics of the cutting machine are average remaining delivery time.

[0016] Preferably, the priority of the integrated cutting machine is: ; in, Indicates The overall cutting machine priority of each cutting machine; Indicates importance weight; Indicates The first importance coefficient of a cutting machine; represents the health weight; Indicates The second health coefficient of a cutting machine; Indicates the urgency weight; Indicates The third urgency factor of a cutting machine.

[0017] Preferably, the first allocatable resource is: ; in, Indicates The first allocatable resource of a cutting machine; Indicates The overall cutting machine priority of each cutting machine; Indicates the number of cutting machines in the same production line; Indicates the total allocatable resources; Indicates rounding down.

[0018] Preferably, the first cutting machine objective function is: ; in, represents the objective function of the first cutting machine; represents the first priority satisfaction impact coefficient; Indicates first priority satisfaction; Indicates the first priority satisfaction goal; represents the second resource utilization impact coefficient; represents the utilization rate of the second resource; represents a second resource utilization target; represents the third production efficiency influence coefficient; Indicates the third production efficiency; Represents the third production efficiency target.

[0019] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention obtains first multi-source cutting machine data of each automatic cutting machine on the same production line, and inputs the first multi-source cutting machine data into the digital twin platform, and analyzes the first multi-source cutting machine data by constructing a digital twin model to obtain the cutting machine importance characteristics, cutting machine fault characteristics and cutting machine work task characteristics, and obtains the first importance coefficient, the second health coefficient and the third urgency coefficient respectively. The present invention analyzes the cutting machine data through the digital twin model to effectively improve the collaborative efficiency and resource utilization of each cutting machine on the production line, ensure that the production tasks are completed on time, and improve production efficiency.

[0020] 2. The present invention obtains the comprehensive cutting machine priority of each cutting machine by comprehensively considering the first importance coefficient, the second health coefficient and the third urgency coefficient, thereby comprehensively and effectively improving the coordination efficiency and resource utilization of each cutting machine on the production line, ensuring that production tasks are completed on time and improving production efficiency.

[0021] 3. The present invention performs effective resource allocation through the comprehensive cutting machine priority of each cutting machine to obtain the first allocatable resource of each cutting machine. The coordinated control of resource allocation of multiple cutting machines is performed through the comprehensive cutting machine priority, which can effectively improve the coordinated efficiency and resource utilization of each cutting machine on the production line, ensure the production tasks are completed on time, and improve production efficiency.

[0022] 4. The present invention constructs the first cutting machine objective function by comprehensively considering the first priority allocation satisfaction, the second resource utilization rate and the third production efficiency of each cutting machine, and optimizes the importance weights of the first importance coefficient, the second health coefficient and the third urgency coefficient through the quantum annealing algorithm introduced in the digital twin model until the first cutting machine objective function meets the preset threshold; it is able to effectively improve the collaborative efficiency and resource utilization rate of each cutting machine in the production line, ensure that the production tasks are completed on time, and improve resource utilization rate and production efficiency.

[0023] 5. The present invention introduces the quantum annealing algorithm into the digital twin model; by mapping the objective function to the Ising model Hamiltonian, the quantum annealing algorithm can globally search for the optimal weight combination in the solution space, ensure that the resource allocation plan adapts to the dynamic changes of the production line, and can quickly recalculate the weights to ensure the rationality of resource allocation, thereby improving resource utilization and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic flow chart of a collaborative control method for an intelligent cutting machine based on digital twins provided in an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a collaborative control system of an intelligent cutting machine based on digital twins provided in an embodiment of the present invention; Figure 3 A schematic diagram of a process for obtaining a comprehensive cutting machine priority according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Embodiment 1 In order to improve the collaborative efficiency and resource utilization of each cutting machine in production line A, ensure that production tasks are completed on time, and improve production efficiency, a collaborative control method of intelligent cutting machines based on digital twins was applied; Reference Figure 1 , which is a flow chart of a collaborative control method of an intelligent cutting machine based on digital twins provided by an embodiment of the present invention, including: S1. Obtain the first multi-source cutting machine data of each automatic cutting machine of the same production line, and input the first multi-source cutting machine data into the digital twin platform; Furthermore, the first multi-source cutting machine data includes product quality reports, equipment failure logs and work task data; the product quality data includes product quality reports; the equipment failure data includes the number of failures of each device; and the work task data includes the deadline for delivery of each order.

[0027] S2. The digital twin platform performs feature analysis on the first multi-source cutting machine data by constructing a digital twin model to obtain the importance feature, the fault feature and the work task feature of the cutting machine, and constructs a first importance coefficient according to the importance feature; constructs a second health coefficient according to the fault feature of the cutting machine; and constructs a third urgency coefficient according to the work task feature of the cutting machine; This embodiment obtains first multi-source cutting machine data of each automatic cutting machine on the same production line, and inputs the first multi-source cutting machine data into the digital twin platform, and analyzes the first multi-source cutting machine data by constructing a digital twin model to obtain the cutting machine importance characteristics, cutting machine fault characteristics and cutting machine work task characteristics, and obtains the first importance coefficient, the second health coefficient and the third urgency coefficient respectively. The present invention analyzes the cutting machine data through the digital twin model to effectively improve the collaborative efficiency and resource utilization of each cutting machine on the production line, ensure that the production tasks are completed on time, and improve production efficiency.

[0028] Furthermore, the importance characteristics of the cutting machine include product qualification rate and product rework rate; the fault characteristics of the cutting machine include equipment fault frequency and equipment fault times; the work task characteristics of the cutting machine are average remaining delivery time; The equipment failure frequency indicates the average number of equipment failures that occur in the cutting machine of production line A every day; the number of equipment failures indicates the average number of equipment failures that occur in the cutting machine of production line A; the average remaining delivery time indicates the average of the remaining delivery time of each task of production line A; Table 1 shows the importance characteristics, fault characteristics and task characteristics of some cutting machines in production line A; Table 1 Characteristic data of some cutting machines in production line A

[0029] S3. Obtaining the comprehensive cutting machine priority of each cutting machine according to the first importance coefficient, the second health coefficient and the third urgency coefficient; Furthermore, the first importance coefficient is: ; in, Indicates The first importance coefficient of a cutting machine; Indicates Product qualification rate of each cutting machine; Indicates Product rework rate of each cutting machine; Indicates the influence weight of product qualification rate, indicating the influence of product qualification rate on the The influence of the importance of a cutting machine; Indicates the influence weight of product rework rate, indicating the influence of product rework rate on the The influence of the importance of a cutting machine; Indicates the number of cutting machines in the same production line; Furthermore, the second health coefficient is: ; in, Indicates The second health coefficient of a cutting machine; Indicates the weight of equipment failure frequency impact; Indicates Equipment failure frequency of each cutting machine; Indicates the impact weight of equipment failure times; Indicates Number of equipment failures per cutting machine; Furthermore, the third urgency coefficient is: ; in, Indicates The third urgency factor of a cutting machine; Indicates The average remaining delivery time for each cutting machine; Indicates the number of cutting machines in the same production line; Furthermore, the priority of the integrated cutting machine is: ; in, Indicates The overall cutting machine priority of each cutting machine; Indicates importance weight; Indicates The first importance coefficient of a cutting machine; represents the health weight; Indicates The second health coefficient of a cutting machine; Indicates the urgency weight; Indicates The third urgency factor of a cutting machine.

[0030] Furthermore, if Figure 3 A schematic diagram of a process for obtaining a comprehensive cutting machine priority according to an embodiment of the present invention; This embodiment obtains the comprehensive cutting machine priority of each cutting machine by comprehensively considering the first importance coefficient, the second health coefficient and the third urgency coefficient. Based on the importance characteristics, health characteristics and work task characteristics of the cutting machines, the collaborative efficiency and resource utilization of each cutting machine in the production line are comprehensively and effectively improved to ensure that production tasks are completed on time and improve production efficiency.

[0031] S4. Allocate resources according to the comprehensive cutting machine priority to obtain the first allocatable resource for each cutting machine.

[0032] Furthermore, the first allocatable resource is: ; in, Indicates The first allocatable resource of a cutting machine; Indicates The overall cutting machine priority of each cutting machine; Indicates the number of cutting machines in the same production line; Indicates the total allocatable resources; Indicates rounding down.

[0033] S5. Obtain first priority satisfaction feedback data, second resource utilization feedback data and third production efficiency feedback data, obtain the first priority satisfaction, second resource utilization and third production efficiency according to the first priority satisfaction feedback data, the second resource utilization feedback data and the third production efficiency feedback data, and construct the first cutting machine objective function; and optimize the importance weights of the first importance coefficient, the second health coefficient and the third urgency coefficient according to the quantum annealing algorithm introduced in the digital twin model until the first cutting machine objective function meets the preset threshold.

[0034] Furthermore, the first priority satisfaction feedback data is obtained by obtaining satisfaction of managers of each cutting machine, and averaging the satisfaction of managers obtained from each cutting machine to obtain the first priority satisfaction; the second resource utilization rate is obtained by the allocated resource utilization rate; the third production efficiency is obtained by the delivery cycle; The objective function of the first cutting machine is: ; in, represents the objective function of the first cutting machine; represents the first priority satisfaction impact coefficient; Indicates first priority satisfaction; Indicates the first priority satisfaction goal; represents the second resource utilization impact coefficient; represents the utilization rate of the second resource; represents a second resource utilization target; represents the third production efficiency influence coefficient; Indicates the third production efficiency; Represents the third production efficiency target.

[0035] Furthermore, the quantum annealing algorithm converts the first clipper objective function into the Ising model Hamiltonian and , and Mapped to the spin coupling coefficient of the quantum bit; solve the ground state of the Hamiltonian through the quantum annealing machine, decode and obtain the optimized importance weight, health weight and urgency weight, input the optimized weight into the digital twin platform, and update the comprehensive priority; This embodiment constructs the first cutting machine objective function by comprehensively evaluating the first priority allocation satisfaction, the second resource utilization rate and the third production efficiency of each cutting machine, and optimizes the importance weights of the first importance coefficient, the second health coefficient and the third urgency coefficient through the quantum annealing algorithm introduced in the digital twin model until the first cutting machine objective function meets the preset threshold; it is able to effectively improve the collaborative efficiency and resource utilization rate of each cutting machine on the production line, ensure that production tasks are completed on time, and improve resource utilization rate and production efficiency.

[0036] This embodiment introduces the quantum annealing algorithm into the digital twin model; by mapping the objective function to the Ising model Hamiltonian, the quantum annealing algorithm can globally search for the optimal weight combination in the solution space, ensure that the resource allocation plan adapts to the dynamic changes of the production line, and can quickly recalculate the weights to ensure the rationality of resource allocation, thereby improving resource utilization and production efficiency.

[0037] In order to verify the effectiveness of a collaborative control method for an intelligent cutting machine based on digital twins provided in this embodiment, a comparative experiment was conducted on production line A by using different methods, including method 1, method 2 and method 3; method 1 is a collaborative control method for an intelligent cutting machine based on digital twins provided in this embodiment; method 2 is based on method 1 without considering resource allocation based on the comprehensive cutting machine priority of the cutting machine; method 3 is based on method 1 without considering the optimization of priority, resource utilization and production efficiency; by comparing the production efficiency, resource utilization and priority satisfaction of production line A applied to different methods, the production efficiency specifically includes the work delivery cycle of production line A; the specific comparison results are shown in Table 2; Table 2 Comparison of production efficiency of production line A using different methods

[0038] It can be seen from Table 2 that the collaborative control method of an intelligent cutting machine based on digital twin provided in this embodiment can effectively reduce the production cycle and effectively improve production efficiency, resource utilization and priority satisfaction.

[0039] This embodiment performs effective resource allocation through the comprehensive cutting machine priority of each cutting machine to obtain the first allocatable resource of each cutting machine. By performing coordinated control of resource allocation of multiple cutting machines based on the comprehensive cutting machine priority, the coordinated efficiency and resource utilization of each cutting machine on the production line can be effectively improved, thereby ensuring that production tasks are completed on time and improving production efficiency.

[0040] This embodiment obtains product quality data, equipment failure data and work task data of each automatic cutting machine on the same production line, and inputs the data into the digital twin platform; the digital twin platform analyzes the importance characteristics, failure characteristics and work task characteristics of the cutting machine by constructing a digital twin model, obtains the first importance coefficient, the second health coefficient and the third urgency coefficient, and calculates the comprehensive cutting machine priority of each cutting machine; finally, resources are allocated based on the comprehensive cutting machine priority to obtain the allocatable resources of each cutting machine; the cutting machine objective function is constructed based on priority allocation satisfaction, resource utilization and production efficiency, and is optimized through the quantum annealing algorithm introduced in the digital twin model; the present invention can effectively improve the collaborative efficiency and resource utilization of each cutting machine on the production line, and improve production efficiency.

[0041] Embodiment 2 In order to improve the collaborative efficiency and resource utilization of each cutting machine on production line B, ensure that production tasks are completed on time, and improve production efficiency, an intelligent cutting machine collaborative control system based on digital twins was applied; Reference Figure 2 A schematic diagram of a structure of a collaborative control system of an intelligent cutting machine based on digital twins provided in an embodiment of the present invention includes: A collaborative control system for an intelligent cutting machine based on digital twins, comprising: A data acquisition module, used for acquiring first multi-source cutting machine data of each automatic cutting machine of the same production line, and inputting the first multi-source cutting machine data into the digital twin platform; Furthermore, the first multi-source cutting machine data includes product quality data, equipment failure data and work task data; the product quality data includes product qualification rate and rework rate; the equipment failure data includes the number of failures of each device; and the work task data includes the remaining delivery time of each order.

[0042] A model analysis module is used to perform feature analysis on the first multi-source cutting machine data by constructing a digital twin model according to the digital twin platform, obtain the importance feature of the cutting machine, the fault feature of the cutting machine and the work task feature of the cutting machine, and construct a first importance coefficient according to the importance feature; construct a second health coefficient according to the fault feature of the cutting machine; and construct a third urgency coefficient according to the work task feature of the cutting machine; This embodiment obtains first multi-source cutting machine data of each automatic cutting machine on the same production line, and inputs the first multi-source cutting machine data into the digital twin platform, and analyzes the first multi-source cutting machine data by constructing a digital twin model to obtain the cutting machine importance characteristics, cutting machine fault characteristics and cutting machine work task characteristics, and obtains the first importance coefficient, the second health coefficient and the third urgency coefficient respectively. The present invention analyzes the cutting machine data through the digital twin model to improve the collaborative efficiency and resource utilization of each cutting machine on the production line, ensure that the production tasks are completed on time, and improve production efficiency.

[0043] Furthermore, the importance characteristics of the cutting machine include product qualification rate and product rework rate; the failure characteristics of the cutting machine include equipment failure frequency and equipment failure times; and the work task characteristics of the cutting machine are average remaining delivery time.

[0044] The equipment failure frequency indicates the average number of equipment failures that occur in the cutting machine of production line A every day; the equipment failure number indicates the average number of equipment failures that occur in the cutting machine of production line A; the average remaining delivery time indicates the average of the remaining delivery time of each task of production line A; Table 3 shows the importance characteristics, cutting machine failure characteristics and cutting machine work task characteristic data of some cutting machines of production line B; Table 3 Characteristic data of some cutting machines in production line B

[0045] A cutting machine priority analysis module, used for obtaining the comprehensive cutting machine priority of each cutting machine according to the first importance coefficient, the second health coefficient and the third urgency coefficient; Furthermore, the first importance coefficient is: ; in, Indicates The first importance coefficient of a cutting machine; Indicates Product qualification rate of each cutting machine; Indicates Product rework rate of each cutting machine; Indicates the influence weight of product qualification rate, indicating the influence of product qualification rate on the The influence of the importance of a cutting machine; Indicates the influence weight of product rework rate, indicating the influence of product rework rate on the The influence of the importance of a cutting machine; Indicates the number of cutting machines in the same production line; Furthermore, the second health coefficient is: ; in, Indicates The second health coefficient of a cutting machine; Indicates the weight of equipment failure frequency impact; Indicates Equipment failure frequency of each cutting machine; Indicates the impact weight of equipment failure times; Indicates Number of equipment failures per cutting machine; Furthermore, the third urgency coefficient is: ; in, Indicates The third urgency factor of a cutting machine; Indicates The average remaining delivery time for each cutting machine; Indicates the number of cutting machines in the same production line; Furthermore, the priority of the integrated cutting machine is: ; in, Indicates The overall cutting machine priority of each cutting machine; Indicates importance weight; Indicates The first importance coefficient of a cutting machine; represents the health weight; Indicates The second health coefficient of a cutting machine; Indicates the urgency weight; Indicates The third urgency factor of a cutting machine.

[0046] Furthermore, if Figure 3 A schematic diagram of a process for obtaining a comprehensive cutting machine priority according to an embodiment of the present invention; This embodiment obtains the comprehensive cutting machine priority of each cutting machine by comprehensively combining the first importance coefficient, the second health coefficient and the third urgency coefficient, thereby comprehensively and effectively improving the coordination efficiency and resource utilization of each cutting machine in the production line, ensuring that production tasks are completed on time, and improving production efficiency.

[0047] The comprehensive resource allocation module is used to allocate resources according to the comprehensive cutting machine priority to obtain the first allocatable resources of each cutting machine; the resources include maintenance resources, operation resources and production scheduling resources.

[0048] Furthermore, the first allocatable resource is: ; in, Indicates The first allocatable resource of a cutting machine; Indicates The overall cutting machine priority of each cutting machine; Indicates the number of cutting machines in the same production line; Indicates the total allocatable resources; Indicates rounding down.

[0049] This embodiment performs effective resource allocation through the comprehensive cutting machine priority of each cutting machine to obtain the first allocatable resource of each cutting machine. By performing coordinated control of resource allocation of multiple cutting machines based on the comprehensive cutting machine priority, the coordinated efficiency and resource utilization of each cutting machine on the production line can be effectively improved, thereby ensuring that production tasks are completed on time and improving production efficiency.

[0050] A comprehensive optimization module is used to obtain first priority satisfaction feedback data, second resource utilization feedback data and third production efficiency feedback data, obtain the first priority satisfaction, second resource utilization and third production efficiency according to the first priority satisfaction feedback data, the second resource utilization feedback data and the third production efficiency feedback data, and construct the first cutting machine objective function; and optimize the importance weights of the first importance coefficient, the second health coefficient and the third urgency coefficient according to the quantum annealing algorithm introduced in the digital twin model until the first cutting machine objective function meets the preset threshold.

[0051] Furthermore, the first priority satisfaction feedback data is obtained by obtaining satisfaction of managers of each cutting machine, and averaging the satisfaction of managers obtained from each cutting machine to obtain the first priority satisfaction; the second resource utilization rate is obtained by the allocated resource utilization rate; the third production efficiency is obtained by the delivery cycle; Furthermore, the objective function of the first cutting machine is: ; in, represents the objective function of the first cutting machine; represents the first priority satisfaction impact coefficient; Indicates first priority satisfaction; Indicates the first priority satisfaction goal; represents the second resource utilization impact coefficient; represents the utilization rate of the second resource; represents a second resource utilization target; represents the third production efficiency influence coefficient; Indicates the third production efficiency; Represents the third production efficiency target.

[0052] Furthermore, the quantum annealing algorithm converts the first clipper objective function into the Ising model Hamiltonian and , and Mapped to the spin coupling coefficient of the quantum bit; solve the ground state of the Hamiltonian through the quantum annealing machine, decode and obtain the optimized importance weight, health weight and urgency weight, input the optimized weight into the digital twin platform, and update the comprehensive priority; This embodiment constructs the first cutting machine objective function by comprehensively evaluating the first priority allocation satisfaction, the second resource utilization rate and the third production efficiency of each cutting machine, and optimizes the importance weights of the first importance coefficient, the second health coefficient and the third urgency coefficient through the quantum annealing algorithm introduced in the digital twin model until the first cutting machine objective function meets the preset threshold; it is able to effectively improve the collaborative efficiency and resource utilization rate of each cutting machine on the production line, ensure that production tasks are completed on time, and improve resource utilization rate and production efficiency.

[0053] This embodiment introduces the quantum annealing algorithm into the digital twin model; by mapping the objective function to the Ising model Hamiltonian, the quantum annealing algorithm can globally search for the optimal weight combination in the solution space, ensure that the resource allocation plan adapts to the dynamic changes of the production line, and can quickly recalculate the weights to ensure the rationality of resource allocation, thereby improving resource utilization and production efficiency.

[0054] In order to verify the effectiveness of a collaborative control method for an intelligent cutting machine based on digital twins provided in this embodiment, a comparative experiment is conducted on production line B through different systems, including system 1, system 2, and system 3; the production efficiency, resource utilization, and priority satisfaction of different systems applied to production line B are compared, and the production efficiency specifically includes the work delivery cycle of production line B; the specific comparison results are shown in Table 4; Table 4 Comparison of production efficiency of production line B using different systems

[0055] System 1 is an intelligent cutting machine collaborative control system based on digital twin provided in this embodiment; System 2 is based on System 1 without considering resource allocation based on the comprehensive cutting machine priority of the cutting machine; System 3 is based on System 1 without considering the optimization of priority, resource utilization and production efficiency; It can be seen from Table 4 that the application of an intelligent cutting machine collaborative control system based on digital twin provided in this embodiment can effectively reduce the production cycle and effectively improve resource utilization, priority satisfaction and production efficiency.

[0056] This embodiment obtains product quality data, equipment failure data and work task data of each automatic cutting machine on the same production line, and inputs the data into the digital twin platform; the digital twin platform analyzes the importance characteristics, failure characteristics and work task characteristics of the cutting machine by constructing a digital twin model, obtains the first importance coefficient, the second health coefficient and the third urgency coefficient, and calculates the comprehensive cutting machine priority of each cutting machine; finally, resources are allocated based on the comprehensive cutting machine priority to obtain the allocatable resources of each cutting machine; the cutting machine objective function is constructed based on priority allocation satisfaction, resource utilization and production efficiency, and is optimized through the quantum annealing algorithm introduced in the digital twin model; the present invention can effectively improve the collaborative efficiency and resource utilization of each cutting machine on the production line, and improve production efficiency.

[0057] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A collaborative control method for an intelligent cutting machine based on digital twin, characterized in that: include: S1. Obtain the first multi-source cutting machine data of each automatic cutting machine of the same production line, and input the first multi-source cutting machine data into the digital twin platform; S2. The digital twin platform performs feature analysis on the first multi-source cutting machine data by constructing a digital twin model to obtain the importance feature, the fault feature and the work task feature of the cutting machine, and constructs a first importance coefficient according to the importance feature; constructs a second health coefficient according to the fault feature of the cutting machine; and constructs a third urgency coefficient according to the work task feature of the cutting machine; S3. Obtaining the comprehensive cutting machine priority of each cutting machine according to the first importance coefficient, the second health coefficient and the third urgency coefficient; S4. Allocate resources according to the integrated cutting machine priority to obtain the first allocatable resource for each cutting machine; S5. Obtain first priority satisfaction feedback data, second resource utilization feedback data and third production efficiency feedback data, obtain the first priority satisfaction, second resource utilization and third production efficiency according to the first priority satisfaction feedback data, the second resource utilization feedback data and the third production efficiency feedback data, and construct the first cutting machine objective function; and optimize the importance weights of the first importance coefficient, the second health coefficient and the third urgency coefficient according to the quantum annealing algorithm introduced in the digital twin model until the first cutting machine objective function meets the preset threshold.

2. According to claim 1, a collaborative control method of an intelligent cutting machine based on digital twinning is characterized in that: The first multi-source cutting machine data includes product quality reports, equipment failure logs and work task data; the product quality data includes product quality reports; the equipment failure data includes the number of failures of each device; and the work task data includes the deadline for delivery of each order.

3. According to claim 2, a collaborative control method of an intelligent cutting machine based on digital twinning is characterized in that: The importance characteristics of the cutting machine include product qualification rate and product rework rate; the failure characteristics of the cutting machine include equipment failure frequency and equipment failure times; the work task characteristics of the cutting machine are the average remaining delivery time.

4. According to claim 3, a collaborative control method for an intelligent cutting machine based on digital twinning is characterized in that: The integrated cutting machine priorities are: ； in, Indicates The overall cutting machine priority of each cutting machine; Indicates importance weight; Indicates The first importance coefficient of a cutting machine; represents the health weight; Indicates The second health coefficient of a cutting machine; Indicates the urgency weight; Indicates The third urgency factor of a cutting machine.

5. According to claim 4, a collaborative control method of an intelligent cutting machine based on digital twinning is characterized in that: The first allocatable resource is: ； in, Indicates The first allocatable resource of a cutting machine; Indicates The overall cutting machine priority of each cutting machine; Indicates the number of cutting machines in the same production line; Indicates the total allocatable resources; Indicates rounding down.

6. The collaborative control method of an intelligent cutting machine based on digital twin according to claim 5 is characterized in that: The objective function of the first cutting machine is: ； in, represents the objective function of the first cutting machine; represents the first priority satisfaction impact coefficient; Indicates first priority satisfaction; Indicates the first priority satisfaction goal; represents the second resource utilization impact coefficient; represents the utilization rate of the second resource; represents a second resource utilization target; represents the third production efficiency influence coefficient; Indicates the third production efficiency; Represents the third production efficiency target.

7. A collaborative control system for an intelligent cutting machine based on digital twins, characterized in that: include: A data acquisition module, used for acquiring first multi-source cutting machine data of each automatic cutting machine of the same production line, and inputting the first multi-source cutting machine data into the digital twin platform; A model analysis module is used to perform feature analysis on the first multi-source cutting machine data by constructing a digital twin model according to the digital twin platform, obtain the importance feature of the cutting machine, the fault feature of the cutting machine and the work task feature of the cutting machine, and construct a first importance coefficient according to the importance feature; construct a second health coefficient according to the fault feature of the cutting machine; and construct a third urgency coefficient according to the work task feature of the cutting machine; A cutting machine priority analysis module, used for obtaining the comprehensive cutting machine priority of each cutting machine according to the first importance coefficient, the second health coefficient and the third urgency coefficient; A comprehensive resource allocation module, used for allocating resources according to the comprehensive cutting machine priority to obtain the first allocable resources for each cutting machine; the resources include maintenance resources, operation resources and production scheduling resources; A comprehensive optimization module is used to obtain first priority satisfaction feedback data, second resource utilization feedback data and third production efficiency feedback data, obtain the first priority satisfaction, second resource utilization and third production efficiency according to the first priority satisfaction feedback data, the second resource utilization feedback data and the third production efficiency feedback data, and construct the first cutting machine objective function; and optimize the importance weights of the first importance coefficient, the second health coefficient and the third urgency coefficient according to the quantum annealing algorithm introduced in the digital twin model until the first cutting machine objective function meets the preset threshold.

8. According to claim 7, a collaborative control system for an intelligent cutting machine based on digital twinning is characterized in that: The first multi-source cutting machine data includes product quality data, equipment failure data and work task data; the product quality data includes product qualification rate and rework rate; the equipment failure data includes the number of failures of each equipment; the work task data includes the remaining delivery time of each order; the cutting machine importance characteristics include product qualification rate and product rework rate; the cutting machine failure characteristics include equipment failure frequency and equipment failure number; the cutting machine work task characteristics are the average remaining delivery time.

9. The collaborative control system of an intelligent cutting machine based on digital twin according to claim 7, characterized in that: The integrated cutting machine priorities are: ； in, Indicates The overall cutting machine priority of each cutting machine; Indicates importance weight; Indicates The first importance coefficient of a cutting machine; represents the health weight; Indicates The second health coefficient of a cutting machine; Indicates the urgency weight; Indicates The third urgency factor of a cutting machine; The first allocatable resource is: ； in, Indicates The first allocatable resource of a cutting machine; Indicates The overall cutting machine priority of each cutting machine; Indicates the number of cutting machines in the same production line; Indicates the total allocatable resources; Indicates rounding down.

10. The collaborative control system of an intelligent cutting machine based on digital twin according to claim 7, characterized in that: The objective function of the first cutting machine is: ； in, represents the objective function of the first cutting machine; represents the first priority satisfaction impact coefficient; Indicates first priority satisfaction; Indicates the first priority satisfaction goal; represents the second resource utilization impact coefficient; represents the utilization rate of the second resource; represents a second resource utilization target; represents the third production efficiency influence coefficient; Indicates the third production efficiency; Represents the third production efficiency target.

Images