Digital production process management and optimization platform based on industrial internet

Abstract

The invention relates to the technical field of industrial internet, and discloses a digital production process management and optimization platform based on the industrial internet, and the platform comprises a dynamic data calculation module, a constraint model construction module, a production task distribution module, a task monitoring feedback module and a deviation iteration correction module. Compared with a production management method which mainly depends on a static task allocation rule and a single optimization algorithm in the prior art, the production management method has the advantages that in a distributed manufacturing scene, under the conditions of complex priorities of long-production business orders, dynamic logistics network changes and heterogeneous production capacity of sub-factories, the production management efficiency is greatly improved; according to the technical scheme of the invention, the real-time optimization capability of the production task in the distributed manufacturing scene is remarkably improved, and the problems of resource waste and delivery delay caused by static planning are avoided. And the dynamic adaptability of task allocation, the utilization rate of production resources and the overall production efficiency are improved.

Description

Technical Field

[0001] The present invention belongs to the field of industrial Internet technology, and in particular relates to a digital production process management and optimization platform based on the industrial Internet. Background Art

[0002] Currently, production task management and optimization methods in distributed manufacturing scenarios lack dynamic adaptability. For example, existing technologies mostly rely on static task allocation rules and single optimization algorithms, and are unable to respond in real time to the complex priorities of long-term business orders, dynamic changes in logistics networks, and the changing demands of heterogeneous production capacity of sub-factories. This leads to resource waste, production delays, and the inability to effectively reduce delivery time. Existing technologies cannot fully meet the needs of dynamic, multi-objective, and real-time optimized production task management. Therefore, there is an urgent need for a digital production process management and optimization platform in distributed manufacturing scenarios that can achieve multi-objective real-time optimization and closed-loop feedback control based on the priorities of long-term business orders, production capacity, and dynamic data of logistics networks, so as to improve the dynamic adaptability of task allocation, resource utilization efficiency, and overall production delivery efficiency. Summary of the Invention

[0003] In response to the above-mentioned technical deficiencies, the purpose of the present invention is to propose a digital production process management and optimization platform based on the industrial Internet, aiming to solve the production management method in the existing technology that mainly relies on static task allocation rules and a single optimization algorithm, especially in distributed manufacturing scenarios, facing the complex priorities of long-term business orders, dynamic logistics network changes and heterogeneous production capabilities of sub-factories, and the technical problem of being unable to achieve multi-objective real-time dynamic optimization and closed-loop feedback control.

[0004] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a digital production process management and optimization platform based on the Industrial Internet, comprising:

[0005] Dynamic data calculation module, used to obtain production business data in real time through microservice architecture, including the economic value of production business W value , current traffic congestion data C traffic , Production business delivery deadline T deadline and the current time T current , and dynamically calculate production business priority data P order ; Collect production capacity data of corresponding sub-factories of production business, including equipment utilization rate U device , current load W load and device health status H device , and dynamically calculate the effective production capacity C of the sub-factory effective ; Get the logistics path distance D log and real-time transport speed V log, and dynamically calculate the logistics time L corresponding to the production business log ;

[0006] A constraint model construction module, which is used to construct the constraint conditions for the production business task allocation according to the production business priority data P order , the effective production capacity C of the sub-factory effective and the logistics time L log including the sub-factory production capacity constraint, the logistics time constraint and the sub-factory resource balance constraint, and dynamically adjust the production business priority data according to the constraint conditions of the production business task allocation;

[0007] A production task allocation module, which is used to take the adjusted production business priority data as the input of the multi-objective optimization algorithm, and generate an optimal production task allocation matrix in combination with the constraint conditions of the production business task allocation;

[0008] A task monitoring and feedback module, which is used to monitor the task execution status of the sub-factory in real time, including the actual completion time T of the task actual and the expected production time T prod , calculate the execution deviation ΔT exec ;

[0009] A deviation iterative correction module, which is used to judge that if the execution deviation ΔT exec is greater than the preset execution deviation threshold, trigger a feedback mechanism to adjust the production business priority data P order , and take the adjusted production business priority data P order as the input of the new multi-objective optimization algorithm.

[0010] Preferably, in the dynamic data acquisition module, the calculation formula of the production business priority data P order is where α is the traffic impact weight coefficient; the calculation formula of the effective production capacity C of the sub-factory effective is where W max is the maximum load preset for the sub-factory; the calculation formula of the logistics time L log is

[0011] Preferably, in the constraint model construction module, the sub-factory production capacity constraint is used to ensure that the task allocation of each sub-factory does not exceed its dynamic production capacity, and the constraint formula is:

[0012]

[0013] where T prod,i,j is the production time of task j on sub-factory i; C effective,iis the effective production capacity of sub-factory i, and m is the total number of current tasks.

[0014] Preferably, in the constraint model construction module, the logistics time constraint is used to ensure that the delivery time of each task is within the specified range, and the constraint formula is:

[0015]

[0016] Among them, T total,j is the total delivery time of task j; T prod,j is the production time of task j; L log,j is the logistics time of task j.

[0017] Preferably, in the constraint model construction module, the formula for the sub-factory resource balance constraint is:

[0018]

[0019] Among them, T prod,k,j is the production time of task j on sub-factory k; C effective,k is the effective production capacity of sub-factory k; ∈ is the preset load balance error threshold.

[0020] Preferably, in the production task allocation module, the optimal production task allocation matrix is generated by using a multi-objective optimization algorithm based on the constraint conditions of production business task allocation, specifically including:

[0021] Take the production business priority data P order , the effective production capacity C of the sub-factory effective and the logistics time L log as the input of the multi-objective optimization algorithm, and construct the initial task allocation matrix A;

[0022] Introduce three corresponding sub-objective function weight coefficients w1, w2, and w3, and construct the multi-objective function S by combining the sub-factory production capacity constraint, the logistics time constraint, and the sub-factory resource balance constraint;

[0023] Use the multi-objective optimization algorithm to dynamically solve the multi-objective function S and output the optimal production task allocation matrix.

[0024] Preferably, the multi-objective optimization algorithm used to dynamically solve the multi-objective function S in the multi-objective optimization algorithm includes the genetic algorithm and the particle swarm algorithm.

[0025] The beneficial effects of the present invention are as follows: Compared with the production management methods in the prior art that mainly rely on static task allocation rules and single optimization algorithms, especially in the distributed manufacturing scenario, in the face of the complex priorities of long industrial business orders, the changes in the dynamic logistics network, and the heterogeneous production capabilities of sub-factories, the technical problem of being unable to achieve multi-objective real-time dynamic optimization and closed-loop feedback control, this application significantly improves the real-time optimization ability of production tasks in the distributed manufacturing scenario, avoids the problems of resource waste and delivery delay caused by static planning, and improves the dynamic adaptability of task allocation, the utilization rate of production resources, and the overall production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0027] Figure 1 It is a schematic diagram of the platform of the first embodiment of a digital production process management and optimization platform based on the industrial Internet provided by the present invention.

[0028] Figure 2 It is a schematic diagram of the equipment of a digital production process management and optimization platform based on the industrial Internet provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0030] Embodiment 1: As Figure 1 shown, it is a schematic diagram of the platform of the first embodiment of the digital production process management and optimization platform based on the industrial Internet of the present invention, and the first embodiment of the digital production process management and optimization platform based on the industrial Internet of the present invention is proposed.

[0031] In the first embodiment, the digital production process management and optimization platform based on the industrial Internet includes:

[0032] A dynamic data calculation module, which is used to obtain production business data in real time through a microservice architecture, including the economic value W of production business value , and the current traffic congestion data C traffic, the production business delivery deadline T deadline and the current time T current , and dynamically calculate the production business priority data P order ; collect the production capacity data of the corresponding sub-factory of the production business, including the equipment utilization rate U device , the current load W load and the equipment health status H device , and dynamically calculate the effective production capacity C of the sub-factory effective ; obtain the logistics path distance D log and the real-time transportation speed V log , and dynamically calculate the corresponding logistics time L of the production business log ;

[0033] It should be noted that in the dynamic data calculation module, the calculation formula for the production business priority data P order is where α is the traffic impact weight coefficient; the calculation formula for the effective production capacity C of the sub-factory effective is where W max is the preset maximum load of the sub-factory; the calculation formula for the logistics time L log is

[0034] It should be understood that in order to better achieve the dynamic allocation and overall optimization of production tasks, this module obtains and calculates the production business priority data, the effective production capacity of the sub-factory, and the logistics time in real time through the dynamic data calculation module. By dynamically adjusting the order priority score, factors such as delivery time, order value, and logistics traffic conditions are comprehensively considered to ensure that high-priority tasks are allocated resources first; by calculating the effective production capacity of the sub-factory, the equipment utilization rate, the current load, and the equipment health status are comprehensively considered to avoid tasks concentrating on high-load or low-efficiency sub-factories, thereby optimizing resource utilization; by calculating the logistics time, the real-time traffic conditions and transportation capacity of the logistics path are dynamically reflected, providing basic support for the on-time delivery of tasks; the data calculation results can respond to the dynamic changes of the environment and orders in real time, providing efficient support for subsequent task allocation and optimization, significantly reducing resource waste and delivery delays caused by static planning. This module lays a data foundation for the real-time optimization of the entire production process, and at the same time realizes efficient decision-making support under dynamic change conditions, greatly improving the dynamic adaptability of task allocation and the system operation efficiency.

[0035] The constraint model construction module is used to, according to the production business priority data P order , the effective production capacity C of the sub-factory effective and the logistics time L logConstruct the constraint conditions for production business task allocation, including the production capacity constraint of sub-factories, the logistics time constraint, and the resource balance constraint of sub-factories, and dynamically adjust the production business priority data according to the constraint conditions of the production business task allocation;

[0036] It should be noted that in the constraint model construction module, the production capacity constraint of sub-factories is used to ensure that the task allocation of each sub-factory does not exceed its dynamic production capacity, and the constraint formula is:

[0037]

[0038] where T prod,i,j is the production time of task j on sub-factory i; C effective,i is the effective production capacity of sub-factory i, and m is the total number of current tasks.

[0039] In the constraint model construction module, the formula for the resource balance constraint of sub-factories is:

[0040]

[0041] where T prod,k,j is the production time of task j on sub-factory k; C effective,k is the effective production capacity of sub-factory k; ∈ is the preset load balance error threshold. In the constraint model construction module, the logistics time constraint is used to ensure that the delivery time of each task is within the specified range, and the constraint formula is:

[0042]

[0043] where T total,j is the total delivery time of task j; T prod,j is the production time of task j; L log,j is the logistics time of task j.

[0044] It should be understood that in order to better achieve the global optimization of task allocation, this module can significantly improve the accuracy and dynamic adaptability of the task allocation scheme by constructing production capacity constraints, logistics time constraints, and resource balance constraints. The production capacity constraint of sub-factories avoids the problem of further overloading of high-load sub-factories and ensures that tasks can be completed efficiently; the logistics time constraint optimizes the task allocation path under dynamic traffic conditions and reduces the risk of delivery failure caused by logistics delays; the resource balance constraint ensures the balance of task allocation among sub-factories and avoids problems of resource concentration or idleness.

[0045] The production task allocation module is used to take the adjusted production business priority data as the input of the multi-objective optimization algorithm and generate the optimal production task allocation matrix in combination with the constraint conditions of the production business task allocation;

[0046] It should be noted that in the production task allocation module, the optimal production task allocation matrix is generated by using a multi-objective optimization algorithm based on the constraint conditions of production business task allocation, specifically including:

[0047] Taking the production business priority data P order , the effective production capacity C of the sub-factory effective and the logistics time L log as the input of the multi-objective optimization algorithm, and constructing the initial task allocation matrix A;

[0048] Introducing three corresponding sub-objective function weight coefficients w1, w2 and w3, and constructing the multi-objective function S by combining the sub-factory production capacity constraint, the logistics time constraint and the sub-factory resource balance constraint;

[0049] Using the multi-objective optimization algorithm to dynamically solve the multi-objective function S, and output the optimal production task allocation matrix.

[0050] It should be understood that in order to better realize the dynamic optimization of production tasks, this module realizes the minimization of the total delivery time, the equalization of the utilization of sub-factory resources and the comprehensive optimization of production and logistics costs through the objective function, avoiding the local optimum problem caused by a single objective; using the sub-factory production capacity constraint, the logistics time constraint and the resource balance constraint to ensure that the generated task allocation scheme is not only optimal, but also can be executed in actual production.

[0051] For example, the objective optimization function S is where m is the total number of production tasks to be allocated currently, such as each order, process task or production process; n is the total number of sub-factories participating in task allocation, such as all factories in a distributed manufacturing system; the optimization objective is to minimize the total delivery time, equalize the sub-factory resource utilization rate and reduce production and logistics costs; the task allocation matrix A = [a i,j , a i,j = 1 means that task j is allocated to sub-factory i; a i,j = 0 means that task j is not allocated to sub-factory i; the dimension of matrix A is n×m, representing the allocation relationship between all tasks and sub-factories;

[0052] For example, the order data is P order,1 = 30, P order,2 = 20, P order,3 = 25, and the due delivery times are 48 hours, 50 hours and 46 hours respectively; the sub-factory data is: sub-factory A: C effective，A = 0.8, C prod,A = 50; sub-factory B: C effective,B = 0.6, C prod,B = 45; the logistics data is: the path length D log,1= 100, real-time speed V log,1 = 50; Weight settings w1 = 0.5, w2 = 0.3, w3 = 0.2; Dynamically solve the objective function S and output the task allocation matrix The result indicates that task 1 is assigned to sub-factory A, and tasks 2 and 3 are assigned to sub-factory B.

[0053] Task monitoring feedback module, used to monitor the task execution status of the sub-factory in real time, including the actual completion time T of the task actual and the expected production time T prod , calculate the execution deviation ΔT exec ;

[0054] It should be noted that when ΔT exec > 0, it indicates that the task is delayed and a feedback mechanism needs to be triggered for adjustment; when ΔT exec ≤ 0, it indicates that the task is completed on time or in advance and does not affect subsequent tasks.

[0055] It should be understood that in order to better ensure the timely completion of production tasks, this module accurately locates the execution deviation by collecting the actual completion time of the task in real time and comparing it with the expected production time. When the task is delayed, the priority score is dynamically adjusted to give priority to high-importance tasks to ensure the timely completion of key tasks.

[0056] Deviation iteration correction module, used to judge if the execution deviation ΔT exec is greater than the preset execution deviation threshold, trigger the feedback mechanism to adjust the production business priority data P order , and use the adjusted production business priority data P order as the input of the new multi-objective optimization algorithm.

[0057] It should be understood that in order to better improve the dynamic adaptability of the production system and the task optimization effect, this module can adjust the priority in a timely manner through deviation correction when a task delay occurs, avoiding the further accumulation of delayed tasks affecting the production chain; the adjusted priority data enters the next round of optimization calculation, forming a complete closed-loop from monitoring, feedback to optimization, ensuring the real-time and accuracy of task allocation.

[0058] For example, the expected production time T of task 1 prod,1 = 6 hours, the actual completion time T actual,1 = 8 hours; the deviation threshold is ΔT threshold = 1 hour; the priority score P of the current task order,1 = 25; the deadline T deadline = 24 hours, the current time T current = 10 hours; calculate the execution deviation ΔT exec = Tactual,1 -T prod,1 = 8 - 6 = 2; Since ΔT exec > ΔT threshold , trigger the feedback mechanism, adjust the production priority, and calculate the adjusted priority score P order,adjust , Substitute the value into the adjusted priority score to get The adjusted priority score is increased to P order,adjust = 26.78.

[0059] In addition, the present invention also provides a digital production process management and optimization device based on the industrial Internet. Please refer to Figure 2, A digital production process management and optimization device based on the industrial Internet includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute a digital production process management and optimization platform in Embodiment 1 above. A digital production process management and optimization device in an embodiment of the present invention may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description: tablet computers), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A digital production process management and optimization device based on the industrial Internet is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention. A digital production process management and optimization device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of a digital production process management and optimization device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following platforms may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow a digital production process management and optimization device to communicate with other devices wirelessly or wirelessly to exchange data. Although a digital production process management and optimization device is shown in the figure, it should be understood that it is not required to implement or have all the shown platforms. Instead, more or fewer platforms may be implemented or had.

[0060] The present invention also provides a computer program product, including a computer program, which when executed by a processor, implements a digital production process management and optimization platform based on the industrial Internet as described above. The computer program product provided by the present invention can solve the technical problem of digital production process management and optimization based on the industrial Internet. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as those of the digital production process management and optimization platform based on the industrial Internet provided by the above embodiments, and will not be elaborated herein.

[0061] Specifically, according to the embodiments disclosed by the present invention, the processes described above with reference to the platform diagrams can be implemented as computer software programs. For example, the embodiments disclosed by the present invention include a computer program product, which includes a computer program carried on a computer-readable medium. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by a processing device 1001, the above functions defined in the platform of the embodiments disclosed by the present invention are executed.

[0062] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0063] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A digital production process management and optimization platform based on the industrial Internet, characterized in that the platform Including: A dynamic data calculation module for obtaining production business data in real time through a microservices architecture, including the production business economic value W value , the current traffic congestion data C traffic , the production business delivery deadline T deadline and the current time T current , and dynamically calculating the production business priority data P order ; collecting the production capacity data of the corresponding sub-factory of the production business, including the equipment utilization rate U device , the current load W load and the equipment health status H device , and dynamically calculating the effective production capacity C of the sub-factory effective ; obtaining the logistics path distance D log and the real-time transportation speed V log , and dynamically calculating the corresponding logistics time L of the production business log ; A constraint model construction module, which is used to construct the constraint conditions for the production business task assignment according to the production business priority data P order , the effective production capacity C of the sub-factory effective and the logistics time L log to construct the constraint conditions for the production business task assignment, including the sub-factory production capacity constraint, the logistics time constraint and the sub-factory resource balance constraint, and dynamically adjust the production business priority data according to the constraint conditions of the production business task assignment; A production task allocation module, which is used to take the adjusted production business priority data as the input of a multi-objective optimization algorithm, and generate an optimal production task allocation matrix in combination with the constraint conditions of production business task allocation; Task monitoring feedback module, used to monitor the execution status of sub-factory tasks in real time, including the actual completion time T of the tasks actual and the estimated production time T prod , and calculate the execution deviation ΔT exec ; The deviation iteration correction module is used to determine if the execution deviation ΔT exec is greater than the preset execution deviation threshold, trigger the feedback mechanism to adjust the production business priority data P order , and use the adjusted production business priority data P order as the input of the new multi-objective optimization algorithm.

2. The digital production process management and optimization platform based on industrial Internet according to claim 1, characterized in that In the dynamic data calculation module, the production business priority data P order has the following calculation formula where α is the traffic impact weight coefficient; the effective production capacity C of the sub-factory effective has the following calculation formula where W max is the maximum load preset for the sub-factory; the logistics time L log has the following calculation formula 3. A digital production process management and optimization platform based on the industrial Internet as claimed in claim 1, characterized in that In the constraint model construction module, the production capacity constraint of the sub-factory is used to ensure that the task allocation of each sub-factory does not exceed its dynamic production capacity, and the constraint formula is: Among them, T prod,i,j is the production time of task j in sub-factory i; C effective,i is the effective production capacity of sub-factory i, and m is the total number of current tasks.

4. The digital production process management and optimization platform based on industrial Internet according to claim 3, characterized in that In the constraint model construction module, the logistics time constraint is used to ensure that the delivery time of each task is within the specified range, and the constraint formula is: Among them, T total,j is the total delivery time of task j; T prod,j is the production time of task j; L log,j is the logistics time of task j.

5. The digital production process management and optimization platform based on industrial Internet according to claim 4, characterized in that In the constraint model construction module, the formula for the resource balance constraint of the sub-factory is: Among them, T prod,k,j is the production time of task j in sub-factory k; C effective,k is the effective production capacity of sub-factory k; ∈ is the preset load balancing error threshold.

6. A digital production process management and optimization platform based on the industrial Internet as claimed in claim 1, wherein, In the production task allocation module, an optimal production task allocation matrix is generated by using a multi-objective optimization algorithm based on the constraint conditions of production business task allocation, specifically including: Take the production business priority data P order , the effective production capacity C of the sub-factory effective and the logistics time L log as the input of the multi-objective optimization algorithm, and construct the initial task assignment matrix A; Introducing three corresponding sub-objective function weight coefficients w1, w2, and w3, and constructing a multi-objective function S in combination with the production capacity constraint of the sub-factory, the logistics time constraint, and the resource balance constraint of the sub-factory; Dynamically solving the multi-objective function S by using a multi-objective optimization algorithm, and outputting an optimal production task allocation matrix.

7. A digital production process management and optimization platform based on industrial Internet as claimed in claim 6, characterized in that, The multi-objective optimization algorithm used to dynamically solve the multi-objective function S in the multi-objective optimization algorithm includes a genetic algorithm and a particle swarm algorithm.

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