CN116466673B - Manufacturing system, manufacturing method, manufacturing equipment and storage medium based on artificial intelligence - Google Patents
Manufacturing system, manufacturing method, manufacturing equipment and storage medium based on artificial intelligence Download PDFInfo
- Publication number
- CN116466673B CN116466673B CN202310721360.XA CN202310721360A CN116466673B CN 116466673 B CN116466673 B CN 116466673B CN 202310721360 A CN202310721360 A CN 202310721360A CN 116466673 B CN116466673 B CN 116466673B
- Authority
- CN
- China
- Prior art keywords
- production
- digital twin
- module
- scheme
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 412
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 39
- 238000003860 storage Methods 0.000 title claims abstract description 13
- 238000012544 monitoring process Methods 0.000 claims abstract description 46
- 239000002994 raw material Substances 0.000 claims abstract description 30
- 230000008859 change Effects 0.000 claims abstract description 15
- 238000010276 construction Methods 0.000 claims abstract description 9
- 238000004590 computer program Methods 0.000 claims description 16
- 238000004458 analytical method Methods 0.000 claims description 13
- 238000004140 cleaning Methods 0.000 claims description 9
- 238000007781 pre-processing Methods 0.000 claims description 5
- 238000007405 data analysis Methods 0.000 claims description 3
- 238000000034 method Methods 0.000 description 17
- 238000005516 engineering process Methods 0.000 description 9
- 238000005457 optimization Methods 0.000 description 9
- 238000004891 communication Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000011960 computer-aided design Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 239000002699 waste material Substances 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 238000003326 Quality management system Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000013523 data management Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000004886 process control Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000001737 promoting effect Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000013480 data collection Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- General Factory Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a manufacturing system, a manufacturing method, equipment and a storage medium based on artificial intelligence, which relate to the technical field of intelligent manufacturing and are used for solving the problem of low production efficiency caused by production by experience and rules in the traditional manufacturing mode, and comprise the following steps: the model construction module is used for constructing a digital twin model corresponding to the actual production line; a solution determination module for determining a production solution based on the digital twin model; the instruction generation module is used for generating a production instruction corresponding to the production scheme; the execution control module is used for controlling the operation of the production equipment in the digital twin model and the input of raw materials according to the production instruction; the monitoring module is used for monitoring the change of the data of the digital twin model in the production process; and the optimizing module is used for optimizing the production scheme based on the monitoring result until the target production scheme applied to the actual production line is determined. The invention continuously improves the production efficiency and quality by continuously optimizing the production scheme, and reduces the production cost.
Description
Technical Field
The present invention relates to the field of intelligent manufacturing technologies, and in particular, to an artificial intelligence based manufacturing system, manufacturing method, apparatus, and storage medium.
Background
With the development of artificial intelligence and digital twin technology, intelligent manufacturing has gradually become a trend of manufacturing industry, and traditional manufacturing modes often produce through experience and rules, so that production efficiency is low, and production quality and efficiency are difficult to ensure.
Disclosure of Invention
In view of the above, an object of the present invention is to provide an artificial intelligence-based manufacturing system, manufacturing method, apparatus and storage medium, which can improve production efficiency and quality and reduce production cost. The specific scheme is as follows:
in a first aspect, the present invention discloses an artificial intelligence based manufacturing system comprising:
the model construction module is used for constructing a digital twin model corresponding to the actual production line;
a solution determination module for determining a corresponding production solution based on the digital twin model;
the instruction generation module is used for generating a production instruction corresponding to the production scheme;
the execution control module is used for controlling the operation of the production equipment in the digital twin model and the input of raw materials according to the production instruction;
the monitoring module is used for monitoring the data change of the digital twin model in the production process to obtain a corresponding monitoring result;
and the optimizing module is used for optimizing the production scheme based on the monitoring result, and feeding the optimized production scheme back to the instruction generating module so as to regenerate a new production instruction corresponding to the optimized production scheme, so that the execution control module controls the operation of production equipment and the input of raw materials in the digital twin model according to the new production instruction until the target production scheme applied to the actual production line is determined.
Optionally, the manufacturing system based on artificial intelligence further comprises:
the data acquisition module is used for acquiring data in the actual production process.
Optionally, the manufacturing system based on artificial intelligence further comprises:
and the data cleaning module is used for preprocessing and cleaning the acquired data in the actual production process to obtain processed data.
Optionally, the model building module specifically includes:
the model construction unit is used for constructing a digital twin model corresponding to the actual production line in a preset data modeling mode.
Optionally, the manufacturing system based on artificial intelligence further comprises:
and the model driving module is used for driving the digital twin model by using the processed data so as to construct a digital twin scene corresponding to the actual production process.
Optionally, the scheme determining module specifically includes:
the data analysis unit is used for analyzing the data in the digital twin model to obtain a corresponding analysis result;
and the scheme determining unit is used for determining corresponding production schemes and production parameters according to the analysis result.
Optionally, the instruction generating module is specifically configured to generate a production instruction corresponding to the production scheme according to the production scheme and the production parameter.
In a second aspect, the invention discloses an artificial intelligence based manufacturing method comprising:
constructing a digital twin model corresponding to an actual production line;
determining a corresponding production scheme based on the digital twin model;
generating a production instruction corresponding to the production scheme;
controlling the operation of production equipment and the input of raw materials in the digital twin model according to the production instruction;
monitoring the data change of the digital twin model in the production process to obtain a corresponding monitoring result;
and optimizing the production scheme based on the monitoring result, and re-executing the step of generating the production instruction corresponding to the production scheme and the subsequent steps based on the optimized production scheme until the target production scheme applied to the actual production line is determined.
In a third aspect, the present invention discloses an electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the artificial intelligence based manufacturing method disclosed previously.
In a fourth aspect, the present invention discloses a computer-readable storage medium for storing a computer program; wherein the computer program when executed by a processor performs the steps of the artificial intelligence based manufacturing method disclosed previously.
It can be seen that the present invention provides an artificial intelligence based manufacturing system comprising: the model construction module is used for constructing a digital twin model corresponding to the actual production line; a solution determination module for determining a corresponding production solution based on the digital twin model; the instruction generation module is used for generating a production instruction corresponding to the production scheme; the execution control module is used for controlling the operation of the production equipment in the digital twin model and the input of raw materials according to the production instruction; the monitoring module is used for monitoring the data change of the digital twin model in the production process to obtain a corresponding monitoring result; and the optimizing module is used for optimizing the production scheme based on the monitoring result, and feeding the optimized production scheme back to the instruction generating module so as to regenerate a new production instruction corresponding to the optimized production scheme, so that the execution control module controls the operation of production equipment and the input of raw materials in the digital twin model according to the new production instruction until the target production scheme applied to the actual production line is determined. Therefore, the invention can carry out intelligent decision according to the constructed digital twin model corresponding to the actual production line to continuously optimize the production flow, improve the production efficiency and quality, analyze and decide the digital twin model to prepare the optimal production scheme and parameters, further improve the production efficiency and quality, automatically control the production process in the digital twin scene through the execution control module, reduce the production cost, reduce human errors and avoid wasting resources, namely, the invention can continuously improve the production efficiency and quality, reduce the production cost and improve the competitiveness of enterprises through continuously optimizing the production scheme.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an artificial intelligence based manufacturing system according to the present disclosure;
FIG. 2 is a flow chart of an artificial intelligence based manufacturing method in accordance with the present disclosure;
FIG. 3 is a flow chart of a specific artificial intelligence based manufacturing method of the present disclosure;
FIG. 4 is a flow chart of a specific artificial intelligence based manufacturing method of the present disclosure;
fig. 5 is a block diagram of an electronic device according to the present disclosure.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
At present, the traditional manufacturing mode is often produced through experience and rules, the production efficiency is low, and the production quality and the production efficiency are difficult to ensure. Therefore, the invention provides a manufacturing system based on artificial intelligence, which can improve the production efficiency and quality and reduce the production cost.
The embodiment of the invention also discloses a manufacturing system based on artificial intelligence, which is shown in fig. 1, and comprises the following steps:
the model construction module 11 is used for constructing a digital twin model corresponding to an actual production line.
It will be appreciated that the model construction module 11 models the actual production line and constructs a digital twin model corresponding to the actual production line.
In a specific embodiment, the model building module 11 may specifically include:
the model construction unit is used for constructing a digital twin model corresponding to the actual production line through a preset data modeling technology.
It may be appreciated that the preset modeling techniques may include, but are not limited to, a picture modeling technique, a two-dimensional drawing modeling technique, a three-dimensional drawing modeling technique, or the like, and perform one-to-one three-dimensional model re-engraving according to the site of the actual production line, so that the actual production line is the same as the digital twin model, for example, CAD (Computer Aided Design ) data, BIM (Building Information Modeling, building information model) data, based on factory buildings and production lines provided by factories, and the like, and acquired video and picture data shot on the site, and the actual factory or the actual production line is restored in the form of a model in the virtual environment by using the data through the preset modeling techniques to obtain the corresponding digital twin model.
Further, the manufacturing system based on artificial intelligence may specifically further include:
the data acquisition module is used for acquiring data in the actual production process.
And the data cleaning module is used for preprocessing and cleaning the acquired data in the actual production process to obtain processed data.
It is understood that data in the actual production process is collected, including relevant information about production equipment, raw materials, workers on the current production line, etc., and specifically, the production process data may include, but is not limited to, production equipment data, raw material data, manual information data, third party video data, security data, logistics data, quality data, etc. And the real-time data generated in the actual production process can be directly collected into the three-dimensional engine, after the data in the actual production process is collected, the collected data in the actual production process can be preprocessed and subjected to data cleaning processing in the data management platform of the digital twin system so as to remove noise and abnormal values, so that the cleaned real-time data can be matched onto a mapping model in a virtual environment, namely, the processed data is combined with an algorithm model matched with the actual business of a manufacturer, and the processed production data is analyzed and applied, thereby realizing the rapid application of the data and meeting the requirements of digital twin on the real-time property of the data. For example, data in an actual production process is collected via an OPC (Object Linking and Embedding for Process Control, object connection and embedding) data collection protocol for process control. For example, data is accessed into a digital twin system by interfacing with SACDA (Supervisory Control And Data Acquisition, data acquisition and monitoring control system), MES ((Manufacturing Execution System, manufacturing execution system), IOT (Internet of Things ), QMS (Quality Management System, quality management system), ERP (Enterprise Resource Planning ), CRM (Customer Relationship Management, customer relationship management), DMS (Database Management System ), etc. to effect data acquisition.
Further, the manufacturing system based on artificial intelligence may specifically further include:
and the model driving module is used for driving the digital twin model by using the processed data so as to construct a digital twin scene corresponding to the actual production process.
It can be understood that the filtered useful data is applied, for example, the data such as the processed device operation and personnel positioning are applied to the digital twin model through the MQTT (Message Queuing Telemetry Transport, message queue telemetry transmission) protocol, so that the device state for driving the virtual scene is synchronous with the real device state, and the digital twin scene building is realized.
A solution determination module 12 for determining a corresponding production solution based on the digital twin model.
It can be understood that the model building module 11 builds a digital twin model corresponding to the actual production line, and drives the digital twin model, so that the current digital twin scene is consistent with the production process of the actual production line, and further the operation condition of each production device in the digital twin model is analyzed, and further the production scheme which can be executed in the current digital twin model is determined.
The instruction generating module 13 is configured to generate a production instruction corresponding to the production scheme.
In this embodiment, after the solution determining module 12 determines the corresponding production solution, the instruction generating module 13 generates a production instruction corresponding to the production solution.
An execution control module 14 is used for controlling the operation of the production equipment and the input of raw materials in the digital twin model according to the production instruction.
In this embodiment, according to the generated production instruction corresponding to the generation scheme, the operation of the production equipment and the input of raw materials in the digital twin model are controlled, so as to simulate the actual production scene in the virtual environment.
And the monitoring module 15 is used for monitoring the data change of the digital twin model in the production process to obtain a corresponding monitoring result.
In this embodiment, the execution control module 14 monitors the changes of the related data of the digital twin model in real time during the operation of the production equipment and the input of the raw materials in the digital twin model according to the production instruction, and obtains the corresponding monitoring result. And the optimizing module 16 is configured to optimize the production scheme based on the monitoring result, and feed back the optimized production scheme to the instruction generating module to regenerate a new production instruction corresponding to the optimized production scheme, so that the execution control module controls the operation of the production equipment and the input of raw materials in the digital twin model according to the new production instruction until the target production scheme applied to the actual production line is determined.
In this embodiment, the production scheme is continuously optimized based on the monitoring result, that is, the optimization module 16 optimizes the production scheme according to the monitoring result, and then feeds back the optimized scheme to the instruction generation module 13, when the instruction generation module 13 receives the optimized production scheme fed back by the optimization model 16, a new production instruction corresponding to the optimized production scheme is generated again, the execution control module 14 controls the operation of the production equipment and the input of raw materials in the current digital twin scene of the digital twin model according to the new production instruction, the monitoring module 15 monitors the production process of the digital twin model in real time, the optimization module 16 further optimizes the new production scheme according to the monitoring result, and the above processes are continuously repeated in the digital twin model, so as to realize the continuous optimization of the production scheme until the target production scheme which meets the preset condition and is applied to the actual production line is obtained, thereby realizing the cost reduction and efficiency improvement of the factory.
In a specific embodiment, the scheme determining module 12 may specifically include:
the data analysis unit is used for analyzing the data in the digital twin model to obtain a corresponding analysis result;
and the scheme determining unit is used for determining corresponding production schemes and production parameters according to the analysis result.
In this embodiment, data in the digital twin model including information on the state of the apparatus, the quality of raw materials, etc. is analyzed, and then, an optimal production scheme and parameters, such as a production flow, a production speed, etc., are determined based on the analysis result.
In a specific embodiment, the instruction generating module 13 is specifically configured to generate a production instruction corresponding to the production scheme according to the production scheme and the production parameter.
In this embodiment, corresponding instructions are generated according to a production scheme and parameters, so that in a subsequent operation, the operation of the production equipment and the input of raw materials are controlled in a digital twin scene according to the instructions, a real production scene is simulated in a virtual environment, the data change in the production process is monitored, continuous optimization is performed on the production scheme based on the monitoring result, namely, the operation of the production equipment and the input of raw materials are controlled in the digital twin scene according to the generation instructions corresponding to the current production scheme, the real production scene is simulated in the virtual environment, then the data change of the digital twin model in the production process is monitored in real time, the optimization module optimizes the production scheme according to the monitoring result, the instruction generation module receives the optimized production scheme fed back by the optimization model, generates a new production instruction corresponding to the optimized production scheme, the execution control module then controls the operation of the production equipment and the input of the raw materials in the current digital twin scene of the digital twin model according to the new production instruction, the new production scheme is optimized by the optimization module, the continuous optimization of the production scheme is continuously repeated in the digital twin scene until the target of the actual production line meeting preset conditions is obtained, and the actual production scheme is reduced. It should be noted that the above technical solution of the present invention can be widely applied to various manufacturing fields, such as automobile manufacturing, electronic manufacturing, and aviation manufacturing. Meanwhile, the invention can provide a more scientific and sustainable solution for intelligent manufacturing and makes positive contribution to promoting the transformation and upgrading of manufacturing industry.
From the above, in the embodiment of the invention, intelligent decision is made according to the constructed digital twin model corresponding to the actual production line to continuously optimize the production flow, so that the production efficiency and quality are improved, the digital twin model is analyzed and decided to make the optimal production scheme and parameters, the production efficiency and quality are further improved, the production process in the digital twin scene is automatically controlled by the execution control module, the production cost is reduced, and human errors are reduced, so that the resource waste is avoided.
Correspondingly, the embodiment of the invention discloses a manufacturing method based on artificial intelligence, and referring to fig. 2, the method comprises the following steps:
step S11: and constructing a digital twin model corresponding to the actual production line.
In this embodiment, a digital twin model corresponding to the actual production line is constructed by a digital twin technology, that is, the actual production line is restored in a model form in a virtual environment. For example, based on CAD and BIM data such as factory buildings and production lines provided by factories and acquired video and picture data shot on site, a real factory or production line is restored in a virtual environment in a model form to obtain a digital twin model corresponding to the real production line.
Step S12: a corresponding production scheme is determined based on the digital twinning model.
In this embodiment, a digital twin model corresponding to an actual production line is constructed, and the digital twin model is driven, so that a current digital twin scene is consistent with a production process of the actual production line, and further, the operation condition of each production device in the digital twin model is analyzed, and further, a production scheme which can be executed in the current digital twin model is determined.
Step S13: and generating a production instruction corresponding to the production scheme.
In this embodiment, after determining the current production scheme, a production instruction corresponding to the production scheme is generated.
Step S14: and controlling the operation of production equipment and the input of raw materials in the digital twin model according to the production instruction.
In this embodiment, according to the generated production instruction corresponding to the production scheme, the operation of the production equipment and the input of raw materials in the digital twin model are controlled, so as to simulate a real production scene in a virtual environment.
Step S15: and monitoring the data change of the digital twin model in the production process to obtain a corresponding monitoring result.
In this embodiment, when the operation of the production equipment in the digital twin model and the input of the raw materials are controlled according to the production instruction, the change of the related data of the digital twin model in the production process is monitored in real time at the same time, so as to obtain a corresponding monitoring result.
Step S16: and optimizing the production scheme based on the monitoring result, and re-executing the step of generating the production instruction corresponding to the production scheme and the subsequent steps based on the optimized production scheme until the target production scheme applied to the actual production line is determined.
In this embodiment, the data change in the production process is monitored, the digital twin model and the production scheme are continuously optimized based on the monitoring result, that is, the operation of the production equipment and the input of raw materials are controlled in the digital twin scene according to the generation instruction corresponding to the current production scheme, the real production scene is simulated in the virtual environment, then the data change of the digital twin model in the production process is monitored in real time, the production scheme is optimized according to the monitoring result, then the new production instruction corresponding to the optimized production scheme is regenerated based on the optimized production scheme, the operation of the production equipment and the input of the raw materials are controlled according to the new production instruction, the production process of the digital twin model is monitored in real time, the new production scheme is optimized according to the new monitoring result, and the processes are repeated in the digital twin model until the target production scheme applied to the actual production line meeting the preset condition is obtained, and the cost reduction and efficiency of the factory are realized. It should be noted that the above technical solution of the present invention can be widely applied to various manufacturing fields, such as automobile manufacturing, electronic manufacturing, and aviation manufacturing. Meanwhile, the invention can provide a more scientific and sustainable solution for intelligent manufacturing and makes positive contribution to promoting the transformation and upgrading of manufacturing industry.
Therefore, in the embodiment of the invention, intelligent decision is made according to the constructed digital twin model corresponding to the actual production line so as to continuously optimize the production flow, so that the production efficiency and quality are improved, the digital twin model is analyzed and decided so as to make an optimal production scheme and parameters, the production efficiency and quality are further improved, the production process in the digital twin scene is controlled by instructions, the automatic control of production is realized, the production cost is reduced, human errors are reduced, and resource waste is avoided.
Referring to fig. 3, an embodiment of the present invention discloses a specific manufacturing method based on artificial intelligence, and compared with the previous embodiment, the present embodiment further describes and optimizes the technical solution.
Step S21: and collecting data in the actual production process.
In this embodiment, data in an actual production process is collected, including relevant information of production equipment, raw materials, workers on a current production line, and the like, and specifically, the production process data may include, but is not limited to, production equipment data, raw material data, manual information data, third party video data, security data, logistics data, quality data, and the like.
Step S22: and preprocessing and cleaning the acquired data in the actual production process to obtain processed data.
In this embodiment, data in an actual production process is collected, and then preprocessing and data cleaning processing are performed on a data management platform of a digital twin system to remove noise and abnormal values, so as to obtain processed data, so that the processed data is matched with a mapping model in a virtual environment in a subsequent manner, that is, the processed data is combined with an algorithm model matched with actual business of a manufacturer, and the processed production data is analyzed and applied, so that rapid application of the data can be realized.
Step S24: and constructing a digital twin model corresponding to an actual production line in a preset data modeling mode, and driving the digital twin model by using the processed data to construct a digital twin scene corresponding to the actual production process.
It may be understood that the preset modeling technology may include, but is not limited to, a picture modeling technology, a two-dimensional drawing modeling technology, a three-dimensional drawing modeling technology, or the like, and performing one-to-one three-dimensional model re-engraving according to the site of the actual production line, so that the actual production line is the same as the digital twin model, for example, based on CAD data such as a factory building and a production line provided by a factory, BIM data, and acquired video and picture data shot on the site, and the actual factory or the actual production line is restored in a model form in a virtual environment by using the data through the preset modeling technology to obtain the corresponding digital twin model. And then, the processed data are applied, for example, the data such as the processed equipment operation, personnel positioning and the like are applied to a digital twin model through an MQTT protocol, and the device state used for driving the virtual scene is synchronous with the real device state, so that the digital twin scene is built.
Step S25: a corresponding production scheme is determined based on the digital twinning model.
Step S26: and generating a production instruction corresponding to the production scheme.
Step S27: and controlling the operation of production equipment and the input of raw materials in the digital twin model according to the production instruction.
Step S28: and monitoring the data change of the digital twin model in the production process to obtain a corresponding monitoring result.
Step S29: and optimizing the production scheme based on the monitoring result, and re-executing the step of generating the production instruction corresponding to the production scheme and the subsequent steps based on the optimized production scheme until the target production scheme applied to the actual production line is determined.
For the specific content of the above steps S25 to S29, reference may be made to the corresponding content disclosed in the foregoing embodiment, and a detailed description is omitted herein.
Therefore, in the embodiment of the invention, intelligent decision is made according to the constructed digital twin model corresponding to the actual production line so as to continuously optimize the production flow, so that the production efficiency and quality are improved, the digital twin model is analyzed and decided so as to make an optimal production scheme and parameters, the production efficiency and quality are further improved, the production process in the digital twin scene is controlled by instructions, the automatic control of production is realized, the production cost is reduced, human errors are reduced, and resource waste is avoided.
Referring to fig. 4, an embodiment of the present invention discloses a specific manufacturing method based on artificial intelligence, and compared with the previous embodiment, the present embodiment further describes and optimizes the technical solution.
Step S31: and constructing a digital twin model corresponding to the actual production line.
Step S32: and analyzing the data in the digital twin model to obtain a corresponding analysis result.
In this embodiment, the analysis result is obtained by analyzing the data in the digital twin model, and the data includes information such as the equipment state and the quality of the raw material.
Step S33: and determining corresponding production schemes and production parameters according to the analysis results.
In this embodiment, the data in the digital twin model is analyzed to obtain a corresponding analysis result, and then an optimal production scheme and parameters are determined according to the analysis result, i.e. the current optimal production flow, production speed, etc. are determined.
Step S34: and generating a production instruction corresponding to the production scheme according to the production scheme and the production parameters.
In this embodiment, after determining a corresponding production scheme and production parameters according to the analysis result, a production instruction corresponding to the production scheme is generated according to the production scheme and the production parameters.
Step S35: and controlling the operation of production equipment and the input of raw materials in the digital twin model according to the production instruction.
Step S36: and monitoring the data change of the digital twin model in the production process to obtain a corresponding monitoring result.
Step S37: and optimizing the production scheme based on the monitoring result, and re-executing the step of generating the production instruction corresponding to the production scheme and the subsequent steps based on the optimized production scheme until the target production scheme applied to the actual production line is determined.
For the specific content of the step S31 and the steps S35 to S37, reference may be made to the corresponding content disclosed in the foregoing embodiment, and no detailed description is given here.
Therefore, in the embodiment of the invention, intelligent decision is made according to the constructed digital twin model corresponding to the actual production line so as to continuously optimize the production flow, so that the production efficiency and quality are improved, the digital twin model is analyzed and decided so as to make an optimal production scheme and parameters, the production efficiency and quality are further improved, the production process in the digital twin scene is controlled by instructions, the automatic control of production is realized, the production cost is reduced, human errors are reduced, and resource waste is avoided.
Further, the embodiment of the invention also provides electronic equipment. Fig. 5 is a block diagram of an electronic device 20, according to an exemplary embodiment, and is not intended to limit the scope of use of the present invention in any way.
Fig. 5 is a schematic structural diagram of an electronic device 20 according to an embodiment of the present invention. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. Wherein the memory 22 is adapted to store a computer program that is loaded and executed by the processor 21 to implement the relevant steps of the artificial intelligence based manufacturing method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in the present embodiment may be specifically an electronic computer.
In this embodiment, the power supply 23 is configured to provide an operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and the communication protocol to be followed is any communication protocol applicable to the technical solution of the present invention, which is not specifically limited herein; the input/output interface 25 is used for acquiring external input data or outputting external output data, and the specific interface type thereof may be selected according to the specific application requirement, which is not limited herein.
The memory 22 may be a carrier for storing resources, such as a read-only memory, a random access memory, a magnetic disk, or an optical disk, and the resources stored thereon may include an operating system 221, a computer program 222, and the like, and the storage may be temporary storage or permanent storage.
The operating system 221 is used for managing and controlling various hardware devices on the electronic device 20 and computer programs 222, which may be Windows Server, netware, unix, linux, etc. The computer program 222 may further include a computer program that can be used to perform other specific tasks in addition to the computer program that can be used to perform the artificial intelligence based manufacturing method performed by the electronic device 20 as disclosed in any of the previous embodiments.
Further, the embodiment of the invention also discloses a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and when the computer program is loaded and executed by a processor, the manufacturing method steps based on artificial intelligence disclosed in any embodiment are realized.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description of the manufacturing system, the manufacturing method, the device and the storage medium based on artificial intelligence provided by the invention has been presented in detail, and specific examples are applied to illustrate the principles and the implementation of the invention, and the above examples are only used to help understand the method and the core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (10)
1. An artificial intelligence based manufacturing system, comprising:
the model construction module is used for constructing a digital twin model corresponding to the actual production line;
a solution determination module for determining a corresponding production solution based on the digital twin model;
the instruction generation module is used for generating a production instruction corresponding to the production scheme;
the execution control module is used for controlling the operation of the production equipment in the digital twin model and the input of raw materials according to the production instruction;
the monitoring module is used for monitoring the data change of the digital twin model in the production process to obtain a corresponding monitoring result;
and the optimizing module is used for optimizing the production scheme based on the monitoring result, and feeding the optimized production scheme back to the instruction generating module so as to regenerate a new production instruction corresponding to the optimized production scheme, so that the execution control module controls the operation of production equipment and the input of raw materials in the digital twin model according to the new production instruction until the target production scheme applied to the actual production line is determined.
2. The artificial intelligence based manufacturing system of claim 1, further comprising:
the data acquisition module is used for acquiring data in the actual production process.
3. The artificial intelligence based manufacturing system of claim 2, further comprising:
and the data cleaning module is used for preprocessing and cleaning the acquired data in the actual production process to obtain processed data.
4. The artificial intelligence based manufacturing system of claim 1, wherein the model building module specifically comprises:
the model construction unit is used for constructing a digital twin model corresponding to the actual production line in a preset data modeling mode.
5. The artificial intelligence based manufacturing system of claim 3, further comprising:
and the model driving module is used for driving the digital twin model by using the processed data so as to construct a digital twin scene corresponding to the actual production process.
6. The artificial intelligence based manufacturing system of any one of claims 1 to 5, wherein the solution determination module specifically comprises:
the data analysis unit is used for analyzing the data in the digital twin model to obtain a corresponding analysis result;
and the scheme determining unit is used for determining corresponding production schemes and production parameters according to the analysis result.
7. The artificial intelligence based manufacturing system of claim 6, wherein the instruction generation module is configured to generate the production instruction corresponding to the production recipe based on the production recipe and the production parameter.
8. A method of manufacturing based on artificial intelligence, comprising:
constructing a digital twin model corresponding to an actual production line;
determining a corresponding production scheme based on the digital twin model;
generating a production instruction corresponding to the production scheme;
controlling the operation of production equipment and the input of raw materials in the digital twin model according to the production instruction;
monitoring the data change of the digital twin model in the production process to obtain a corresponding monitoring result;
and optimizing the production scheme based on the monitoring result, and re-executing the step of generating the production instruction corresponding to the production scheme and the subsequent steps based on the optimized production scheme until the target production scheme applied to the actual production line is determined.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to perform the steps of the artificial intelligence based manufacturing method as claimed in claim 8.
10. A computer-readable storage medium storing a computer program; wherein the computer program when executed by a processor performs the steps of the artificial intelligence based manufacturing method according to claim 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310721360.XA CN116466673B (en) | 2023-06-19 | 2023-06-19 | Manufacturing system, manufacturing method, manufacturing equipment and storage medium based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310721360.XA CN116466673B (en) | 2023-06-19 | 2023-06-19 | Manufacturing system, manufacturing method, manufacturing equipment and storage medium based on artificial intelligence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116466673A CN116466673A (en) | 2023-07-21 |
| CN116466673B true CN116466673B (en) | 2023-09-12 |
Family
ID=87179215
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310721360.XA Active CN116466673B (en) | 2023-06-19 | 2023-06-19 | Manufacturing system, manufacturing method, manufacturing equipment and storage medium based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116466673B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111857065A (en) * | 2020-06-08 | 2020-10-30 | 北京邮电大学 | Intelligent production system and method based on edge calculation and digital twinning |
| CN113031549A (en) * | 2021-03-04 | 2021-06-25 | 常州信息职业技术学院 | Industrial internet intelligent manufacturing solution and system based on digital twin |
| CN113110283A (en) * | 2021-04-29 | 2021-07-13 | 武汉纺织大学 | MPS system intelligent control method and system based on digital twin |
| CN114435897A (en) * | 2022-04-11 | 2022-05-06 | 山东捷瑞数字科技股份有限公司 | Conveyor belt self-adaptive speed optimization method and system based on digital twinning technology |
| CN115062478A (en) * | 2022-06-23 | 2022-09-16 | 珠海市长陆工业自动控制系统股份有限公司 | Dynamic workshop production scheduling method, system and medium based on digital twin |
| CN115114856A (en) * | 2022-07-01 | 2022-09-27 | 哈工大机器人（合肥）国际创新研究院 | Intelligent manufacturing production line operation system based on digital twin |
| CN115826438A (en) * | 2022-12-23 | 2023-03-21 | 北京途为科技有限公司 | Industrial simulation method and system based on digital twinning |
| CN116187729A (en) * | 2023-05-04 | 2023-05-30 | 山东捷瑞数字科技股份有限公司 | Resource scheduling method, device, equipment and storage medium based on digital twin |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10928809B2 (en) * | 2018-12-04 | 2021-02-23 | General Electric Company | As-designed, as-manufactured, as-tested, as-operated and as-serviced coupled digital twin ecosystem |
-
2023
- 2023-06-19 CN CN202310721360.XA patent/CN116466673B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111857065A (en) * | 2020-06-08 | 2020-10-30 | 北京邮电大学 | Intelligent production system and method based on edge calculation and digital twinning |
| CN113031549A (en) * | 2021-03-04 | 2021-06-25 | 常州信息职业技术学院 | Industrial internet intelligent manufacturing solution and system based on digital twin |
| CN113110283A (en) * | 2021-04-29 | 2021-07-13 | 武汉纺织大学 | MPS system intelligent control method and system based on digital twin |
| CN114435897A (en) * | 2022-04-11 | 2022-05-06 | 山东捷瑞数字科技股份有限公司 | Conveyor belt self-adaptive speed optimization method and system based on digital twinning technology |
| CN115062478A (en) * | 2022-06-23 | 2022-09-16 | 珠海市长陆工业自动控制系统股份有限公司 | Dynamic workshop production scheduling method, system and medium based on digital twin |
| CN115114856A (en) * | 2022-07-01 | 2022-09-27 | 哈工大机器人（合肥）国际创新研究院 | Intelligent manufacturing production line operation system based on digital twin |
| CN115826438A (en) * | 2022-12-23 | 2023-03-21 | 北京途为科技有限公司 | Industrial simulation method and system based on digital twinning |
| CN116187729A (en) * | 2023-05-04 | 2023-05-30 | 山东捷瑞数字科技股份有限公司 | Resource scheduling method, device, equipment and storage medium based on digital twin |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116466673A (en) | 2023-07-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8000946B2 (en) | Discrete event simulation with constraint based scheduling analysis | |
| CN109634733A (en) | Task scheduling and managing method, device and operation management server | |
| WO2023151523A1 (en) | Deep learning programming method and system based on digital twin daas platform | |
| CN111240935B (en) | Automatic intelligent operation and maintenance system and operation and maintenance method | |
| CN110333895B (en) | Automatic operation and maintenance platform for electric power regulation cloud | |
| CN110601883A (en) | Cloud configuration method, system, storage medium and terminal of edge gateway | |
| EP3286700B1 (en) | Templates in a multidisciplinary engineering system | |
| CN114926143A (en) | Method and platform for configuring enterprise workflow based on business components and process engine | |
| CN116466673B (en) | Manufacturing system, manufacturing method, manufacturing equipment and storage medium based on artificial intelligence | |
| CN112200491A (en) | Digital twin model construction method and device and storage medium | |
| CN112565404A (en) | Data processing method, edge server, center server and medium | |
| CN113656897A (en) | Intelligent design method, system and device for central air conditioner | |
| CN109740766B (en) | Industrial equipment maintenance service planning method | |
| CN113508343A (en) | Method and device for updating data of industrial model | |
| CN114327839B (en) | Task optimization method and system | |
| CN113609563B (en) | Template building method, system, equipment and storage medium based on BIM technology | |
| CN116167935A (en) | Repairing method, device, equipment and medium for two-dimensional code | |
| CN116044867A (en) | Hydraulic system control method, system, equipment and medium based on automatic programming | |
| US20190080259A1 (en) | Method of learning robust regression models from limited training data | |
| Franceschini et al. | Decentralized approach for efficient simulation of DEVS models | |
| JP3027420B2 (en) | Project management equipment | |
| Hein et al. | Managing variability with configuration techniques | |
| CN117428760B (en) | Joint module control system and method based on artificial intelligence | |
| WO2023142069A1 (en) | Software architecture method and software architecture | |
| CN118036248A (en) | Supply chain production line simulation method, device and equipment based on digital twin model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |