CN116300736A - MES-based digital factory twin management system, platform and method - Google Patents

Abstract

The invention discloses a twin management system, a twin management platform and a twin management method of a digital factory based on MES, which comprise a database, a database and a database, wherein the database is used for storing and updating historical operation data of workshops and equipment thereof; the data acquisition module is used for acquiring real-time operation data of workshops and equipment of the factory; and the digital twin module is used for constructing a digital twin model corresponding to each workshop and equipment thereof, mapping the operation states of the workshops and equipment to the corresponding digital twin model according to the operation data of each workshop and equipment thereof, and reversely simulating the operation states of the workshops and equipment in response to the operation instructions. Compared with the prior art, the invention can enable the manager to know various running conditions of the factory in real time without going to the site, thereby realizing real-time monitoring of the production and processing process.

Description

MES-based digital factory twin management system, platform and method

technical field

The invention relates to the technical field of digital twins, in particular to an MES-based digital factory twin management system, platform and method.

Background technique

In the entire 3C industry, due to the wide variety of assets of enterprises, they mainly include production, office, park and so on. In some enterprises, the total value of existing assets mainly including production equipment and various tooling and fixtures is as high as hundreds of millions of yuan, which makes the asset management of enterprises extremely difficult and requires a lot of manpower and material resources to invest in management work. However, the energy of managers is limited, and it is inevitable that there will be mistakes in the management process. At the same time, through personnel management, it is impossible to accurately capture and monitor the parameters of assets during operation, and the management lacks timeliness and effectiveness. Moreover, personnel management cannot accurately predict and avoid potential risks that may exist during asset operation.

At present, in view of the difficulty, lack of timeliness and effectiveness of enterprise asset management, management accounts or management systems are usually used to manage enterprise assets. However, since the management accounts are usually obtained offline, management has certain lag, still unable to manage in a timely and effective manner. Although the management system can monitor the operating status of assets in a timely manner to a certain extent, it usually monitors in the form of data in the management process, which is relatively abstract; for data management, due to the large number of equipment and complexity, the data is large and the data structure is different. , the monitoring is not perfect, and the data is difficult to manage; for the spatial dimension, there are many factories and workshops with wide distribution, large spans, scattered personnel and equipment, and the timeliness of handling emergencies is not high; for equipment maintenance, it is difficult to share operating conditions in real time, and equipment Insufficient operation status prediction and maintenance capabilities lead to over-maintenance or under-maintenance of equipment, reducing the service life of equipment and wasting unnecessary human resources. When a fault occurs, maintenance personnel often arrive at the scene after the fault occurs, and the data cannot be real Restoring the scene where the fault occurred caused the maintenance personnel to be unable to fully grasp the fault situation, and thus unable to respond and repair quickly, resulting in a delay in the time to restore normal operation, seriously affecting the production rhythm of the enterprise, and causing downtime losses.

Therefore, there is an urgent need to invent a system that can map the physical entities of the enterprise into the virtual space to manage the assets of the enterprise in real time and efficiently.

Contents of the invention

In view of this, the purpose of the present invention is to provide an MES-based digital factory twin management system, platform and method to solve the problems of lagging enterprise asset management, lack of timeliness in management and inability to truly reflect the entire process of asset operation in the prior art The problem.

In order to solve the above technical problems, a technical solution of the present invention provides an MES-based digital factory twin management system, including:

The database is used to store and update the historical operation data of each workshop and its equipment;

The data collection module is used to collect the real-time operation data of each workshop and its equipment in the factory; and

The digital twin module is used to build a digital twin model corresponding to each workshop and its equipment, and map the operating status of the workshop and equipment to the corresponding digital twin model according to the operating data of each workshop and its equipment, and respond to the reverse operation instruction Simulate the operating status of workshops and equipment.

Further, the operation data includes at least personnel data, production data and status data; the data collection module includes:

The personnel data collection sub-module is used to collect the personnel data of each workshop, wherein the personnel data includes at least the number of on-duty personnel and the personnel information of each on-duty personnel and their real-time location and on-duty time;

The production data collection sub-module is used to collect the production data of each workshop and its equipment, wherein the production data at least includes the total output of the workshop and its equipment, the unit output, the quantity of good products and the quantity of consumables; and

The status data collection sub-module is used to collect status data of each workshop and its equipment, wherein the status data includes at least status parameters and operating parameters of the workshop and its equipment.

Further, the digital twin module includes:

The model construction sub-module is used to construct the mechanism model and data model of the workshop and its equipment and associate the mechanism model with the data model to obtain the digital twin model of the factory;

The data analysis and processing sub-module is used to make statistics on the operation data of each workshop and its equipment and perform comprehensive calculation on the statistical operation data to obtain the operation status of each workshop and its equipment; and

The data two-way transmission sub-module is used to map the operation data and operation state to the digital twin model and receive operation instructions to reversely control the digital twin model for real-time simulation.

Further, the model building submodule includes:

The mechanism model building unit is used to obtain the existing physical drawings of each workshop and equipment in the factory or draw physical drawings based on the physical entities of the workshops and equipment, perform 3D modeling on the factory based on the physical drawings, and construct the mechanism model of the factory;

The data model building unit is used to obtain the historical operation data and real-time operation data of the factory, convert the historical operation data and real-time operation data into the action data of each workshop and equipment in the factory, and construct the data model of the factory; and

The model association unit is used to associate the data model with the mechanism model, map the action data of the data model to the mechanism model, and construct a digital twin model of the factory.

Further, the data analysis and processing sub-module is also used to make statistics on the personnel data, production data and status data, and calculate according to the number of on-duty personnel, the personnel information of each on-duty personnel and their real-time position and on-duty time And analyze the real-time operation situation of the personnel in each workshop and equipment in the factory, calculate and analyze the production efficiency, yield rate and operating efficiency of each workshop and its equipment based on the total output, unit output, good product quantity and consumable quantity of the workshop and its equipment rate, and calculate and analyze the operating conditions and abnormal information of the workshop and its equipment based on the state parameters and operating parameters.

Further, a visualization module is also included;

The visualization module is used to visualize the digital twin model, form a twin management scene, and perform twin management on the production and processing processes of each workshop and its equipment in the factory.

Further, it also includes an early warning module;

The early warning module is used to comprehensively judge the real-time operation data according to the historical operation data combined with the production efficiency, yield rate, utilization rate, operation status and abnormal information calculated and analyzed by the data analysis and processing sub-module, and When the above real-time operation data is in an abnormal state, an abnormal information is generated and an alarm is issued.

In order to solve the above technical problems, another technical solution of the present invention provides an MES-based digital factory twin management platform, including:

The data acquisition layer is used for data acquisition based on the preset data transmission protocol with each workshop and its equipment in the factory, and collects real-time operation data of each workshop and its equipment;

The platform layer is used to issue data acquisition instructions to the data acquisition layer and receive real-time operation data collected by the data acquisition layer, store operation data and statistically analyze the operation status and abnormal status of workshops and equipment;

The model layer is used to perform digital twinning of each workshop and its equipment in the factory based on the real-time operation data and historical operation data, and display the digital twin model on the platform layer; and

The application layer is used for human-computer interaction. Based on user instructions, it reads the operating data stored in the platform layer and receives the push of the operating data, operating status, and abnormal status of the platform layer.

Further, the platform layer includes:

The management data layer is used to receive and store the real-time operation data of each workshop and its equipment;

The interface layer is used for docking with the application layer based on a preset interface protocol, so as to receive reading instructions from the application layer and push operation data, operation status, and abnormal status to the application layer.

The data processing and display layer is used to comprehensively analyze the stored real-time operating data, obtain the operating status and abnormal status of the workshop and equipment, and map the operating status and abnormal status to the digital twin model synchronously.

In order to solve the above technical problems, another technical solution of the present invention provides an MES-based digital factory twin management method, including the following steps:

Construct a database to store and update the historical operation data of each workshop and its equipment in the factory;

Collect real-time operation data of each workshop and its equipment in the factory;

Build a digital twin model corresponding to each workshop and its equipment, map the operating status of the workshop and equipment to the corresponding digital twin model according to the operating data of each workshop and its equipment, and reversely simulate the operation of the workshop and equipment in response to operating instructions state.

The present invention integrates the historical operation data and real-time operation data of each workshop and its equipment in the factory by building the database and setting the data acquisition module, and can integrate the operation data of each workshop and equipment in the factory during the whole life cycle, and based on the digital twin technology Build a digital twin model of the factory, make full use of the operating data to simulate the operating state of the factory at multiple scales and probabilities, and visualize the simulation process, so that managers can understand the various operating conditions of the factory in real time without having to visit the site in person, and In the form of digital twins, based on real-time operating data, the production and processing process of the workshop and equipment is dynamically displayed to realize real-time monitoring of the production and processing process.

In addition, by setting up an early warning module, the present invention can monitor the abnormal conditions of the workshop and equipment in real time and push the alarm information in time, and reproduce the whole process of workshop or equipment operation when a fault occurs through the digital twin model. It is beneficial for maintenance personnel to respond quickly, quickly formulate countermeasures, and ensure smooth production; at the same time, the early warning module can also predict the operation trend of workshops and equipment based on historical and real-time operation data of workshops and equipment, which is beneficial Discover potential risks in time, and reasonably plan equipment pre-inspection and maintenance tasks.

Description of drawings

The drawings described here are used to provide a further understanding of the application and constitute a part of the application. The schematic embodiments and descriptions of the application are used to explain the application and do not constitute an improper limitation to the application. In the attached picture:

FIG. 1 is a system block diagram of an MES-based digital factory twin management system according to Embodiment 1 of the present invention.

Fig. 2 is a block diagram of the data acquisition module in Fig. 1 .

Fig. 3 is a block diagram of the digital twin module in Fig. 1 .

Fig. 4 is a system block diagram of an MES-based digital factory twin management system according to Embodiment 2 of the present invention.

Fig. 5 is a frame diagram of an MES-based digital factory twin management platform according to Embodiment 3 of the present invention.

Fig. 6 is a flow chart of the MES-based digital factory twin management method according to Embodiment 4 of the present invention.

Detailed ways

The following is further described in detail through specific implementation methods:

Example 1

As shown in FIG. 1 , it is a system block diagram of the MES-based digital factory twin management system of this embodiment. The MES-based digital factory twin management system in this embodiment is based on digital twin technology, and maps it to the mechanism model based on the factory entity by integrating the historical and real-time operation data of each workshop and equipment in the factory. Construct a digital twin model that can display the operation status of the factory in a timely and dynamic manner, so as to realize the visualization of the operation status of the workshop and equipment in the factory, and realize the monitoring and management of the whole life cycle of the workshop and equipment without visiting the site in person .

Specifically, the MES-based digital factory twin management system of this embodiment includes a database 1 , a data collection module 2 , a digital twin module 3 and a visualization module 4 . in:

The database 1 stores the historical operating data of each workshop and equipment in the factory, and can dynamically update the historical operating data based on the real-time operating data collected by the data acquisition module 2, so as to monitor the entire life cycle of the workshop and equipment. In this embodiment, the historical operation data and real-time operation data include but not limited to personnel data, production data and status data. Specifically, the personnel data can be used to manage personnel in the factory. The personnel data includes but is not limited to the number of on-duty personnel, the personnel information of each on-duty personnel, their real-time location and on-duty time, etc., so that according to the personnel's Real-time attendance and production scheduling, scheduling, etc.; the production data can be used to manage the production and processing of the factory, and the production data includes but is not limited to the total output of the workshop and its equipment, unit output, quantity of good products and The amount of consumables, etc., in order to monitor the output and production efficiency of each workshop and equipment, and then formulate or modify the production plan; the status data can be used to manage the operating status of the factory, and the status data includes but is not limited to workshops and The state parameters and operating parameters of the equipment, etc., the state parameters include but not limited to spot inspection status, repair status, maintenance status, stop inspection status, shutdown status, etc., the operating parameters include but not limited to temperature, humidity, air pressure, Water pressure, etc., in order to monitor the operating parameters of each workshop and equipment in real time, and then solve and troubleshoot abnormal situations in time and formulate appropriate maintenance tasks.

In this embodiment, corresponding data identifiers can be set for various types of operating data, so that when the database 1 receives the real-time operating data collected by the data acquisition module 2, it can correspondingly update the real-time operating data to the corresponding operating data. When the database 1 is updating the operation data, the operation data can be sorted and stored according to the time sequence, so that the procedure can use the operation data to analyze the development or operation trend of the personnel situation, processing situation and status of the workshop and equipment, So as to formulate or improve personnel scheduling plan, production plan, maintenance plan, etc. according to personnel situation, production situation and state situation.

The data collection module 2 can collect real-time operation data of each workshop and its equipment in the factory, so as to map the real-time operation of the factory to the subsequent digital twin model.

As shown in Figure 2, described data acquisition module 2 comprises personnel data acquisition submodule 21, production data acquisition submodule 22 and status data acquisition submodule 23; Wherein:

The personnel data collection sub-module 21 can collect the personnel data of each workshop; the personnel data includes but not limited to the number of on-duty personnel and the personnel information of each on-duty personnel as well as their real-time location and on-duty time. During concrete realization, described personnel data collection sub-module 21 can be enterprise's attendance system or station check-in system, the number of on-duty personnel and the personnel information and on-duty time of on-duty personnel can obtain the check-in and attendance situation of personnel every day from the attendance system, According to the check-in and attendance status, the number of currently on-duty personnel and their information and on-duty time (such as employee name, production unit, job type, clock-in time at work, clock-in time at work, etc.) are obtained; the real-time position of the personnel can be set at the station. Obtained from the station punch card system.

The production data collection sub-module 22 can collect the production data of each workshop and its equipment. The production data includes but not limited to the total output, unit output, good product quantity and consumables of the workshop and its equipment. The production data collection sub-module The module 22 can collect corresponding production data by identifying and counting the total output, unit output, good product quantity and/or consumables quantity of a workshop or a single device.

During specific implementation, when the total output or unit output is collected, the production data collection sub-module 22 can be based on the number of products loaded or unloaded in the loading and unloading area or the number of times the product is processed at the processing station for each workshop or equipment. The total output and unit output in the whole life cycle or counting production cycle are collected, that is, the output is accumulated by one each time the material is loaded in the feeding area, each time the processing position is processed, and/or each time the material is unloaded, so as to obtain the workshop or the total output of the equipment and/or the unit output; when collecting the quantity of good products, the production data acquisition sub-module 22 can identify the products processed by each workshop or equipment, and count each workshop or The good product data processed by the equipment in the whole life cycle or the planned production cycle is collected. When identifying the product, it can be realized by setting up an identification device in the corresponding position of the workshop or equipment. It collects the appearance of the product and can pass the machine The learning algorithm recognizes the appearance of the collected product to judge whether the product is a good product; when collecting the amount of consumables, for consumables that can be clearly quantified (such as sandpaper for grinding equipment, tools for turning and milling equipment, etc.) Statistics can be made on the number of times or frequency of replacement, and for consumables that cannot be clearly quantified (such as engine lubricating oil, etc.), statistics can be made based on the number of times the consumables are added and the amount added each time.

The status data collection sub-module 23 can collect status data of each workshop and its equipment, and the status data includes at least status parameters and operating parameters of the workshop and its equipment. The state parameters include but are not limited to spot inspection state, repair state, maintenance state, stop inspection state, shutdown state, etc.; for the collection of the state parameters, the state data collection sub-module 23 can be set in the workshop or corresponding to the equipment The external sensor of the position is used to collect the state parameters, such as setting the voltage or current sensor to collect the voltage or current signal during startup, shutdown and operation, so as to determine whether the status of the equipment is in operation, shutdown or fault, abnormal, etc. , Another example is to set up speed sensors, timers, counters, etc. to collect the speed, running time, and number of operations of the engine or machine tool during the operation of the equipment, so as to determine whether the equipment needs to be inspected, repaired, and maintained. The operating parameters include but are not limited to temperature, humidity, air pressure, water pressure, etc.; for the collection of the operating parameters, the state data acquisition sub-module 23 can also be an external sensor installed in the corresponding position of the workshop or equipment to monitor the operating parameters. Parameters are collected, such as setting the temperature sensor to collect the real-time temperature of the operation, setting the humidity sensor to collect the real-time humidity of the operation, setting the air pressure sensor to collect the real-time air pressure of the operation, and setting the hydraulic sensor to collect the real-time water pressure, etc. Of course , in addition, other types of sensors can also be set according to other data acquisition requirements, such as flow sensors, photoelectric switches, and so on.

The digital twin module 3 can map the operating status of the workshop and equipment to the corresponding digital twin model according to the operation data of each workshop and its equipment by constructing a digital twin model corresponding to each workshop and its equipment, and simulate the workshop and equipment in real time The operating conditions of the system, and can reversely simulate the operating status of the workshop and equipment in response to operating instructions.

As shown in Figure 3, the digital twin model includes a model building submodule 31, a data analysis and processing submodule 32 and a data two-way transmission submodule 33; wherein:

The model construction sub-module 31 is based on the physical drawings of each workshop and equipment in the factory, performs 3D modeling on each workshop and equipment of the factory to obtain a mechanism model of the project, and builds a data model of the factory based on the mechanism model according to real-time operation data. The model is associated with the data model to obtain the digital twin model of the factory. The model building sub-module 31 includes a mechanism model building unit 313, a data model building unit 312 and a model association unit 313; wherein:

The mechanism model construction unit 313 obtains existing physical drawings of each workshop and equipment in the factory (such as construction drawings or design drawings of factories and workshops, appearance drawings of equipment, etc.) or draws physical drawings based on physical entities of workshops and equipment, Carry out 3D modeling of the factory based on the physical drawings, and build a mechanism model of the factory. In this embodiment, when modeling the factory and its internal workshops and equipment, first, preferably use 3D modeling software (such as UG, SW, 3Dmax, etc.) to perform isometric modeling and render to obtain the factory, workshop and equipment. The 3D model of the equipment; then, expand the 3D model of the factory, workshop and equipment, and superimpose the images of the factory, workshop and equipment on the expanded 3D model, so that the subsequent construction of the mechanism model is closer to the real space; Finally, establish a coordinate system based on the unity3D engine, import the 3D model superimposed with the factory, workshop and equipment images into unity, assign the position coordinates of each workshop and equipment in the factory, and construct the mechanism model of the factory.

The data model construction unit 312 obtains the historical operation data in the database 1 and the real-time operation data collected by the data acquisition module 2, and converts the historical operation data and the real-time operation data into the action data of each workshop and equipment of the factory , build the data model of the factory. Since the historical operating data in the database 1 and the operating data collected by the data acquisition module 2 are single static parameter data, when performing digital twinning, it is necessary to convert the static parameter data into action data corresponding to the mechanism model one by one, In order to facilitate the real-time simulation of the target of the digital twin. For example, if the unit output of a machine tool is 30pcs/h, it is necessary to convert the unit output into corresponding production action data, and if the engine speed is 500rad/s, it needs to be converted into corresponding rotation action data, etc. , can correspond the data model with the mechanism model.

The model association unit 313 imports the data model into the mechanism model to associate the data model with the mechanism model, maps the action data of the data model to the mechanism model, and constructs a digital twin model of the factory. In the specific implementation, in order to ensure that the data model and the mechanism model can be accurately associated and mapped, several feature points can be determined in the mechanism model, and the corresponding mapping points corresponding to the feature points can be determined at the corresponding positions in the data model, so that based on the feature points The association with the mapping point accurately associates and maps the data model and the mechanism model.

The data analysis and processing sub-module 32 can make statistics on the operation data of each workshop and its equipment and perform comprehensive calculation on the statistical operation data to obtain the operation status of each workshop and its equipment, so that the digital twin model can dynamically display the workshop and equipment real-time operating status. Specifically, the data analysis and processing sub-module 32 conducts statistics on the personnel data, production data and status data, and analyzes personnel conditions, production and processing conditions, operation conditions, and abnormal conditions according to the personnel data, production data and status data. Calculate and analyze information to determine the current operating status of the workshop and its equipment.

When this embodiment is actually implemented, for the analysis and calculation of the personnel situation, the data analysis and processing sub-module 32 can calculate the work of each personnel according to the number of on-duty personnel and the personnel information of each on-duty personnel and their real-time position and on-duty time. Duration (can be calculated by clocking in time) and work saturation (can be calculated based on the employee's processing status or production efficiency per unit time), etc., to monitor the real-time operation status of each workshop and equipment in the factory or the required personnel It is analyzed and displayed in the digital twin model, so that managers can perform attendance, assessment, production scheduling and shift scheduling of personnel. For the analysis and calculation of production conditions, the data analysis sub-module can calculate the production efficiency, yield rate and utilization rate of the workshop and equipment according to the total output, unit output, quantity of good products and consumables of the workshop and its equipment and reflect it to In the digital twin system, managers can adjust or formulate production plans in a timely manner according to production operations. For the analysis and calculation of the status data, the data analysis processing sub-module 32 can calculate and analyze the current operating conditions (such as whether spot checks, repairs, maintenance, etc. are required) and abnormal information (such as calculated temperature, Humidity, air pressure, water pressure, etc. exceed the set threshold), so that the manager can timely formulate appropriate inspection and maintenance tasks when inspection, repair, and maintenance are required, and timely check and solve abnormal situations in case of abnormalities.

In this embodiment, the data analysis and processing sub-module 32 can also analyze the historical operation data of each workshop and equipment in the factory to generate an operation curve, thereby analyzing the operation trend of each workshop and equipment, so as to identify potential risks Prediction facilitates reasonable planning of workshop and equipment maintenance.

The data two-way transmission sub-module 33 can map the operation data and operation state to the digital twin model and receive operation instructions to reversely control the digital twin model for real-time simulation. Specifically, the data two-way transmission sub-module 33 is based on the idea of two-way reading and writing, which can read the real-time operation data collected by the data collection module 2 in time and transmit them to the digital twin model to realize virtual-real linkage; at the same time, the The data two-way transmission sub-module 33 can also receive the operation instructions generated by the user based on the simulation of the digital twin model, and reversely transmit the control instructions to the device to realize two-way control.

The visualization module 4 can visualize the digital twin model to form a twin management scene, so that the manager can monitor the production and processing process of each workshop and its equipment in the factory in real time without having to visit the site in person, so as to realize the real-time monitoring of the factory, workshop and equipment. digital twin management. During the specific implementation of this embodiment, the visualization module 4 is configured so that when clicking in the digital twin model, the current operation data and/or historical operation data of the selected workshop or equipment can be displayed in a table or It can be displayed graphically in real time according to the manager's management needs.

As a preferred mode of this embodiment, in addition to visualizing the digital twin model, the visualization module 4 can also access the basic support equipment of the factory (such as power equipment, water supply equipment, and environmental perception equipment, etc.), access After the power equipment, the important operation indicators (such as switch status, fault alarm, voltage, current, power factor, power consumption, etc.) data of the operation status of each equipment in the power supply and distribution system of the factory can be visualized; access to water supply equipment After that, the operation monitoring of water supply equipment can be realized, and the operating status of the system can be intuitively displayed, such as the visualization of water flow direction, total power consumption, instantaneous power consumption and peak power consumption; after connecting to the environmental sensing device, the temperature and humidity of the environment can be visualized .

The MES-based digital factory twin management system of this embodiment, based on digital twin technology, integrates the historical and real-time operating data of each workshop and its equipment in the factory, sets the digital twin module 3 to construct the digital twin model of the factory, and makes full use of the database The historical operation data in 1 and the real-time operation data collected by the digital acquisition module dynamically display and visualize the operation status of the factory, so that managers can understand the various operation conditions of the factory in real time without having to visit the site in person, so as to realize real-time production and processing. monitor.

Example 2

As shown in FIG. 4 , it is a system block diagram of the MES-based digital factory twin management system of this embodiment. The MES-based digital factory twin management system of this embodiment includes a database 1 , a data acquisition module 2 , a digital twin module 3 and a visualization module 4 with the same or similar structure and function as those in Embodiment 1. The difference in this example is:

This embodiment also includes a pre-warning module 5 for comprehensively judging based on historical operation data and real-time operation data, and generating abnormal information and giving an alarm when the operation is abnormal.

Specifically, the early warning module 5 can be based on the historical operating data in the database 1, combined with the data analysis and processing sub-module 32 to calculate and analyze the production efficiency, yield rate, utilization rate, operating conditions and abnormal information. The real-time operation data is comprehensively judged, and abnormal information is generated and an alarm is issued when the real-time operation data is in an abnormal state. More specifically, the early warning module 5 can match and compare the real-time operating data, production efficiency, yield rate, utilization rate, operating conditions and abnormal information with the corresponding data in the historical operating data. , generating abnormal information, displaying it in the visualization module 4, and synchronously giving an alarm in the form of sound and/or light, so that the manager can take countermeasures in time.

The MES-based digital factory twin management system of this embodiment, by setting the early warning module 5, can give an alarm when the operation of each workshop and equipment in the factory is abnormal, so that the manager can find the abnormality in time and take countermeasures quickly, thereby reducing the number of personnel. injury and economic loss.

Example 3

As shown in FIG. 5 , it is a frame diagram of the MES-based digital factory twin management platform of this embodiment. The MES-based digital factory twin management platform of this embodiment is implemented based on the MES-based digital factory twin management system of embodiment 1 or embodiment 2, and serves as the bottom layer of the MES-based digital factory twin management system of embodiment 1 or embodiment 2 Architecture to support its realization of the digital twin of the production process. The MES-based digital factory twin management platform of this embodiment includes a data acquisition layer, a platform layer, a model layer and an application layer; wherein:

The data collection layer has a number of preset data collection protocols, based on the preset data transmission protocols, data collection is performed between workshops of the factory and their equipment. Specifically, the data collection layer can structure the collection instructions issued by the application layer to collect data and transmit the real-time operation data collected from different workshops and equipment to the platform layer.

The platform layer can issue data collection instructions to the data collection layer and receive the real-time operation data collected by the data collection layer, store the operation data and then statistically analyze the operation status and abnormal status of workshops and equipment. The platform layer includes a management data layer, an interface layer, and a data processing and display layer; wherein:

The management data layer can receive real-time operation data of workshops and equipment and store historical operation data of each workshop and its equipment.

The interface layer is docked with the application layer based on a preset interface protocol, and the interface layer gathers external interface data (mainly the application layer in this embodiment) to receive the read instruction issued by the application layer and send the data to the application layer. Push operating data, operating status, and abnormal status.

The data processing and display layer can comprehensively analyze the stored operating data of each workshop and its equipment, obtain the operating status and abnormal status of the workshop and equipment, and map the operating status and abnormal status to the digital twin model synchronously, so that The digital twin model can dynamically display the real-time operating status of workshops and equipment.

The model layer constructs a digital twin model corresponding to each workshop and its equipment based on the real-time operation data and historical operation data, and can map the operation status of the workshop and equipment to the corresponding digital twin according to the operation data of each workshop and its equipment The model simulates the operation of the workshop and equipment in real time, and can display the digital twin model on the platform layer after reversely simulating the operation status of the workshop and equipment in response to operating instructions.

The application layer is used for human-computer interaction, reads operating data stored in the platform layer based on user instructions, and receives pushes of operating data, operating status, and abnormal status of the platform layer. In this embodiment, the application layer can be implemented based on third-party software built in the mobile terminal, so as to realize the sending of instructions and the reception of operating data, operating status, and abnormal status.

In this embodiment, the data acquisition layer, management data layer, interface layer, data processing and display layer, and model layer are respectively connected with the data acquisition module 2, database 1, data two-way transmission sub-module 33, data The analysis and processing sub-module 32 (and/or the visualization module 4 ) corresponds to the model building sub-module 31 one by one. For related descriptions, refer to the corresponding descriptions in Embodiment 1, which will not be repeated in this embodiment.

The MES-based digital factory twin management platform of this embodiment builds the underlying framework for the MES-based digital factory twin management system, collects operating data through the data collection layer, stores and updates operating data at the management data layer, and implements data transmission at the interface layer The , data processing and display layer analyze, process and visualize the operational data, and carry out the digital twin of the factory through the model layer to support the realization of the digital twin of the production and processing process.

Example 4

As shown in FIG. 6 , it is a flow chart of the MES-based digital factory twin management method of this embodiment. The MES-based digital factory twin management method of this embodiment is implemented based on the MES-based digital factory twin management system of embodiment 1 or embodiment 2, including a database 1 and a data acquisition module with the same or similar structure and function as that of embodiment 1 2. The digital twin module 3 and the visualization module 4 may optionally include an early warning module 5 having the same or similar structure and function as that of Embodiment 2. The MES-based digital factory twin management method of this embodiment includes the following steps:

S1: Build database 1 to store and update historical operation data.

Concretely, build a database 1, store the historical operation data of each workshop and equipment in the said database 1, and can dynamically update the historical operation data based on the real-time operation data collected by the data acquisition module 2, so that the workshop and The entire life cycle of equipment is monitored. In this embodiment, the step S1 is implemented based on the database 1 in the first embodiment. For the construction process, data storage and update process, refer to the relevant description in the first embodiment, which will not be repeated in this embodiment.

S2: Collect real-time running data.

Specifically, the real-time operation data of each workshop of the factory and its equipment is collected through the data collection module 2, so as to map the real-time operation of the factory to the subsequently constructed digital twin model. In this embodiment, the step S1 is implemented based on the data collection module 2 of the first embodiment. For the specific collection process, please refer to the related description in the first embodiment, which will not be repeated in this embodiment.

S3: Build a digital twin model and visualize it.

Specifically, by constructing a digital twin model corresponding to each workshop and its equipment through the digital twin module 3, the operating status of the workshop and equipment can be mapped to the corresponding digital twin model according to the operating data of each workshop and its equipment, real-time Simulate the operation of the workshop and equipment, and reversely simulate the operation status of the workshop and equipment in response to operating instructions, and visualize the digital twin model to form a twin management scenario. In this embodiment, the step S1 is implemented based on the digital twin module 3 and the visualization module 4 of the embodiment 1. For the specific process, refer to the relevant description in the embodiment 1, and this embodiment will not repeat it.

The MES digital factory twin management method of this embodiment, by constructing a database 1 capable of storing the operation data of each workshop and equipment in the factory, can manage various types of operation data, and combine the collected real-time operation data for each workshop and equipment of the factory. The real-time operation of the equipment is digitally twinned, so as to really display the real-time operation scenarios of the workshops and equipment in the factory, so as to understand the various operation conditions of the factory in a timely and accurate manner.

Claims (10)

1. A MES-based digital factory twin management system, characterized in that it comprises: The database is used to store and update the historical operation data of each workshop and its equipment; The data collection module is used to collect the real-time operation data of each workshop and its equipment in the factory; and The digital twin module is used to build a digital twin model corresponding to each workshop and its equipment, and map the operating status of the workshop and equipment to the corresponding digital twin model according to the operating data of each workshop and its equipment, and respond to the reverse operation instruction Simulate the operating status of workshops and equipment. 2. The MES-based digital factory twin management system according to claim 1, wherein said operating data at least includes personnel data, production data and status data; said data collection module includes: The personnel data collection sub-module is used to collect the personnel data of each workshop, wherein the personnel data includes at least the number of on-duty personnel and the personnel information of each on-duty personnel and their real-time location and on-duty time; The production data collection sub-module is used to collect the production data of each workshop and its equipment, wherein the production data at least includes the total output of the workshop and its equipment, the unit output, the quantity of good products and the quantity of consumables; and The status data collection sub-module is used to collect status data of each workshop and its equipment, wherein the status data includes at least status parameters and operating parameters of the workshop and its equipment. 3. The MES-based digital factory twin management system according to claim 2, wherein the digital twin module includes: The model construction sub-module is used to construct the mechanism model and data model of the workshop and its equipment and associate the mechanism model with the data model to obtain the digital twin model of the factory; The data analysis and processing sub-module is used to make statistics on the operation data of each workshop and its equipment and perform comprehensive calculation on the statistical operation data to obtain the operation status of each workshop and its equipment; and The data two-way transmission sub-module is used to map the operation data and operation state to the digital twin model and receive operation instructions to reversely control the digital twin model for real-time simulation. 4. MES-based digital factory twin management system according to claim 3, is characterized in that, described model construction submodule comprises: The mechanism model building unit is used to obtain the existing physical drawings of each workshop and equipment in the factory or draw physical drawings based on the physical entities of the workshops and equipment, perform 3D modeling on the factory based on the physical drawings, and construct the mechanism model of the factory; The data model building unit is used to obtain the historical operation data and real-time operation data of the factory, convert the historical operation data and real-time operation data into the action data of each workshop and equipment in the factory, and construct the data model of the factory; and The model association unit is used to associate the data model with the mechanism model, map the action data of the data model to the mechanism model, and construct a digital twin model of the factory. 5. The MES-based digital factory twin management system according to claim 3, wherein the data analysis and processing sub-module is also used to make statistics on the personnel data, production data and status data, and according to the The number of on-the-job personnel, the personnel information of each on-the-job personnel and their real-time location and on-the-job time are calculated and analyzed for the real-time operation of the personnel in each workshop and equipment in the factory, based on the total output of the workshop and its equipment, unit output, quantity of good products and Calculate and analyze the production efficiency, yield rate and utilization rate of each workshop and its equipment, and calculate and analyze the operation status and abnormal information of the workshop and its equipment according to the state parameters and operating parameters. 6. The MES-based digital factory twin management system according to claim 1, further comprising a visualization module; The visualization module is used to visualize the digital twin model, form a twin management scene, and perform twin management on the production and processing processes of each workshop and its equipment in the factory. 7. The MES-based digital factory twin management system according to claim 5, further comprising an early warning module; The early warning module is used to comprehensively judge the real-time operation data according to the historical operation data combined with the production efficiency, yield rate, utilization rate, operation status and abnormal information calculated and analyzed by the data analysis and processing sub-module, and When the above real-time operation data is in an abnormal state, an abnormal information is generated and an alarm is issued. 8. MES-based digital factory twin management platform, characterized in that it includes: The data acquisition layer is used for data acquisition based on the preset data transmission protocol with each workshop and its equipment in the factory, and collects real-time operation data of each workshop and its equipment; The platform layer is used to issue data acquisition instructions to the data acquisition layer and receive real-time operation data collected by the data acquisition layer, store operation data and statistically analyze the operation status and abnormal status of workshops and equipment; The model layer is used to perform digital twinning of each workshop and its equipment in the factory based on the real-time operation data and historical operation data, and display the digital twin model on the platform layer; and The application layer is used for human-computer interaction. Based on user instructions, it reads the operating data stored in the platform layer and receives the operating data, operating status, and abnormal status of the platform layer. 9. The MES-based digital factory twin management platform according to claim 8, wherein the platform layer comprises: The management data layer is used to receive and store the real-time operation data of each workshop and its equipment; The interface layer is used for docking with the application layer based on the preset interface protocol, to receive the read instruction issued by the application layer and push the operation data, operation status and abnormal status to the application layer. The data processing and display layer is used to comprehensively analyze the stored real-time operating data, obtain the operating status and abnormal status of the workshop and equipment, and map the operating status and abnormal status to the digital twin model synchronously. 10. The digital factory twin management method based on MES, is characterized in that, comprises the following steps: Construct a database to store and update the historical operation data of each workshop and its equipment in the factory; Collect real-time operation data of each workshop and its equipment in the factory; Construct a digital twin model corresponding to each workshop and its equipment, map the operating status of the workshop and equipment to the corresponding digital twin model according to the operating data of each workshop and its equipment, and reversely simulate the operation of the workshop and equipment in response to operating instructions state.

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