Smart factory control method and device based on digital twinning, medium and product

Abstract

The invention relates to the technical field of digital twinning, in particular to a smart factory control method and device based on digital twinning, a medium and a product, and the method comprises the steps: building a three-dimensional model based on the digital twinning technology and factory operation data, and obtaining a smart factory model; then, multi-dimensional monitoring data obtained in real time are input into the smart factory model, factory operation prediction is carried out, and operation prediction data are determined; and finally, performing intelligent regulation and control analysis on the smart factory based on the operation prediction data, and determining intelligent regulation and control information. When the smart factory model is constructed, the environment data and the personnel data are comprehensively considered, so that the smart factory model can more comprehensively know the actual operation condition of factory production, and meanwhile, analysis and mining are executed, so that the relationship and the rule among multi-dimensional data can be mined, optimization and adjustment operations can be adopted in time, and the operation efficiency is improved. Therefore, the safety and rationality of the production process of the smart factory are improved.

Description

Technical Field

[0001] The present application relates to the technical field of digital twins, and in particular to smart factory control methods, equipment, media, and products based on digital twins. Background Art

[0002] With the advent of the industrial age, the concept of smart factories has become increasingly popular. By integrating advanced technologies such as the Internet of Things, big data, and cloud computing, smart factories have achieved intelligent, automated, and efficient production processes. Digital twin technology, as one of the core technologies of smart factories, provides a new solution for production control.

[0003] Digital twin technology uses digital means to construct a virtual entity that is identical to the real world, simulating its behavior in a real environment and dynamically presenting past and present actions or processes. However, in the construction process of digital twin models for smart factories, related technologies often only consider device-level data, while ignoring the coupling effects of various risk factors such as personnel and the environment. As a result, digital twin models are unable to accurately predict and identify abnormal situations in factory production processes, thus affecting the safety of smart factory production processes.

[0004] Therefore, how to solve the above technical defects is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a smart factory control method, equipment, medium and product based on digital twins to solve at least one of the above technical problems.

[0006] The above-mentioned invention objectives of this application are achieved through the following technical solutions: In the first aspect, the present application provides a smart factory control method based on digital twins, which adopts the following technical solutions: A digital twin-based smart factory control method, comprising: Extracting factory operation data for building a factory model from a factory historical data set, and building a three-dimensional model based on digital twin technology and the factory operation data to obtain a smart factory model; Acquire multi-dimensional monitoring data in real time, input the multi-dimensional monitoring data acquired in real time into the smart factory model, and control the smart factory model to perform factory operation prediction and determine operation prediction data, wherein the multi-dimensional monitoring data includes: environmental monitoring data, personnel monitoring data, and equipment monitoring data; Based on the operation prediction data, the smart factory is intelligently controlled and analyzed, the intelligent control information is determined, and the intelligent control information is sent to the target terminal, which helps to improve the response speed to abnormal conditions of the smart factory.

[0007] By employing the above technical solution, factory operation data for building a factory model is extracted from a collection of historical factory data. A three-dimensional model is constructed based on digital twin technology and factory operation data, resulting in a smart factory model. Next, multi-dimensional monitoring data is acquired in real time and fed into the smart factory model. The model is then controlled to predict factory operation and determine predicted operation data. Finally, based on the predicted operation data, the smart factory is intelligently controlled and analyzed, intelligent control information is determined, and this intelligent control information is sent to the target terminal, helping to improve the response speed to abnormal conditions in the smart factory. When constructing the smart factory model, in addition to considering equipment data, environmental data and personnel data are also comprehensively considered, allowing the smart factory model to more comprehensively understand the actual operation of factory production. Furthermore, by analyzing and mining multi-dimensional data, connections and patterns between the multi-dimensional data are discovered, enabling more accurate prediction and diagnosis of abnormal conditions in the production process and timely optimization and adjustment operations, thereby improving the safety and rationality of the smart factory production process.

[0008] In a preferred example, the present application may be further configured as follows: the real-time acquisition of multi-dimensional monitoring data includes: Using the target data transmission protocol, monitoring data transmission information and collection equipment working information are acquired in real time, and based on the monitoring data transmission information and the collection equipment working information, transmission status analysis and collection equipment status analysis are performed to determine the data transmission status and collection equipment working status; When the working state of the acquisition device is abnormal, a backup device is selected based on the abnormal acquisition device, the backup device is controlled to collect monitoring data, and the multi-dimensional monitoring data is obtained in real time; When the data transmission state is abnormal, a pause transmission instruction is sent to the collection device that collects the multi-dimensional monitoring data, and a backup data transmission channel is enabled; When the backup data transmission channel is connected successfully, a resume sending instruction is sent to the acquisition device, and the multi-dimensional monitoring data is obtained in real time, wherein the resume sending instruction is used to control the acquisition device to send all data from the time of suspension of sending to the current time, so as to ensure the integrity and continuity of the multi-dimensional monitoring data.

[0009] In a preferred example, the present application may be further configured as follows: controlling the smart factory model to perform factory operation prediction and determining operation prediction data includes: Obtaining a prediction target, performing data screening based on the prediction target and the multi-dimensional monitoring data, and determining target monitoring data; A simulation is performed based on the smart factory model, the target monitoring data and the prediction target to obtain full-dimensional prediction data, and key data is extracted based on the full-dimensional prediction data and the prediction target to determine the operation prediction data.

[0010] In a preferred example, the present application may be further configured as follows: performing intelligent control analysis on the smart factory based on the operation prediction data to determine intelligent control information includes: Acquiring operation standard data corresponding to the predicted target, and performing operation monitoring based on the operation prediction data and the operation standard data to determine an operation monitoring result, wherein the operation monitoring result includes: factors to be regulated and normal factors; Based on the target prediction data corresponding to the factors to be regulated, intelligent regulation analysis is performed on the smart factory to determine intelligent regulation information.

[0011] In a preferred example, the present application may be further configured as follows: the factory operation data includes: historical environmental data, personnel data, and equipment operation data within the factory; the three-dimensional model is constructed based on the digital twin technology and the factory operation data to obtain a smart factory model, including: Obtaining a factory layout map and a digital twin framework, and performing initial three-dimensional modeling based on the factory layout map to obtain an initial factory model, wherein the initial factory model is used to reflect the physical form and spatial relationships of the factory; Integrating the digital twin framework with the initial plant model to obtain a digital twin model; A data prediction algorithm is obtained, and the data prediction algorithm and the factory operation data are added to the digital twin model to obtain the smart factory model.

[0012] In a preferred example, the present application may be further configured as follows: after performing intelligent control analysis on the smart factory based on the operation prediction data and determining the intelligent control information, the following steps may be further included: Performing three-dimensional visualization of the smart factory model using holographic projection technology to obtain a virtual three-dimensional image; The operation prediction data and the intelligent control information are added to the virtual three-dimensional image so that workers can interactively view the factory data of the location of interest.

[0013] In a second aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the above-mentioned digital twin-based smart factory control method.

[0014] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute the above-mentioned smart factory control method based on digital twins.

[0015] In a fourth aspect, the present application provides a computer program product that adopts the following technical solution: A computer program product includes a computer program, which, when executed by a processor, implements the above-mentioned smart factory control method based on digital twins.

[0016] In summary, this application includes at least one of the following beneficial technical effects: The factory model is constructed by extracting operational data from the factory's historical data set. A three-dimensional model is constructed based on digital twin technology and factory operational data, resulting in a smart factory model. Next, multi-dimensional monitoring data is acquired in real time and fed into the smart factory model. The model is then controlled to predict factory operations and determine predicted operational data. Finally, based on the predicted operational data, the smart factory is intelligently controlled and analyzed, intelligent control information is determined, and this information is sent to the target terminal, helping to improve the response speed to abnormal conditions in the smart factory. When constructing the smart factory model, in addition to equipment data, environmental data and personnel data are also comprehensively considered, allowing the smart factory model to more comprehensively understand the actual operational status of factory production. Furthermore, through the analysis and mining of multi-dimensional data, connections and patterns between these data are discovered, enabling more accurate prediction and diagnosis of abnormal conditions in the production process and timely optimization and adjustment operations, thereby improving the safety and rationality of the smart factory production process.

[0017] Obtain forecast targets, filter data based on the forecast targets and multi-dimensional monitoring data, and determine target monitoring data. Then, perform simulations based on the smart factory model, target monitoring data, and forecast targets to obtain full-dimensional forecast data. Based on this full-dimensional forecast data and forecast targets, extract key data and determine operational forecast data. Executing factory operational forecasting operations predicts future production capacity, equipment failure risks, energy consumption trends, and other factors. This helps factories rationally allocate human resources, equipment resources, and material resources to ensure smooth production processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a smart factory control method based on digital twins according to one embodiment of the present application; Figure 2 This is a structural diagram of a smart factory control system based on digital twins in one embodiment of the present application; Figure 3 This is a structural diagram of an electronic device according to one embodiment of the present application. DETAILED DESCRIPTION

[0019] The following combination Figures 1 to 3 This application is described in further detail.

[0020] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the present application, they are protected by patent law.

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should be noted that in the optional embodiments of the present application, when the embodiments in the present application are applied to specific products or technologies, the object information and other related data involved need to obtain the object's permission or consent, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained through the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0022] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0023] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0024] The embodiment of the present application provides a smart factory control method based on digital twins, which is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited to this. The terminal device and the server can be directly or indirectly connected through wired or wireless communication. The embodiment of the present application does not limit this. Figure 1 As shown, the method includes step S101, step S102 and step S103, wherein: Step S101: extracting the factory operation data for building the factory model from the factory historical data set, and building a three-dimensional model based on the digital twin technology and the factory operation data to obtain a smart factory model.

[0025] For the embodiments of the present application, in the process of constructing the digital twin model of the smart factory in the related art, only the data at the equipment level is often considered, while the coupling effect of various risk factors such as personnel and environment is ignored, resulting in the digital twin model being unable to accurately predict and identify abnormal conditions in the factory production process, thereby affecting the safety and rationality of the smart factory production process. In order to solve the defect of poor accuracy of the digital twin model in the related art, when constructing the smart factory model, the embodiments of the present application, in addition to considering equipment data, will also comprehensively consider environmental data and personnel data, so that the smart factory model can more comprehensively understand the actual operation of the factory production. At the same time, through the analysis and mining of multi-dimensional data, it is helpful to dig out the connections and laws between multi-dimensional data, help to more accurately predict and judge abnormal conditions in the production process, and take optimization and adjustment operations in a timely manner, thereby improving the safety and rationality of the smart factory production process.

[0026] Specifically, the electronic device pre-stores a set of historical factory data corresponding to the factory. This set of factory historical data represents the sum of various data generated by the factory over a period of time. It is collected using various sensors, IoT devices, and the factory's internal information system deployed within the factory. This data includes, but is not limited to, environmental data, personnel data, and equipment data. Environmental data is important for evaluating the factory's production environment, ensuring product quality, and optimizing resource consumption. Personnel data helps understand employee status and performance, thereby optimizing human resource allocation and improving production efficiency. Equipment data is crucial for preventive maintenance, troubleshooting, and optimizing equipment operation. Furthermore, a three-dimensional model is constructed based on digital twin technology and factory operation data to obtain a smart factory model that can truly reflect the factory's physical layout, equipment configuration, and personnel flow. The specific implementation process for building a 3D model of a smart factory model is as follows: The factory layout map and digital twin framework are obtained, and initial 3D modeling is performed based on the factory layout map to obtain an initial factory model. The initial factory model is used to reflect the factory's physical form and spatial relationships. The initial factory model is integrated with the digital twin framework to obtain a digital twin model. A data prediction algorithm is obtained and added to the digital twin model along with factory operating data to obtain a smart factory model. This smart factory model is used for production control, equipment maintenance, and safety management within the factory. It enables real-time monitoring of the factory's production process, fault warnings, and optimized scheduling, thereby improving the smart factory's production efficiency, reducing operating costs, and ensuring production safety.

[0027] Step S102: Acquire multi-dimensional monitoring data in real time, input the real-time acquired multi-dimensional monitoring data into the smart factory model, and control the smart factory model to perform factory operation prediction and determine operation prediction data, wherein the multi-dimensional monitoring data includes: environmental monitoring data, personnel monitoring data and equipment monitoring data.

[0028] In the embodiments of the present application, a sensor network and detection devices are pre-deployed within the factory to acquire real-time environmental monitoring data, personnel monitoring data, and equipment monitoring data within the factory. Simultaneously, the sensor network and detection devices transmit data to electronic devices via wireless communication, enabling the electronic devices to acquire multi-dimensional monitoring data in real time. To ensure the accuracy and real-time nature of the multi-dimensional monitoring data, high-quality sensors and efficient data transmission protocols are employed to ensure the collected multi-dimensional monitoring data is highly reliable and stable, and the data transmission process is characterized by low latency and high reliability. Furthermore, the real-time multi-dimensional monitoring data is input into a smart factory model, which is then controlled to perform factory operation predictions and determine predicted operation data. The smart factory model is trained based on historical factory operation data and also incorporates a data prediction algorithm. Therefore, upon receiving the real-time multi-dimensional monitoring data, the smart factory model can automatically perform factory operation predictions and output predicted operation data, including but not limited to predicted output, predicted failure rate, and predicted energy consumption. The specific content of the predicted operation data can be determined based on the prediction objectives of the smart factory, and this embodiment of the present application does not limit this.

[0029] Step S103: Perform intelligent control analysis on the smart factory based on the operation prediction data, determine the intelligent control information, and send the intelligent control information to the target terminal, which helps to improve the response speed to abnormal conditions of the smart factory.

[0030] For the embodiment of the present application, the operation prediction data gives the operation status of the factory under the current conditions for a period of time in the future, which to a certain extent reflects the production capacity, equipment failure risk, energy consumption trend, etc. of the factory in the future, so as to more accurately grasp the production status of the factory. Therefore, based on the operation prediction data, the smart factory is intelligently controlled and analyzed, the intelligent control information is determined, and the intelligent control information is sent to the target terminal, which helps to timely discover and handle abnormal conditions based on the operation prediction data, realize real-time monitoring and remote control of the factory production process, and improve the response speed to abnormal conditions of the smart factory. There are many specific implementation methods for intelligent control analysis, which are no longer limited in the embodiment of the present application. In one feasible method, the operation standard data corresponding to the prediction target is obtained, and the operation monitoring is performed based on the operation prediction data and the operation standard data to determine the operation monitoring results, wherein the operation monitoring results include: factors to be controlled and normal factors; based on the target prediction data corresponding to the factors to be controlled, the smart factory is intelligently controlled and analyzed to determine the intelligent control information. Intelligent control information includes but is not limited to: production plan adjustment information, equipment maintenance plan, energy consumption optimization strategy, staffing suggestions, inventory management suggestions, etc. Among them, production plan adjustment information helps to optimize production processes, improve production efficiency, and reduce production costs; equipment maintenance plan is used to reduce the risk of equipment failure and reduce production interruption time; energy consumption optimization strategy is used to improve energy utilization efficiency; staffing suggestions are used to reasonably allocate personnel to ensure the smooth completion of production tasks; inventory management suggestions are used to optimize inventory and avoid inventory backlogs and waste.

[0031] It can be seen that in the embodiment of the present application, the factory operation data for the factory model is extracted from the factory historical data set, and a three-dimensional model is constructed based on the digital twin technology and the factory operation data to obtain a smart factory model. Then, multi-dimensional monitoring data is obtained in real time, and the multi-dimensional monitoring data obtained in real time is input into the smart factory model, and the smart factory model is controlled to perform factory operation prediction and determine the operation prediction data. Finally, based on the operation prediction data, the smart factory is intelligently controlled and analyzed, the intelligent control information is determined, and the intelligent control information is sent to the target terminal, which helps to improve the response speed to abnormal conditions of the smart factory. When constructing the smart factory model, in addition to considering equipment data, environmental data and personnel data are also comprehensively considered so that the smart factory model can more comprehensively understand the actual operation of the factory production. At the same time, through the analysis and mining of multi-dimensional data, it helps to dig out the connections and laws between the multi-dimensional data, which helps to more accurately predict and judge abnormal conditions in the production process, and take optimization and adjustment operations in a timely manner, thereby improving the safety and rationality of the smart factory production process.

[0032] Furthermore, in order to promptly discover and resolve anomalies in the data transmission or data collection phase and ensure the accuracy and completeness of the multi-dimensional monitoring data, in an embodiment of the present application, real-time acquisition of multi-dimensional monitoring data includes: Utilize the target data transmission protocol to obtain monitoring data transmission information and acquisition equipment working information in real time, and perform transmission status analysis and acquisition equipment status analysis based on the monitoring data transmission information and acquisition equipment working information to determine the data transmission status and acquisition equipment working status; When the working status of the collection device is abnormal, a backup device is selected based on the abnormal collection device, and the backup device is controlled to collect monitoring data and obtain multi-dimensional monitoring data in real time; When the data transmission status is abnormal, a pause transmission instruction is sent to the collection device that collects multi-dimensional monitoring data, and a backup data transmission channel is enabled; When the backup data transmission channel is connected successfully, a resume sending instruction is sent to the acquisition device, and multi-dimensional monitoring data is obtained in real time. The resume sending instruction is used to control the acquisition device to send all data from the time of suspension of sending to the current time to ensure the integrity and continuity of the multi-dimensional monitoring data.

[0033] In the embodiments of this application, when using a digital twin smart factory model to predict and intelligently control factory operations, it is necessary to ensure the accuracy of the multi-dimensional monitoring data input into the smart factory model to avoid inaccurate subsequent prediction data and control information due to deviations or errors in the multi-dimensional monitoring data. Therefore, during the real-time acquisition of multi-dimensional monitoring data, transmission status analysis and acquisition device status analysis are performed to promptly detect and resolve anomalies in the data transmission or data collection stages, ensuring the accuracy and integrity of the multi-dimensional monitoring data.

[0034] Specifically, monitoring data transmission information and collection device operating information are acquired in real time using a target data transmission protocol. The target data transmission protocol is a low-latency, high-reliability transmission protocol suitable for real-time data transmission. The monitoring data transmission information includes, but is not limited to, data packets carrying multi-dimensional monitoring data, transmission time, transmission speed, and other information. Then, based on the monitoring data transmission information, a transmission status analysis and a collection device status analysis are performed to determine the data transmission status and the collection device operating status. Specifically, during the transmission status analysis, the continuity, stability, and delay of the data transmission are evaluated based on the monitoring data transmission information. Pre-set transmission status standards are provided. If any of these standards are not met, the data transmission status is determined to be abnormal; otherwise, the data transmission status is determined to be normal. Furthermore, during the collection device status analysis, the collection device status is analyzed based on the operating temperature, operating voltage, and signal strength in the collection device operating information. If any of these standards are not met, the collection device operating status is determined to be abnormal; otherwise, the collection device operating status is determined to be normal.

[0035] Furthermore, when the working status of the acquisition device is normal or the data transmission status is normal, no other operations need to be performed, and it is only necessary to continuously obtain multi-dimensional monitoring data. When the working status of the acquisition device is abnormal, a backup device is selected based on the abnormal acquisition device, and the backup device is controlled to collect monitoring data and obtain multi-dimensional monitoring data in real time. The activation of the backup device can ensure that the data collection work will not be interrupted, thereby maintaining the continuity of the data. When the data transmission status is abnormal, a pause transmission instruction is sent to the acquisition device that collects multi-dimensional monitoring data, and the backup data transmission channel is enabled; when the backup data transmission channel is successfully connected, a resume transmission instruction is sent to the acquisition device, and multi-dimensional monitoring data is obtained in real time, wherein the resume transmission instruction is used to control the acquisition device to send all data from the pause transmission moment to the current moment, so as to ensure the integrity and continuity of the multi-dimensional monitoring data. The introduction of backup devices and backup data transmission channels can enhance the redundancy of data acquisition, help improve the stability of data acquisition, and ensure the accuracy and integrity of multi-dimensional monitoring data.

[0036] It can be seen that in the embodiment of the present application, the target data transmission protocol is used to obtain monitoring data transmission information and collection device working information in real time, and based on the monitoring data transmission information and collection device working information, transmission status analysis and collection device status analysis are performed to determine the data transmission status and collection device working status. Furthermore, when the collection device working status is abnormal, a backup device is selected based on the abnormal collection device, and the backup device is controlled to collect monitoring data and obtain multi-dimensional monitoring data in real time; when the data transmission status is abnormal, a pause transmission instruction is sent to the collection device that collects multi-dimensional monitoring data, and a backup data transmission channel is enabled; when the backup data transmission channel is successfully connected, a resume transmission instruction is sent to the collection device, and multi-dimensional monitoring data is obtained in real time, wherein the resume transmission instruction is used to control the collection device to send all data from the pause transmission moment to the current moment, so as to ensure the integrity and continuity of the multi-dimensional monitoring data. In the process of obtaining multi-dimensional monitoring data in real time, transmission status analysis and collection device status analysis are performed to timely discover and solve the abnormalities in the data transmission or data collection stage, and ensure the accuracy and integrity of the multi-dimensional monitoring data.

[0037] Furthermore, in order to ensure the smooth progress of the factory production process, in the embodiment of the present application, the smart factory model is controlled to perform factory operation prediction and determine the operation prediction data, including: Obtain the forecast target, perform data screening based on the forecast target and multi-dimensional monitoring data, and determine the target monitoring data; Based on the smart factory model, target monitoring data and prediction targets, simulation is carried out to obtain full-dimensional prediction data. Based on the full-dimensional prediction data and prediction targets, key data is extracted to determine the operation prediction data.

[0038] For the embodiments of the present application, the digital twin's intelligent engineering model can predict the factory's production capacity, equipment failure risk, energy consumption trends, etc. in the future based on the factory's current multi-dimensional monitoring data, which helps the factory to reasonably allocate human resources, equipment resources and material resources to ensure the smooth progress of the factory's production process.

[0039] Specifically, a prediction target is obtained, and data is screened based on the prediction target and multi-dimensional monitoring data to determine target monitoring data. Prediction targets include, but are not limited to, production efficiency monitoring, production energy consumption monitoring, and equipment failure monitoring. The target monitoring data is selected from a large volume of multi-dimensional monitoring data to identify monitoring data that is highly relevant to the prediction target. This reduces the data processing load of subsequent simulation operations and improves the speed of factory operation prediction. Furthermore, a simulation is performed based on the smart factory model, the target monitoring data, and the prediction target to obtain full-dimensional prediction data. This full-dimensional prediction data is all prediction data related to the future development of the factory output by the smart factory model and is comprehensive and complete. The specific simulation process is as follows: Based on the target monitoring data, the data is cleaned, integrated, and formatted to ensure data accuracy and consistency. Initial simulation parameters and boundary conditions are set based on the prediction target and the characteristics of the smart factory model. The processed target monitoring data is then input into the smart factory model. The simulation program is started, and the smart factory model is controlled to simulate the operation of the smart factory over a period of time according to the set simulation parameters and boundary conditions. All data output by the smart factory model is recorded as full-dimensional prediction data.

[0040] Furthermore, key data is extracted based on the full-dimensional forecast data and the forecast target to determine operational forecast data. Specifically, the electronic device pre-stores key indicators corresponding to the forecast target. Therefore, key data extraction is performed based on the full-dimensional forecast data and the key indicators corresponding to the forecast target to determine operational forecast data. This operational forecast data is data that has a significant impact on the forecast target. This removes redundant data from the full-dimensional forecast data, improving its quality and usability. This key data extraction allows for a more accurate understanding of the smart factory's operational status, timely identification of potential problems and risks, and strong support for subsequent decision optimization and response development.

[0041] It can be seen that in the embodiment of the present application, a prediction target is obtained, and data screening is performed based on the prediction target and multi-dimensional monitoring data to determine the target monitoring data. Then, simulation is performed based on the smart factory model, target monitoring data and prediction target to obtain full-dimensional prediction data, and key data is extracted based on the full-dimensional prediction data and prediction target to determine the operation prediction data. The factory operation prediction operation is performed to predict the factory's production capacity, equipment failure risk, energy consumption trend, etc. in the future, which helps the factory to reasonably allocate human resources, equipment resources and material resources to ensure the smooth progress of the factory production process.

[0042] Furthermore, in order to promptly identify bottlenecks and inefficient links in the production process and improve the production efficiency of the factory, in the embodiment of the present application, intelligent control analysis of the smart factory is performed based on the operation prediction data to determine the intelligent control information, including: Obtaining the operation standard data corresponding to the predicted target, and performing operation monitoring based on the operation prediction data and the operation standard data to determine the operation monitoring results, wherein the operation monitoring results include: factors to be regulated and normal factors; Based on the target prediction data corresponding to the factors to be regulated, the smart factory is intelligently regulated and analyzed to determine the intelligent regulation information.

[0043] In the embodiments of the present application, intelligent control and analysis of the smart factory based on operational prediction data helps to promptly identify bottlenecks and inefficient links in the production process, thereby implementing targeted optimization and adjustments to improve the factory's production efficiency. Therefore, after performing operational predictions on the factory based on the prediction targets, intelligent control and analysis of the smart factory is performed based on the operational standard data to determine intelligent control information, so that the factory can reasonably manage equipment and human resources on the production line, avoid resource waste and idleness, and can promptly issue alarms when abnormal situations are discovered to avoid the occurrence of safety accidents.

[0044] Specifically, the electronic device pre-stores the operating standard data corresponding to each prediction target. The operating standard data is the operating standard data related to production efficiency, production energy consumption and equipment failure collected from the factory database, historical records or standard operation manuals. Then, operation monitoring is performed based on the operation prediction data and the operation standard data to determine the operation monitoring results, that is, each data item in the operation prediction data is matched with the operation standard data. The data items with successful matching are recorded as normal factors, and the data items with unsuccessful matching are recorded as factors to be regulated.

[0045] At the same time, the electronic device also pre-stores the correspondence between different abnormal operating data and intelligent control solutions. Therefore, based on the correspondence between the two and the target prediction data corresponding to the factors to be controlled, the smart factory is intelligently controlled and analyzed to determine intelligent control information. The intelligent control information includes but is not limited to: production plan adjustment information, equipment maintenance plans, energy consumption optimization strategies, staffing recommendations, inventory management recommendations, etc. The specific content of the intelligent control information is not limited in the embodiments of this application, and users can set it according to the actual operation of the factory.

[0046] As can be seen, in the embodiments of the present application, the operating standard data corresponding to the predicted target is obtained, and operation monitoring is performed based on the operation prediction data and the operating standard data to determine the operation monitoring results. Then, based on the target prediction data corresponding to the factors to be regulated, intelligent control analysis is performed on the smart factory to determine intelligent control information. Performing intelligent control analysis helps to promptly identify bottlenecks and inefficient links in the production process, thereby performing targeted optimization and adjustments to improve the factory's production efficiency.

[0047] Furthermore, in order to achieve simulation and prediction of the real world in a virtual environment and improve the production efficiency of the factory, in the embodiment of the present application, the factory operation data includes: historical environmental data, personnel data and equipment operation data in the factory. A three-dimensional model is built based on digital twin technology and factory operation data to obtain a smart factory model, including: Obtain a factory layout map and digital twin framework, perform initial 3D modeling based on the factory layout map, and obtain an initial factory model. The initial factory model is used to reflect the physical form and spatial relationships of the factory. Integrate the digital twin framework with the initial factory model to obtain a digital twin model; Obtain a data prediction algorithm, and add the data prediction algorithm and factory operation data to the digital twin model to obtain a smart factory model.

[0048] For the embodiments of the present application, digital twin technology can create a virtual model that is highly consistent with the real factory. Through three-dimensional modeling, it can accurately restore the physical layout, equipment structure, operating status, etc. of the factory, thereby realizing the simulation and prediction of the real world in a virtual environment, which helps the factory optimize production plans and perform predictive maintenance, thereby improving the factory's production efficiency.

[0049] Specifically, obtain the factory layout map and digital twin framework. The factory layout map is a drawing that shows the layout of various areas, equipment, production lines, etc. within the factory. It can be obtained through CAD drawings, architectural design drawings, or GIS data of the factory construction. The digital twin framework is the core foundation of 3D modeling. It integrates multiple technologies and methods for creating, managing, and maintaining a virtual model corresponding to the physical factory. The digital twin framework specifies the hierarchical structure of the twin model, data interaction methods, etc. The hierarchical structure of the twin model usually includes the equipment level, workshop level, and factory level. It is necessary to clarify the data flow and interaction methods between each level to ensure the accuracy and real-time nature of the information. In terms of data interaction methods, data exchange between physical entities and virtual models is specified, including the collection, transmission, storage, and processing of real-time data.

[0050] Then, use the basic geometry and modeling tools in the modeling software to perform three-dimensional geometric modeling based on the factory layout map to obtain a three-dimensional geometric model of the factory. On the basis of the three-dimensional geometric model, add detailed features of the equipment, such as pipeline connections, valve safety, instrument layout, etc., and finally obtain the initial factory model.

[0051] Furthermore, the digital twin framework is integrated with the initial factory model to obtain a digital twin model. That is, the factory layout data in the initial factory model is connected to the digital twin framework, and the connected factory layout data is fused to eliminate data redundancy and contradictions, and improve data accuracy and reliability; then, the geometric shape, physical properties and behavioral characteristics of the initial factory model are mapped to the digital twin framework to ensure that the virtual digital twin model can accurately reflect the status and behavior of the physical entity.

[0052] Finally, a data prediction algorithm is obtained, which is determined based on the specific needs and data characteristics of factory monitoring, including but not limited to: linear regression, logistic regression, decision tree, random forest, support vector machine, etc., and the data prediction algorithm and factory operation data are added to the digital twin model to obtain a smart factory model. Among them, adding data prediction algorithms and factory operation data helps the smart factory model to perform real-time data prediction based on factory operation data.

[0053] It can be seen that in the embodiment of the present application, a factory layout map and a digital twin framework are obtained, and initial three-dimensional modeling is performed based on the factory layout map to obtain an initial factory model. Then, the digital twin framework is integrated with the initial factory model to obtain a digital twin model. Furthermore, a data prediction algorithm is obtained, and the data prediction algorithm and factory operation data are added to the digital twin model to obtain a smart factory model. The constructed smart factory model realizes simulation and prediction of the real world in a virtual environment, which helps factories optimize production plans and perform predictive maintenance, thereby improving factory production efficiency.

[0054] Furthermore, in order to facilitate the staff to intuitively understand the factory's operating status and control needs, in the embodiment of the present application, the smart factory is intelligently controlled and analyzed based on the operation prediction data. After determining the intelligent control information, the following is also included: Use holographic projection technology to visualize the smart factory model in three dimensions and obtain a virtual three-dimensional image; Operational prediction data and intelligent control information are added to virtual 3D images, allowing workers to interactively view factory data at locations of interest.

[0055] For the embodiments of the present application, smart factories involve a large amount of operating data and control information, but these data are often complex and difficult to understand intuitively. In order to facilitate the staff to intuitively understand the operating status and control needs of the factory, these complex data and information are presented in a three-dimensional visual manner through holographic projection technology, so that the staff can more quickly discover problems and bottlenecks in the production process, and make timely adjustments and optimizations, which helps to improve production efficiency.

[0056] Specifically, holographic projection technology is based on the principles of interference and diffraction, and achieves three-dimensional visualization by recording and reproducing the real three-dimensional image of the object. Therefore, the hardware equipment required for holographic projection, such as holographic projection equipment, laser transmitters, holographic negatives, etc., is pre-deployed, and the quality and performance of the equipment are ensured to meet the requirements of three-dimensional visualization of the smart factory model. Then, the constructed smart factory model is imported into the holographic projection equipment, and the projection parameters are adjusted to ensure that the three-dimensional smart factory model can be clearly presented in the holographic projection and a virtual three-dimensional image is obtained. Furthermore, the operation prediction data and intelligent control information are added to the virtual three-dimensional image to ensure that the data in the virtual three-dimensional image is updated synchronously with the actual data. Of course, interactive functions can also be set in the virtual three-dimensional image, such as clicking, dragging, zooming, etc., to ensure that staff can view the factory data of the location of interest in an interactive way.

[0057] As can be seen, in this embodiment of the application, in order to facilitate workers' intuitive understanding of the factory's operating status and control requirements, holographic projection technology is used to perform a three-dimensional visualization of the smart factory model, generating a virtual three-dimensional image. Operational prediction data and intelligent control information are then added to the virtual three-dimensional image, allowing workers to interactively view factory data at locations of interest.

[0058] The above embodiment introduces a smart factory control method based on digital twins from the perspective of method flow. The following embodiment introduces a smart factory control system based on digital twins from the perspective of virtual modules or virtual units. For details, please see the following embodiment.

[0059] The embodiment of the present application provides a smart factory control system based on digital twins, such as Figure 2 As shown, the digital twin-based smart factory control system can specifically include: A three-dimensional model building module 210 is used to extract factory operation data for factory model building from a factory historical data set, and to build a three-dimensional model based on digital twin technology and factory operation data to obtain a smart factory model; The factory operation prediction module 220 is used to obtain multi-dimensional monitoring data in real time, input the real-time multi-dimensional monitoring data into the smart factory model, and control the smart factory model to perform factory operation prediction and determine operation prediction data, wherein the multi-dimensional monitoring data includes: environmental monitoring data, personnel monitoring data, and equipment monitoring data; The intelligent control analysis module 230 is used to perform intelligent control analysis on the smart factory based on the operation prediction data, determine the intelligent control information, and send the intelligent control information to the target terminal, which helps to improve the response speed to abnormal conditions of the smart factory.

[0060] In one possible implementation of the embodiment of the present application, the plant operation prediction module 220, when performing real-time acquisition of multi-dimensional monitoring data, is configured to: Utilize the target data transmission protocol to obtain monitoring data transmission information and acquisition equipment working information in real time, and perform transmission status analysis and acquisition equipment status analysis based on the monitoring data transmission information and acquisition equipment working information to determine the data transmission status and acquisition equipment working status; When the working status of the collection device is abnormal, a backup device is selected based on the abnormal collection device, and the backup device is controlled to collect monitoring data and obtain multi-dimensional monitoring data in real time; When the data transmission status is abnormal, a pause transmission instruction is sent to the collection device that collects multi-dimensional monitoring data, and a backup data transmission channel is enabled; When the backup data transmission channel is connected successfully, a resume sending instruction is sent to the acquisition device, and multi-dimensional monitoring data is obtained in real time. The resume sending instruction is used to control the acquisition device to send all data from the time of suspension of sending to the current time to ensure the integrity and continuity of the multi-dimensional monitoring data.

[0061] In one possible implementation of the embodiment of the present application, the plant operation prediction module 220, when executing the control smart factory model to perform plant operation prediction and determining operation prediction data, is configured to: Obtain the forecast target, perform data screening based on the forecast target and multi-dimensional monitoring data, and determine the target monitoring data; Based on the smart factory model, target monitoring data and prediction targets, simulation is carried out to obtain full-dimensional prediction data. Based on the full-dimensional prediction data and prediction targets, key data is extracted to determine the operation prediction data.

[0062] In one possible implementation of the embodiment of the present application, when the intelligent control analysis module 230 performs intelligent control analysis on the smart factory based on the operation prediction data and determines the intelligent control information, it is used to: Obtaining the operation standard data corresponding to the predicted target, and performing operation monitoring based on the operation prediction data and the operation standard data to determine the operation monitoring results, wherein the operation monitoring results include: factors to be regulated and normal factors; Based on the target prediction data corresponding to the factors to be regulated, the smart factory is intelligently regulated and analyzed to determine the intelligent regulation information.

[0063] In one possible implementation of the embodiment of the present application, the three-dimensional model building module 210, when executing the three-dimensional model building based on the digital twin technology and the factory operation data to obtain the smart factory model, is used to: Obtain a factory layout map and digital twin framework, perform initial 3D modeling based on the factory layout map, and obtain an initial factory model. The initial factory model is used to reflect the physical form and spatial relationships of the factory. Integrate the digital twin framework with the initial factory model to obtain a digital twin model; Obtain a data prediction algorithm, and add the data prediction algorithm and factory operation data to the digital twin model to obtain a smart factory model.

[0064] A possible implementation of the embodiment of the present application further includes: A holographic projection module is used to visualize the smart factory model in three dimensions using holographic projection technology to obtain a virtual three-dimensional image; Operational prediction data and intelligent control information are added to virtual 3D images, allowing workers to interactively view factory data at locations of interest.

[0065] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working process of the digital twin-based smart factory control system described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0066] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 The electronic device 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in actual applications, the number of transceivers 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0067] Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0068] Bus 302 may include a path for transmitting information between the above components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or one type of bus.

[0069] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0070] The memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0071] Electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. They may also include servers, etc. Figure 3 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0072] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0073] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method in any of the above embodiments is implemented.

[0074] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0075] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A smart factory control method based on digital twins, characterized in that: include: Extracting factory operation data for building a factory model from a factory historical data set, and building a three-dimensional model based on digital twin technology and the factory operation data to obtain a smart factory model; Acquire multi-dimensional monitoring data in real time, input the multi-dimensional monitoring data acquired in real time into the smart factory model, and control the smart factory model to perform factory operation prediction and determine operation prediction data, wherein the multi-dimensional monitoring data includes: environmental monitoring data, personnel monitoring data and equipment monitoring data; Based on the operation prediction data, intelligent control analysis is performed on the smart factory, intelligent control information is determined, and the intelligent control information is sent to the target terminal, which helps to improve the response speed to abnormal conditions of the smart factory.

2. The digital twin-based smart factory control method according to claim 1 is characterized in that: The real-time acquisition of multi-dimensional monitoring data includes: Using the target data transmission protocol, monitoring data transmission information and collection equipment working information are acquired in real time, and based on the monitoring data transmission information and the collection equipment working information, transmission status analysis and collection equipment status analysis are performed to determine the data transmission status and collection equipment working status; When the working state of the acquisition device is abnormal, a backup device is selected based on the abnormal acquisition device, the backup device is controlled to collect monitoring data, and the multi-dimensional monitoring data is obtained in real time; When the data transmission state is abnormal, a pause transmission instruction is sent to a collection device that collects the multi-dimensional monitoring data, and a backup data transmission channel is enabled; When the backup data transmission channel is connected successfully, a resume sending instruction is sent to the acquisition device, and the multi-dimensional monitoring data is acquired in real time, wherein the resume sending instruction is used to control the acquisition device to send all data from the time of suspension of sending to the current time, so as to ensure the integrity and continuity of the multi-dimensional monitoring data.

3. The digital twin-based smart factory control method according to claim 1, characterized in that: The controlling the smart factory model to perform factory operation prediction and determine operation prediction data includes: Acquire a prediction target, perform data screening based on the prediction target and the multi-dimensional monitoring data, and determine target monitoring data; A simulation is performed based on the smart factory model, the target monitoring data and the prediction target to obtain full-dimensional prediction data, and key data is extracted based on the full-dimensional prediction data and the prediction target to determine the operation prediction data.

4. The digital twin-based smart factory control method according to claim 3 is characterized in that: The intelligent control analysis of the smart factory based on the operation prediction data to determine the intelligent control information includes: Acquire the operation standard data corresponding to the prediction target, and perform operation monitoring based on the operation prediction data and the operation standard data to determine the operation monitoring result, wherein the operation monitoring result includes: factors to be regulated and normal factors; Based on the target prediction data corresponding to the factors to be regulated, intelligent regulation analysis is performed on the smart factory to determine the intelligent regulation information.

5. The digital twin-based smart factory control method according to claim 1, characterized in that: The factory operation data includes: historical environmental data, personnel data and equipment operation data in the factory. The three-dimensional model is constructed based on the digital twin technology and the factory operation data to obtain a smart factory model, including: Obtaining a factory layout map and a digital twin framework, and performing initial three-dimensional modeling based on the factory layout map to obtain an initial factory model, wherein the initial factory model is used to reflect the physical form and spatial relationship of the factory; Based on the digital twin framework, the digital twin model is integrated with the initial plant model to obtain a digital twin model; Acquire a data prediction algorithm, and add the data prediction algorithm and the factory operation data to the digital twin model to obtain the smart factory model.

6. The digital twin-based smart factory control method according to claim 1, characterized in that: After performing intelligent control analysis on the smart factory based on the operation prediction data and determining the intelligent control information, the method further includes: Using holographic projection technology to perform three-dimensional visualization of the smart factory model to obtain a virtual three-dimensional image; The operation prediction data and the intelligent control information are added to the virtual three-dimensional image so that the staff can view the factory data of the location of interest in an interactive manner.

7. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the digital twin-based smart factory control method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the digital twin-based smart factory control method as described in any one of claims 1 to 6.

9. A computer program product, characterized in that It includes a computer program, and the computer program is executed by a processor to implement the digital twin-based smart factory control method as described in any one of claims 1 to 6.

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