Production data processing method and device based on digital twinning technology, and medium

Abstract

The invention provides a production data processing method and device based on a digital twin model, equipment and a medium, and the method comprises the steps: collecting data on a production line in real time through employing an Internet of Things technology, and transmitting the data to a data center; processing and analyzing the collected data by using big data analysis and machine learning; constructing a digital twinborn model corresponding to the production line, and adjusting and updating the digital twinborn model according to the collected data; based on analysis of historical operation data and simulation results of a digital twinborn model, a production line is dynamically managed and controlled, so that the problems that in the production field of automation and informatization, data among production links are isolated, large-scale real-time data cannot be efficiently processed, an updating mechanism is not flexible enough, and the production efficiency is high are solved. And changes of production conditions cannot be reflected in time.

Description

Technical Field

[0001] The present invention relates to the technical field of production automation and informatization, and particularly to a production data processing method, device, equipment and medium based on digital twin technology. Background Art

[0002] In the field of building materials production, the traditional production mode has long dominated. In the past, the production process mainly relied on the manual experience of workers and manual operations. For example, in cement production, the determination of the mixing ratio of raw materials such as limestone and clay mostly relied on the subjective judgment of workers and the accumulation of past experience. The control of temperature and time in the firing process was also set by operators based on visual observation and experience. In this mode, key parameters such as the particle size distribution of raw materials, the heat transfer efficiency during the reaction process, and the internal pressure change of equipment in the production process could not be monitored and controlled in real time and accurately. This brought a series of drawbacks. The stability of product quality was difficult to guarantee, and there were large fluctuations in performance indicators such as strength and density of products in different batches. The production efficiency was difficult to be greatly improved, and due to the lack of precise regulation, the phenomena of energy consumption and raw material waste were relatively prominent.

[0003] In recent years, automation and informatization technologies have gradually been applied in the field of building materials production. In terms of automation, some production equipment has achieved automated operation, reducing the intensity and workload of direct manual operation. For example, the automated material conveying system has improved the material flow efficiency; informatization technology has enabled some production data to be recorded and stored. For example, production order management, inventory management and other links have become more orderly with the help of informatization systems. However, most of the currently introduced automation and informatization systems still have many defects. First, there are loopholes in the data collection link. The layout of sensors for collecting many key parameters is not reasonable enough or the accuracy is insufficient, resulting in poor data accuracy and unable to fully reflect the real production state. Second, the data processing ability is limited. Most systems lack advanced data analysis models and algorithms, and it is difficult to deeply mine and analyze massive production data, and unable to provide accurate and effective decision-making basis for production process optimization, equipment maintenance, etc. Third, the system integration is poor. There are often problems such as poor data interaction and insufficient compatibility between automated equipment and informatization systems, and it is difficult to form an efficient and collaborative production control system.

[0004] Therefore, it is urgent to propose a production data processing method based on digital twin technology to solve the problems of data isolation between production links in automation and informatization in the production field, inability to efficiently process large-scale real-time data, and inflexible update mechanism, which cannot timely reflect changes in production conditions. Summary of the Invention

[0005] To overcome the problems existing in the related technologies, the present disclosure provides a production data processing method, apparatus, device, and medium based on digital twin technology to solve the technical problems of data isolation between production links, inability to efficiently process large-scale real-time data, and inflexible update mechanism, which cannot timely reflect changes in production conditions in the related technologies.

[0006] One or more embodiments of this specification provide a production data processing method based on digital twin technology, including the following steps:

[0007] Collect data on the production line in real time using Internet of Things technology and transmit it to the data center;

[0008] Process and analyze the collected data using big data analysis and machine learning;

[0009] Construct a digital twin model corresponding to the production line and adjust and update the digital twin model according to the collected data;

[0010] Based on the analysis of historical operation data and the simulation results of the digital twin model, dynamically manage and control the production line.

[0011] Preferably, it includes the following steps:

[0012] Collect data on the production line in real time using Internet of Things technology and transmit it to the data center;

[0013] Process and analyze the collected data using big data analysis and machine learning;

[0014] Construct a digital twin model corresponding to the production line and adjust and update the digital twin model according to the collected data;

[0015] Based on the analysis of historical operation data and the simulation results of the digital twin model, dynamically manage and control the production line.

[0016] Preferably, the step of collecting data on the production line in real time using Internet of Things technology and transmitting it to the data center specifically includes the following steps:

[0017] Install high-precision sensors and Internet of Things devices at key points on the production line to collect data on the production line in real time;

[0018] Transmit the data to the data center using data encryption technology and security protection measures.

[0019] Preferably, the step of dynamically managing and controlling the production line specifically includes the following steps:

[0020] Through a feedback control system, adjust the production parameters of the production line according to real-time data;

[0021] Convert the data on the production line into intuitive graphs and charts, display the operating status of the production line through a graphical interface, monitor production data in real time, and perform anomaly alarms.

[0022] One or more embodiments of this specification provide a production data processing device based on digital twin technology, including a data acquisition module, a data processing module, a model construction module, and a control module;

[0023] The data acquisition module is used to collect the data on the production line in real time using Internet of Things technology and transmit it to the data center;

[0024] The data processing module is used to process and analyze the collected data using big data analysis and machine learning;

[0025] The model construction module is used to construct a digital twin model corresponding to the production line, and adjust and update the digital twin model according to the collected data;

[0026] The control module is used to dynamically manage and control the production line based on the analysis of historical operation data and the simulation results of the digital twin model.

[0027] Preferably, the data processing module includes a data cleaning unit, an integration unit, an analysis unit, and a prediction unit;

[0028] The data cleaning unit is used to preprocess the collected data, removing noise and invalid data;

[0029] The integration unit is used to integrate data from different sources to form a unified data format;

[0030] The analysis unit is used to deeply analyze the integrated data using big data analysis technology to extract effective information;

[0031] The prediction unit is used to establish a prediction model through machine learning algorithms to identify abnormal data and optimization points.

[0032] Preferably, the data acquisition module includes a collection unit and a transmission unit;

[0033] The collection unit is used to install high-precision sensors and Internet of Things devices at various key points on the production line to collect the data on the production line in real time;

[0034] The transmission unit is used to transmit the data to the data center using data encryption technology and security protection measures.

[0035] Preferably, the control module includes a control unit and a management unit;

[0036] The control unit is used to adjust the production parameters of the production line according to real-time data through a feedback control system;

[0037] The management unit is used to convert the data on the production line into intuitive graphs and charts, display the operating status of the production line through a graphical interface, monitor the production data in real time, and give out abnormal alarms.

[0038] One or more embodiments of this specification provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the production data processing method based on digital twin technology as described above.

[0039] One or more embodiments of this specification provide a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, it implements the steps of the production data processing method based on digital twin technology as described above.

[0040] A production data processing method, device, equipment, and medium based on digital twin technology provided by the present disclosure have the advantages that by using the Internet of Things technology to collect data on the production line in real time and transmit it to the data center, rich and diverse key data on the production line can be obtained with high frequency and high precision; the collected data is processed and analyzed using big data analysis and machine learning to deeply explore the hidden information and rules in the data, and abnormal situations in the production process can be quickly identified; a digital twin model corresponding to the production line is constructed, which is synchronized with the production line in real time and accurately reflects the actual state and operating conditions of the production line. The digital twin model is adjusted and updated according to the collected data to always maintain a high degree of accuracy and effectiveness; based on the analysis of historical operation data and the simulation results of the digital twin model, the production line is dynamically managed and controlled, and the consumption of energy in each production link can be accurately mastered, and a more scientific and reasonable equipment maintenance plan can be formulated. The production data processing method based on digital twin technology can comprehensively improve the intelligent level of the production process, bring significant technical effects in improving product quality, enhancing production efficiency, optimizing equipment maintenance management, and reducing costs, win advantages for enterprises in the fierce market competition, and promote the industry to develop in the direction of digitalization and intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 A schematic flowchart of a production data processing method provided for one or more embodiments of this specification;

[0043] Figure 2 A schematic diagram of a digital twin management and control platform provided for one or more embodiments of this specification;

[0044] Figure 3 A schematic structural diagram of a production data processing device based on digital twin technology provided for one or more embodiments of this specification;

[0045] Figure 4 A schematic structural diagram of a computer device provided for one or more embodiments of this specification. Specific embodiments

[0046] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will clearly and completely describe the technical solutions in one or more embodiments of this specification with reference to the accompanying drawings in one or more embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this invention document.

[0047] The following will make a detailed description of the present invention in combination with the specific embodiments and the accompanying drawings of the specification.

[0048] Method embodiments

[0049] According to an embodiment of the present invention, a production data processing method based on digital twin technology is provided. As Figure 1 shown, it is a schematic flowchart of the production data processing method based on digital twin technology provided for this embodiment. The production data processing method based on digital twin technology according to the embodiment of the present invention includes the following steps:

[0050] S110. Use Internet of Things technology to collect data on the production line in real time and transmit it to the data center.

[0051] S120. Use big data analysis and machine learning to process and analyze the collected data.

[0052] S130. Based on the physical model and / or behavior model in digital twin technology, construct a digital twin model corresponding to the production line, and adjust and update the digital twin model according to the collected data.

[0053] S140. Dynamically manage and control the production line based on the analysis of historical operation data and simulation results of the digital twin model.

[0054] The method provided in this embodiment can collect data on the production line in real time by using Internet of Things technology and transmit it to the data center, enabling the acquisition of rich and diverse key data on the production line with high frequency and high precision; use big data analysis and machine learning to process and analyze the collected data, and deeply mine the hidden information and rules in the data. It can quickly identify abnormal situations in the production process; construct a digital twin model corresponding to the production line, synchronize it with the production line in real time, accurately reflect the actual state and operation of the production line, adjust and update the digital twin model according to the collected data to keep it highly accurate and effective at all times; based on the analysis of historical operation data and simulation results of the digital twin model, dynamically manage and control the production line, accurately master the energy consumption in each production link, and formulate a more scientific and reasonable equipment maintenance plan. This production data processing method based on digital twin technology can comprehensively improve the intelligent level of the production process, bring significant technical effects in improving product quality, enhancing production efficiency, optimizing equipment maintenance management, and reducing costs, win advantages for enterprises in the fierce market competition, and promote the industry to develop in the direction of digitalization and intelligentization.

[0055] In one embodiment, using big data analysis and machine learning to process and analyze the collected data specifically includes the following steps:

[0056] Data cleaning: Preprocess the collected data to remove noise and invalid data.

[0057] Data integration: Integrate data from different sources to form a unified data format.

[0058] Big data analysis: Use big data analysis technologies such as Hadoop (an open-source distributed computing platform mainly composed of the Hadoop Distributed File System (HDFS) and the MapReduce computing model) and Spark (a fast and general big data processing engine whose core is the Resilient Distributed Dataset) to deeply analyze the integrated data, which can process large-scale real-time data and extract effective information.

[0059] Machine learning: Establish a prediction model through machine learning algorithms to identify abnormal data and optimization points. Specifically, establish a prediction model through machine learning algorithms, use historical data to train the prediction model, identify abnormal situations and optimization points in the production process, and predict the failure probability and maintenance requirements of equipment.

[0060] Artificial intelligence algorithms such as deep learning and reinforcement learning can also be used to further improve the accuracy and efficiency of data analysis.

[0061] The method provided in this embodiment can accurately process production data, effectively remove noise and invalid data, integrate multi-source data into a unified format, deeply mine valuable information, build a prediction model to accurately identify abnormal data and optimization points, thereby significantly improving the controllability and stability of the production process, optimizing the production process, improving product quality, reducing production risks and costs, and enhancing the intelligent decision-making ability and overall operation efficiency of the production system.

[0062] In one embodiment, the Internet of Things technology is used to collect data on the production line in real time and transmit it to the data center. The specific steps are as follows:

[0063] High-precision sensors and Internet of Things devices are installed at various key points on the production line to collect data on the production line in real time. Among them, the high-precision sensors are used to collect various data on the production line, such as temperature, humidity, pressure, material composition, etc. RFID (Radio Frequency Identification) tags are used to identify and track materials and products in the production process. Cameras are used for visual inspection and monitoring of abnormal situations in the production process.

[0064] The data is transmitted to the data center or cloud platform through wired networks such as Ethernet or wireless networks such as Wi-Fi, 5G, and LoRa to improve the speed and reliability of data transmission. Encryption technologies such as AES (Advanced Encryption Standard, symmetric encryption algorithm) and RSA (Rivest-Shamir-Adleman, asymmetric encryption algorithm) are used to protect the security of the data during transmission. Through security protection measures such as firewalls and intrusion detection systems, unauthorized access and attacks are prevented to ensure the stability and security of the system.

[0065] The method provided in this embodiment realizes real-time and accurate data collection by installing relevant equipment at key points on the production line, and uses encryption and protection measures to securely transmit it to the data center, ensuring the accuracy and security of the data, providing a reliable basis for subsequent production analysis, decision-making, and control, and helping to improve the overall stability and intelligent level of production.

[0066] In one embodiment, the production line is dynamically managed and controlled. The specific steps are as follows:

[0067] Through a feedback control system, the production parameters of the production line are adjusted according to real-time data to achieve closed-loop control, ensuring the stability and efficiency of the production process. At the same time, technologies such as adaptive control and fuzzy control can be used to achieve more refined control effects.

[0068] Convert the data on the production line into intuitive graphs and charts, and implement a graphical interface using technologies such as VUE and JavaScript to display the operating status of the production line. Augmented reality and virtual reality technologies can also be used to provide a more intuitive visualization experience, monitor production data in real time, and perform anomaly alarms.

[0069] The method provided in this embodiment can accurately adjust production parameters based on real-time data, visually present the operating status of the production line with visual graphs and charts, achieve efficient real-time monitoring and timely anomaly alarms, effectively improve the accuracy, stability, and transparency of the production line operation, enhance the production process control ability, quickly respond to production changes, and improve production efficiency and product quality.

[0070] As Figure 2 shown, it is a schematic diagram of the digital twin control platform provided in this embodiment. The digital twin control platform includes an IoT platform, a data platform, an application platform, and a BI (Business Intelligence) platform. The following is a detailed description of each platform:

[0071] 1. IoT Platform

[0072] The IoT platform is connected downward to IoT acquisition terminals / sensors, DCS systems / PLC systems, third-party information systems, etc., to form the data acquisition base of the entire system. Facing the actual production process of this project, the IoT platform mainly integrates data related to seven major subsystems: raw material warehouse system, crushing workshop system, aging warehouse system, forming workshop system, kiln workshop system, packaging workshop system, and environmental protection system, etc. In addition, the IoT platform also integrates data of scattered individual equipment and affiliated systems.

[0073] 2. Data Platform

[0074] The data platform aggregates the data integrated by the IoT platform and builds data models such as digital twin devices, digital twin production lines, and digital twin factories according to the digital twin system. The data platform integrates various algorithm libraries required by the data model, including algorithms for data processing, spectrum analysis, machine learning, etc. The data platform provides corresponding data services for the integration platform, and the data services are grouped according to the structure of the digital twin model.

[0075] 3. Application Platform

[0076] The application platform completes the modular integration of many application functions. These applications include function modules such as energy management, safety management, environmental protection management, production management, inventory management, and equipment management, and can be continuously extended. The integration platform is also responsible for integrating advanced application services such as identification and resolution application systems and equipment predictive maintenance systems. These services are provided through WeChat / DingTalk, mobile APPs, etc.

[0077] 4. BI Platform

[0078] The BI platform is the visualization main body of the whole system, and is presented in the way of integrating 3D digital twin with BI pages. The BI platform also maps the scattered DCS monitoring system / PLC system and video monitoring, etc. to the cyber space and integrates them for presentation.

[0079] Device Embodiment

[0080] According to an embodiment of the present invention, a production data processing device based on digital twin technology is provided. As Figure 3 shown, it is a schematic structural diagram of the production data processing device based on digital twin technology provided by this embodiment. The production data processing device based on digital twin technology according to an embodiment of the present invention includes a data acquisition module 31, a data processing module 32, a model construction module 33 and a control module 34.

[0081] The data acquisition module 31 is used to collect data on the production line in real time by using Internet of Things technology and transmit it to the data center.

[0082] The data processing module 32 is used to process and analyze the collected data by using big data analysis and machine learning.

[0083] The model construction module 33 is used to construct a digital twin model corresponding to the production line and adjust and update the digital twin model according to the collected data.

[0084] The control module 34 is used to dynamically manage and control the production line based on the analysis of the historical operation data and the simulation results of the digital twin model.

[0085] The device provided in this embodiment, the data acquisition module 31 uses Internet of Things technology to collect data on the production line in real time and transmit it to the data center, and can obtain rich and diverse key data on the production line with high frequency and high precision; the data processing module 32 uses big data analysis and machine learning to process and analyze the collected data, and deeply excavates the hidden information and rules in the data. It can quickly identify abnormal situations in the production process; the model construction module 33 constructs a digital twin model corresponding to the production line, synchronizes with the production line in real time, accurately reflects the actual state and operation of the production line, and adjusts and updates the digital twin model according to the collected data to keep it highly accurate and effective all the time; the control module 34 dynamically manages and controls the production line based on the analysis of the historical operation data and the simulation results of the digital twin model, can accurately master the energy consumption in each production link, and formulate a more scientific and reasonable equipment maintenance plan. This production data processing method based on digital twin technology can comprehensively improve the intelligent level of the production process, bring significant technical effects in improving product quality, enhancing production efficiency, optimizing equipment maintenance management and reducing costs, win advantages for enterprises in the fierce market competition, and promote the industry to develop towards digitalization and intelligentization.

[0086] In one embodiment, the data processing module 32 includes a data cleaning unit, an integration unit, an analysis unit, and a prediction unit.

[0087] The data cleaning unit is used to preprocess the collected data and remove noise and invalid data.

[0088] The integration unit is used to integrate data from different sources to form a unified data format.

[0089] The analysis unit is used to deeply analyze the integrated data using big data analysis technology and extract effective information.

[0090] The prediction unit is used to establish a prediction model through machine learning algorithms to identify abnormal data and optimization points.

[0091] The device provided in this embodiment can accurately process production data, effectively remove noise and invalid data, integrate multi-source data into a unified format, deeply excavate valuable information, construct a prediction model to accurately identify abnormal data and optimization points, thereby significantly improving the controllability and stability of the production process, optimizing the production process, improving product quality, reducing production risks and costs, and enhancing the intelligent decision-making ability and overall operation efficiency of the production system.

[0092] In one embodiment, the data acquisition module 31 includes a collection unit and a transmission unit.

[0093] The acquisition unit is used to install high-precision sensors and Internet of Things devices at various key points on the production line to collect data on the production line in real time.

[0094] The transmission unit is used to transmit the data to the data center by using data encryption technology and security protection measures.

[0095] The device provided in this embodiment realizes real-time and accurate data acquisition by installing relevant devices at key points on the production line, and uses encryption and protection measures to securely transmit the data to the data center, ensuring the accuracy and security of the data, providing a reliable basis for subsequent production analysis, decision-making and control, and helping to improve the overall stability and intelligent level of production.

[0096] In one embodiment, the control module 34 includes a control unit and a management unit.

[0097] The control unit is used to adjust the production parameters of the production line according to the real-time data through a feedback control system.

[0098] The management unit is used to convert the data on the production line into intuitive graphs and charts, display the operating status of the production line through a graphical interface, monitor the production data in real time and give an alarm for abnormalities.

[0099] The device provided in this embodiment can accurately adjust the production parameters according to the real-time data, intuitively present the operating status of the production line with visual graphs and charts, realize efficient real-time monitoring and timely alarm for abnormalities, effectively improve the accuracy, stability and transparency of the production line operation, enhance the production process control ability, quickly respond to production changes, and improve production efficiency and product quality.

[0100] The embodiment of the present invention is a device embodiment corresponding to the above method embodiment. The specific operations of each module processing step can be understood with reference to the description of the method embodiment, and will not be elaborated here.

[0101] As Figure 4 shown, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the production data processing method in the above embodiment, or when the computer program is executed by a processor, it implements the production data processing method based on digital twin technology in the above embodiment.

[0102] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0103] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the device or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description of the method embodiments. The device and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and the content not described in detail in the specification of the present invention belongs to the well-known technology in the art.

Claims

1. A production data processing method based on digital twin technology, characterized in that: The following steps are involved: Use IoT technology to collect data on the production line in real time and transmit it to the data center; Use big data analysis and machine learning to process and analyze the collected data; Constructing a digital twin model corresponding to the production line, and adjusting and updating the digital twin model according to the collected data; Dynamically manage and control production lines based on analysis of historical operating data and simulation results of digital twin models.

2. The production data processing method based on digital twin technology according to claim 1, characterized in that: The use of big data analysis and machine learning to process and analyze the collected data specifically includes the following steps: Preprocess the collected data to remove noise and invalid data; Integrate data from different sources into a unified data format; Use big data analysis technology to conduct in-depth analysis of the integrated data and extract effective information; Build predictive models through machine learning algorithms to identify abnormal data and optimization points.

3. The production data processing method based on digital twin technology according to claim 1, characterized in that: The method of using the Internet of Things technology to collect data on the production line in real time and transmit it to the data center specifically includes the following steps: Install high-precision sensors and IoT devices at key points of the production line to collect data on the production line in real time; Data encryption technology and security protection measures are used to transmit data to the data center.

4. The production data processing method based on digital twin technology according to claim 1, characterized in that: The dynamic management and control of the production line specifically includes the following steps: Through the feedback control system, the production parameters of the production line are adjusted according to real-time data; Convert the data on the production line into intuitive graphics and charts, display the operating status of the production line through a graphical interface, monitor production data in real time and issue abnormal alarms.

5. A production data processing device based on digital twin technology, characterized in that: It includes a data acquisition module, a data processing module, a model building module and a control module; The data acquisition module is used to collect data on the production line in real time using the Internet of Things technology and transmit it to the data center; The data processing module is used to process and analyze the collected data using big data analysis and machine learning; The model building module is used to build a digital twin model corresponding to the production line, and adjust and update the digital twin model according to the collected data; The control module is used to dynamically manage and control the production line based on the analysis of historical operation data and simulation results of the digital twin model.

6. The production data processing device based on digital twin technology according to claim 5, characterized in that: The data processing module includes a data cleaning unit, an integration unit, an analysis unit and a prediction unit; The data cleaning unit is used to pre-process the collected data to remove noise and invalid data; The integration unit is used to integrate data from different sources to form a unified data format; The analysis unit is used to use big data analysis technology to perform in-depth analysis on the integrated data and extract effective information; The prediction unit is used to establish a prediction model through a machine learning algorithm to identify abnormal data and optimization points.

7. The production data processing device based on digital twin technology according to claim 5, characterized in that: The data acquisition module includes a collection unit and a transmission unit; The acquisition unit is used to install high-precision sensors and Internet of Things devices at various key points of the production line to collect data on the production line in real time; The transmission unit is used to transmit data to the data center using data encryption technology and security protection measures.

8. The production data processing device based on digital twin technology according to claim 5, characterized in that: The control module includes a control unit and a management unit; The control unit is used to adjust the production parameters of the production line according to real-time data through a feedback control system; The management unit is used to convert the data on the production line into intuitive graphics and charts, display the operating status of the production line through a graphical interface, monitor the production data in real time and issue abnormal alarms.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the production data processing method based on the digital twin technology as described in any one of claims 1 to 4.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the production data processing method based on digital twin technology as described in any one of claims 1 to 4 are implemented.

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