Digital twinborn simulation optimization system based on deep learning

Abstract

The invention provides a digital twin simulation optimization system based on deep learning, and relates to the technical field of computers. The digital twinborn simulation optimization system based on deep learning comprises a data acquisition layer, the data acquisition layer is connected with a data processing layer, the data processing layer is connected with a digital twinborn model layer, the digital twinborn model layer is connected with a deep learning layer, the deep learning layer is connected with a simulation and optimization layer, and the simulation and optimization layer is connected with a digital twinborn model layer. The simulation and optimization layer is connected with a business application layer, and the business application layer is connected with a security and management layer. According to the method, delay is reduced through real-time data processing and edge calculation, accuracy is ensured through a digital twinborn model, accurate optimization suggestions are provided through deep learning, manual intervention is reduced, system performance and expansibility are remarkably improved, and complex requirements are met.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a digital twin simulation optimization system based on deep learning. Background Art

[0002] Existing simulation optimization technologies are mainly implemented through steps such as data collection, data processing, model construction, simulation operation and optimization suggestions. First, the real-time data of the system, including equipment status, environmental parameters, performance indicators, etc., are collected through sensors and industrial equipment. Then, the collected data is preprocessed, such as cleaning, normalization and preliminary analysis, to ensure the accuracy and consistency of the data. Next, a simulation model of the system is constructed using traditional mathematical models or physics-based models (such as finite element analysis models), and the model is corrected and verified through historical data and a small amount of real-time data to improve the accuracy of the model. Subsequently, the constructed model is run using simulation software to simulate the operating status of the system under different conditions, and the performance and efficiency of the system are evaluated by setting different parameters and operating scenarios. Finally, optimization suggestions are put forward based on the simulation results, such as adjusting equipment parameters, optimizing processes, etc., and the optimization suggestions are implemented through manual intervention or automated control systems.

[0003] Existing technologies have several shortcomings when implementing simulation optimization. First, data dependence is strong. If the data is insufficient or inaccurate, the prediction and optimization effects of the model will be greatly reduced. Secondly, the real-time performance is poor. The data preprocessing and model correction process is time-consuming, and it is difficult to cope with real-time changes in system status. In addition, the operation of traditional simulation models usually requires a lot of computing resources, especially in complex systems, where the computing cost is high. Model complexity is also a problem. Building and maintaining high-precision simulation models is relatively complex and requires professional modeling and simulation technology, which increases the technical threshold and maintenance costs. Finally, the implementation of optimization suggestions usually requires manual intervention, which not only increases the complexity of the operation, but also may introduce human errors. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a digital twin simulation optimization system based on deep learning, which solves the problem of high cost of the prior art.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a digital twin simulation and optimization system based on deep learning, comprising a data acquisition layer, the data acquisition layer is connected to a data processing layer, the data processing layer is connected to a digital twin model layer, the digital twin model layer is connected to a deep learning layer, the deep learning layer is connected to a simulation and optimization layer, the simulation and optimization layer is connected to a business application layer, and the business application layer is connected to a security and management layer.

[0006] Preferably, the data acquisition layer includes sensors, industrial equipment and edge computing devices, which are used to collect data from the physical system in real time and transmit it to the data processing layer through a data gateway.

[0007] Preferably, the data processing layer includes data cleaning, data storage and data stream processing modules for cleaning and preprocessing data, storing historical and real-time data, and performing real-time data stream processing.

[0008] Preferably, the digital twin model layer includes virtual modeling, model correction and model optimization modules, which are used to build a virtual model of the physical system, correct the model through real-time data, and use deep learning technology to optimize the prediction accuracy and response speed of the model.

[0009] Preferably, the deep learning layer includes data set management, model training and model deployment modules, which are used to manage training data sets, train deep learning models, and deploy them to the cloud or edge devices.

[0010] Preferably, the simulation and optimization layer includes a simulation engine, an optimization algorithm and a result feedback module, which are used to run the simulation of the virtual model, perform system optimization in combination with the prediction results of the deep learning model, and feed back the optimization results to the physical system.

[0011] Preferably, the business application layer includes a user interface, an API interface and a business logic module, which are used to provide a friendly user interface, support system integration and implement specific business logic.

[0012] Preferably, the security and management layer includes data security, system security, compliance checking and system monitoring modules, which are used to protect the security of data transmission and storage and ensure the stability and compliance of the system.

[0013] The present invention provides a digital twin simulation optimization system based on deep learning. It has the following beneficial effects: The present invention provides a digital twin simulation optimization system based on deep learning. The core advantage of the present invention lies in real-time data processing and edge computing. It collects and preprocesses data through sensors and edge devices, reduces latency, and reduces server burden. The digital twin model is combined with deep learning optimization to accurately reflect the behavior of the physical system and provide real-time optimization suggestions. Business applications adopt modern front-end and back-end technologies, with rich interfaces, strong scalability, and easy maintenance. In terms of security, advanced encryption and multi-level protection are used to ensure data transmission and system security. Automated compliance checks and comprehensive system monitoring ensure efficient operation. The integration of these technologies significantly improves system performance, meets complex needs, and enhances security and scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1It is a schematic diagram of the system framework flow of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] like Figure 1 As shown, an embodiment of the present invention provides a digital twin simulation and optimization system based on deep learning, including a data acquisition layer, the data acquisition layer is connected to a data processing layer, the data processing layer is connected to a digital twin model layer, the digital twin model layer is connected to a deep learning layer, the deep learning layer is connected to a simulation and optimization layer, the simulation and optimization layer is connected to a business application layer, and the business application layer is connected to a security and management layer.

[0017] The data acquisition layer includes sensors, industrial equipment, and edge computing devices, which are used to collect data from physical systems in real time and transmit it to the data processing layer through data gateways. The data processing layer includes data cleaning, data storage, and data stream processing modules, which are used to clean and preprocess data, store historical and real-time data, and perform real-time data preprocessing. The digital twin model layer includes virtual modeling, model correction, and model optimization modules, which are used to build virtual models of physical systems, correct models through real-time data, and use deep learning technology to optimize the prediction accuracy and response speed of the model.

[0018] The deep learning layer includes dataset management, model training, and model deployment modules, which are used to manage training datasets, train deep learning models, and deploy them to the cloud or edge devices. The simulation and optimization layer includes simulation engines, optimization algorithms, and result feedback modules, which are used to run simulations of virtual models, optimize the system based on the prediction results of deep learning models, and feed back the optimization results to the physical system. The business application layer includes user interfaces, API interfaces, and business logic modules, which are used to provide a user-friendly interface, support system integration, and implement specific business logic. The security and management layer includes data security, system security, compliance checks, and system monitoring modules, which are used to protect the security of data transmission and storage and ensure the stability and compliance of the system.

[0019] Specifically: Data Collection Layer Select sensor types, such as temperature sensors, humidity sensors, and pressure sensors. Industrial equipment such as PLC (Programmable Logic Controller) and SCADA (Supervisory Control and Data Acquisition System). Edge computing devices can use Raspberry Pi, JetsonNano small computing devices. Use Edge TPU (Tensor Processing Unit) or similar accelerators. The data gateway uses the MQTT (Message Queuing Telemetry Transport) protocol for data transmission. Use AWS IoTCore and Azure IoT Hub cloud services as data gateways.

[0020] Data processing layer Data cleaning and preprocessing use Python's Pandas and NumPy libraries for data cleaning and preprocessing. Data storage uses MongoDB and MySQL databases to store historical data. Amazon S3 and Azure Data Lake are used to store large amounts of raw data. Data stream processing uses Apache Kafka and Apache Flink stream processing frameworks for real-time data processing.

[0021] Digital Twin Model Layer Virtual modeling uses CAD software (such as SolidWorks, AutoCAD) or 3D scanning technology to generate a virtual model of the physical system. Model calibration uses MATLAB and Simulink tools for model calibration and verification. Model optimization uses Python's Scikit-learn and TensorFlow libraries for model optimization.

[0022] Deep Learning Layers Dataset management uses TensorFlow Data Validation (TFDV) for data set validation and management. Model training uses TensorFlow and PyTorch deep learning frameworks for model training. Model deployment uses TensorFlowServing and TorchServe tools for model deployment. Docker containerization technology is used to ensure the portability and isolation of the model.

[0023] Simulation and Optimization Layer The simulation engine uses MATLAB / Simulink and AnyLogic simulation engines. The optimization algorithm uses genetic algorithm and simulated annealing algorithm for optimization. The result feedback uses MQTT and HTTP protocols to feed back the simulation and optimization results to the physical system.

[0024] Business application layer The user interface uses React and Vue front-end frameworks to develop the user interface. The API interface uses Spring Boot and Flask frameworks to develop the API interface. The business logic uses Python and Java back-end languages ​​to implement the business logic.

[0025] Security and Management Data security uses SSL / TLS to encrypt data transmission. Use access control and authentication mechanisms to protect data. System security uses firewalls and intrusion detection systems (IDS) to protect the system. Regularly update security patches and configurations. Compliance checks are to comply with relevant data protection regulations, such as GDPR. Conduct regular compliance audits. System monitoring uses Prometheus and Grafana tools for system monitoring. Set up an alarm mechanism to detect and handle problems in a timely manner.

[0026] The present invention collects data through sensors and industrial equipment, and combines edge computing devices for data preprocessing, thereby reducing data transmission delays and bandwidth requirements, and improving the real-time performance and response speed of the system. At the same time, edge computing reduces the load on the central server, improves the scalability and stability of the system, uses CAD software and 3D scanning technology to build a high-precision virtual model, and continuously corrects the model through real-time data to ensure its consistency and accuracy with the physical system, so that the simulation result is more reliable and can truly reflect the operating status of the physical system. The deep learning technology is introduced to optimize the digital twin model, which improves the prediction accuracy and response speed of the model. The deep learning algorithm can learn from a large amount of historical data and provide more accurate fault prediction and performance optimization suggestions, thereby improving the operating efficiency and reliability of the system. The simulation engine can run the digital twin model and perform real-time optimization in combination with the prediction results of deep learning. The optimization algorithm is efficient and can quickly provide optimization suggestions. The physical system is adjusted in real time through an automated feedback mechanism, reducing dependence on manual intervention and improving the automation level of the system. A user interface is built to provide rich data visualization and real-time monitoring functions to meet the complex needs of users. The background logic is implemented using Python and Java programming languages, supports flexible API interface design, is convenient for integration with other systems, and improves the scalability and maintainability of the system. SSL / TLS encryption technology is used to protect data transmission and ensure data integrity and privacy. Multi-level access control and authentication mechanisms enhance the security of the system, which can effectively respond to complex threats, regularly update security patches and configurations, and enhance the system's protection capabilities. The use of automated tools for compliance checks improves the efficiency and accuracy of checks, reduces the need for manual intervention, and provides real-time feedback on compliance check results to ensure that the system always meets industry standards and regulatory requirements. Prometheus and Grafana monitoring tools are used for comprehensive system status monitoring, and a comprehensive alarm mechanism is set up to discover and solve potential problems in real time and ensure the stable operation of the system. In summary, the technology of the present invention not only improves the real-time and accuracy of data processing and simulation by introducing edge computing, high-precision digital twins, deep learning, modern front-end and back-end technologies, and multi-level security protection measures, but also enhances the scalability, flexibility, and security of the system, enabling it to better cope with the complex needs of modern industrial and Internet of Things environments.

[0027] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital twin simulation optimization system based on deep learning, including a data acquisition layer, characterized in that: The data acquisition layer is connected to the data processing layer, the data processing layer is connected to the digital twin model layer, the digital twin model layer is connected to the deep learning layer, the deep learning layer is connected to the simulation and optimization layer, the simulation and optimization layer is connected to the business application layer, and the business application layer is connected to the security and management layer.

2. According to a deep learning-based digital twin simulation optimization system according to claim 1, it is characterized in that: The data acquisition layer includes sensors, industrial equipment and edge computing devices, which are used to collect data from physical systems in real time and transmit it to the data processing layer through a data gateway.

3. According to a deep learning-based digital twin simulation optimization system according to claim 1, it is characterized in that: The data processing layer includes data cleaning, data storage and data stream processing modules, which are used to clean and pre-process data, store historical and real-time data, and perform real-time data stream processing.

4. The digital twin simulation optimization system based on deep learning according to claim 1, characterized in that: The digital twin model layer includes virtual modeling, model correction and model optimization modules, which are used to build a virtual model of the physical system, correct the model through real-time data, and use deep learning technology to optimize the model's prediction accuracy and response speed.

5. The digital twin simulation optimization system based on deep learning according to claim 1, characterized in that: The deep learning layer includes dataset management, model training, and model deployment modules, which are used to manage training datasets, train deep learning models, and deploy them to the cloud or edge devices.

6. The digital twin simulation optimization system based on deep learning according to claim 1, characterized in that: The simulation and optimization layer includes a simulation engine, an optimization algorithm and a result feedback module, which are used to run the simulation of the virtual model, optimize the system in combination with the prediction results of the deep learning model, and feed back the optimization results to the physical system.

7. The digital twin simulation optimization system based on deep learning according to claim 1, characterized in that: The business application layer includes a user interface, an API interface and a business logic module, which are used to provide a friendly user interface, support system integration and implement specific business logic.

8. The digital twin simulation optimization system based on deep learning according to claim 1, characterized in that: The security and management layer includes data security, system security, compliance checking and system monitoring modules, which are used to protect the security of data transmission and storage.

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