Experimental process monitoring system for chemical experiment table based on digital twinborn technology

Abstract

The invention provides a chemical experiment table experiment process monitoring system based on a digital twinborn technology. The system integrates various sensors, such as a high-definition camera, a barometer, a thermometer, infrared imaging equipment and a hygrometer, as well as a human-computer interaction interface and a real-time data transmission module, so that comprehensive monitoring and intelligent analysis of a chemical experiment process are realized. By adopting algorithms such as linear discriminant analysis and linear regression and a machine learning technology, the system can analyze and mine experimental data and provide intelligent experimental optimization suggestions for experimenters. Meanwhile, due to the introduction of the digital twinning technology, the experimental process and environment have the characteristics of traceability and visualization, and the accuracy and efficiency of the experiment are further improved. The innovation not only provides a new thought and method for the development of a chemical experiment monitoring technology, but also lays a foundation for the intelligent and automatic development of chemical experiments.

Description

Technical Field

[0001] The present invention relates to the field of chemical laboratory bench equipment monitoring technology, integrating technologies and fields such as sensor technology, communication timing, three-dimensional modeling, digital twin technology and AI artificial intelligence algorithms. Background Art

[0002] Traditional chemical experiment monitoring relies primarily on direct observation and manual recording by the experimenter. This method is not only time-consuming and laborious, but also susceptible to human error, limiting the accuracy and traceability of experimental data. Video recording is also available, but the information recorded is less comprehensive and in-depth. With the rapid development of the Internet of Things, big data, artificial intelligence, and digital twin technology, the development of a system that can automatically and intelligently monitor chemical experimental processes and create a digital twin of the experimental environment and process has become a key development direction in chemical experiment monitoring technology.

[0003] Digital twin technology integrates multidisciplinary, multi-physics, multi-scale, and multi-probability simulation processes to map physical equipment in a virtual space, reflecting the entire lifecycle of the corresponding physical equipment. Applying digital twin technology to chemical laboratory monitoring enables real-time simulation, monitoring, and analysis of experimental processes, improving the accuracy and traceability of experimental data while providing intelligent optimization recommendations for experimenters.

[0004] However, most existing chemical experiment monitoring systems can only achieve single data collection or display functions, lacking comprehensive monitoring capabilities and intelligent analysis capabilities. Therefore, developing a chemical laboratory bench experimental process monitoring system based on digital twin technology is of great significance for improving the efficiency and accuracy of chemical experiments. Summary of the Invention

[0005] The purpose of this invention is to provide a chemical laboratory bench experimental process monitoring system based on digital twin technology. The system integrates multiple sensors such as high-definition cameras, barometers, thermometers, infrared imaging equipment, and hygrometers, as well as a human-computer interface and real-time data transmission module, enabling comprehensive monitoring of the chemical experiment process. At the same time, through computer learning and specific algorithm models, the system can analyze experimental data, provide optimization suggestions for the experiment, and construct a digital twin model of the experimental environment and process, achieving traceability of experimental steps and environment.

[0006] In order to solve the above problems, the present invention provides a chemical laboratory bench experimental process monitoring system based on digital twin technology, comprising: 1. Data acquisition module: HD camera: Used to capture high-definition video data during experiments, recording experimental details and phenomena. The HD camera can be fixed in a suitable position on the chemical laboratory bench to ensure that key steps and changes in the experimental process are clearly captured.

[0007] Barometer: Used to monitor the air pressure changes in the experimental environment in real time. Air pressure is one of the important factors affecting the results of chemical experiments. The barometer can record the air pressure data in real time during the experiment, providing an important reference for experimental analysis.

[0008] Thermometer: Used to monitor temperature changes in the experimental environment in real time. Temperature is a key factor affecting chemical reaction rates and product properties. A thermometer can record ambient temperature data in real time during the experiment, helping to analyze the accuracy of experimental results.

[0009] Infrared imaging equipment: Used to record temperature changes on the lab bench, in containers, and during experiments. Infrared imaging equipment provides thermal imaging data, helping experimenters intuitively understand the temperature changes of elements such as the bench, instruments, and reagents during the experiment, allowing for more accurate analysis of experimental results.

[0010] Hygrometer: Used to monitor humidity changes in the experimental environment in real time. Humidity is one of the important factors affecting the results of chemical experiments. A hygrometer can record humidity data in real time during the experiment, providing an important basis for experimental analysis.

[0011] Spectral illuminance meter: used to record the light intensity of the experimental environment in real time, providing a basis for later experimental analysis, data comparison, etc.

[0012] 2. Human-computer interaction interface: The human-computer interaction interface allows experimenters to manually enter or import the experimental framework, including information such as the experimental purpose, experimental steps, required reagents, etc. Experimenters can conveniently set experimental parameters and conditions through the human-computer interaction interface to ensure the accuracy and traceability of the experiment.

[0013] During the experiment, the human-computer interaction interface also provides real-time manual entry of experimental changes. Experimenters can record key steps, abnormal phenomena, or data changes at any time for subsequent analysis and optimization.

[0014] At the end of the experiment, the human-computer interface allows the experimenter to enter the experimental results, including the properties of the experimental product, yield, etc. This information will be stored together with the data during the experiment, providing a basis for subsequent experimental analysis and optimization.

[0015] 3. Data transmission module: The data transmission module uses an image transmission module for real-time data transmission. This module utilizes high-speed wireless communication technology to transmit high-definition video data, environmental parameter data, and other data to a central processing unit or cloud server in real time. This ensures the real-time and accuracy of experimental data, providing strong support for subsequent analysis and optimization.

[0016] 4. Data display module: The data display module uses a dashboard to display experimental data in real time. The dashboard displays high-definition video, environmental parameter data, experimental steps, and other information, which updates synchronously with the experimental process. This allows experimenters to intuitively understand the progress and status of the experiment, allowing them to promptly identify and address any anomalies.

[0017] 5. Data preprocessing module: The data preprocessing module performs preprocessing operations on the collected raw data, including cleaning, missing value processing, and normalization. Data cleaning can remove invalid or redundant data to improve data quality; missing value processing can fill in gaps in the data to ensure data integrity; and normalization can convert the data to a consistent scale to facilitate subsequent processing and analysis by algorithm models.

[0018] 6. Algorithm model and analysis module: The algorithmic model and analysis module uses algorithms such as linear discriminant analysis (LDA) and linear regression to establish experimental models. Using preprocessed data, the algorithmic model can identify key steps and characteristics of the experimental process and provide optimization suggestions. For example, by analyzing the changing trends of parameters such as temperature, air pressure, and humidity during the experiment, the algorithmic model can predict the trends of experimental results and potential problems, thereby guiding experimenters to make appropriate adjustments and optimizations.

[0019] Furthermore, algorithmic models can leverage machine learning techniques to conduct in-depth mining and analysis of experimental data. By training on large amounts of experimental data, machine learning models can learn the underlying patterns and characteristics of the experimental process, providing more intelligent optimization recommendations. For example, by analyzing abnormal data and changing trends during the experiment, machine learning models can predict potential failures or problems and provide corresponding solutions or preventative measures.

[0020] 7. Digital twin construction and experimental scene reproduction module: The digital twin construction module builds a digital twin model of the experimental environment and process based on preprocessed data and AI data analysis results from the algorithm model. The digital twin model can reflect various parameters and status changes during the experiment in real time, providing experimenters with a visual simulation of the experimental environment and process.

[0021] Digital twin models allow experimenters to conduct experimental simulations and predictive analysis in a virtual environment. Based on the data and visualization provided by the digital twin, they can adjust experimental conditions and procedures and optimize experimental plans. This not only improves experimental efficiency but also reduces costs and risks.

[0022] If necessary, this module can be used to reproduce the scene of the experiment for which the digital twin model has been built, and the various experimental details of the experiment at that time can be carefully and repeatedly analyzed.

[0023] 8. Optimization suggestion module: The optimization suggestion module provides experimenters with experimental optimization suggestions based on the analysis results of the algorithm model and the visualization information of the digital twin model. These suggestions can include adjusting experimental conditions (such as temperature, air pressure, and humidity), optimizing experimental procedures, and improving experimental methods. These suggestions aim to improve experimental accuracy and efficiency while reducing experimental costs and risks.

[0024] The present invention provides a chemical laboratory bench experimental process monitoring system based on digital twin technology, which has the following beneficial effects: 1. Improve the accuracy and traceability of experimental data: By integrating data acquisition devices such as multiple sensors and high-definition cameras, the system can collect and record various parameters and status changes during the experiment in real time. After preprocessing and analysis, this data can provide accurate and reliable experimental data support to experimenters, while also ensuring traceability of experimental procedures and environment.

[0025] 2. Comprehensive monitoring of the experimental process: The system adopts a modular design, integrating multiple functional modules such as data acquisition, processing, display, analysis, and optimization suggestions. These modules work together to achieve comprehensive monitoring of the chemical experiment process, ensuring the smooth progress of the experiment and the accuracy of the results.

[0026] 3. Provide intelligent experimental optimization suggestions: By using algorithms such as linear discriminant analysis and linear regression, as well as machine learning techniques, the system can analyze and mine experimental data and provide intelligent experimental optimization suggestions to experimenters. These suggestions are designed to improve experimental accuracy and efficiency while reducing experimental costs and risks.

[0027] 4. Enhanced Experimental Visualization and Interactivity: The system uses a dashboard to display experimental data, environmental parameters, and other information in real time, providing an intuitive interface for experimenters to monitor their experiments. Furthermore, the human-computer interaction interface allows experimenters to manually enter or import experimental frameworks, record experimental changes in real time, and enter experimental results, enhancing the interactivity and flexibility of experiments.

[0028] 5. Promoting the intelligent development of chemical experiment monitoring technology: This invention applies digital twin technology to chemical experiment monitoring, enabling real-time simulation, monitoring, and analysis of experimental processes. This innovation not only improves the efficiency and accuracy of chemical experiments but also provides new ideas and methods for the intelligent development of chemical experiment monitoring technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a front view of the information collection device of the present invention.

[0030] Figure 2 It is a side view of the information collection device of the present invention.

[0031] Figure 3 This is a schematic diagram of the use and installation of the information collection device of the present invention.

[0032] Figure 4 This is a schematic diagram of the overall architecture of the chemical lab bench experimental process monitoring system based on digital twin technology. The diagram shows the connections and functional distribution of various modules, including the data acquisition module, human-computer interaction interface, data transmission module, data display module, data preprocessing module, algorithm model and analysis module, digital twin construction module, and optimization suggestion module.

[0033] Figure 5 This is a schematic diagram of the overall layout of the chemical laboratory bench experimental process monitoring system based on digital twin technology. The diagram shows the connection relationship and functional distribution between the various offices, including the laboratory, data center, and AI data analysis room.

[0034] 1. Thermometer; 2. Barometer; 3. High-definition camera; 4. Infrared imaging device; 5. Hygrometer; 6. Human-computer interaction screen; 7. Collection device base; 8. Collection device bracket; 9. Collection device upper beam; 10. Collection device lower beam; 11. Sensor mounting bracket; 12. Spectral illuminance meter; 13. Image transmission; 14. Upper sensor group; 15. Lower sensor group; 16. First human-computer interaction screen connector; 17. Second human-computer interaction screen connector; 18. Information collection device; 19. Experimental equipment; 20. Test bench; 21. Information collection device base. DETAILED DESCRIPTION

[0035] The following describes the specific embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0036] In the present invention, unless otherwise specified, directional words such as "up, down, left, right" generally refer to the up, down, left, and right shown in the reference drawings; "inside and outside" refer to the inside and outside relative to the outline of each component itself.

[0037] 1. Information collection equipment structure design: like Figure 1-5 As shown, the upper sensor group 14 consists of three sensors: a thermometer 1, a high-definition camera 3, and an infrared imaging device 4, which are fixed to the crossbeam 9 of the acquisition device. The lower sensor group 15 consists of three sensors: a barometer 2, a spectrophotometer 12, and a hygrometer 5, which are fixed to the lower crossbeam 10 of the acquisition device. The human-computer interaction screen 6 and the image transmission device 13 are fixed to the information acquisition device bracket 8. The first human-computer interaction screen connector 16 and the second human-computer interaction screen connector 17 realize the purpose of electrical connection, signal transmission and mechanical fixation between the human-computer interaction screen 6 and other devices. The acquisition device base 7 is connected to the fixed acquisition device bracket 8 to play a supporting role. The upper crossbeam 9 of the acquisition device and the lower crossbeam 10 of the acquisition device are connected to the acquisition device bracket 8. The sensor mounting bracket 11 serves to connect the crossbeam and the sensor, and can install, fix and adjust the orientation of the sensor.

[0038] 2. How to install and use the information collection equipment: like Figure 3 As shown, bolt the base 21 of the information acquisition device 18 to the test bench 20. Adjust the angles of each sensor to achieve optimal data collection. After adjusting each sensor, power on the system to check its operating status and signal transmission. If normal, manually enter or import pre-prepared experimental data into the system to begin the experiment. During the experiment, the central control center can monitor the progress of the experimental equipment 19 in real time via the 3D data dashboard. At the end of the experiment, enter the experimental data and disconnect the power supply.

[0039] 2.1 System Architecture Design: The present invention's chemical lab bench process monitoring system adopts a modular design, including a data acquisition module, a human-computer interface, a data transmission module, a data display module, a data preprocessing module, an algorithm model and analysis module, a digital twin construction module, and an optimization suggestion module. These modules work together to achieve comprehensive monitoring and intelligent analysis of the chemical experiment process.

[0040] 2.2 Data Collection: Before the experiment begins, the experimenter sets up the experimental framework and parameters through the human-computer interface. After the system starts, sensors such as the high-definition camera 3, barometer 2, thermometer 1, infrared imaging device 4, and hygrometer 5 begin collecting experimental data. This data is transmitted in real time to the data center via the data transmission module for preprocessing and storage. The data preprocessing module performs preprocessing operations such as cleaning, missing value processing, and normalization on the collected raw data to improve data quality and the accuracy of the algorithm model. The preprocessed data is used for algorithm model analysis and the construction of the digital twin model.

[0041] 3. Algorithm model analysis and digital twin construction: The AI data analysis room uses algorithms such as linear discriminant analysis (LDA) and linear regression to analyze preprocessed data through algorithmic models and analysis modules. By analyzing key steps and features of the experimental process, the algorithmic model can identify underlying patterns and trends in the experimental process and provide optimization suggestions.

[0042] The digital twin construction module also builds a digital twin model of the experimental environment and process based on preprocessed data and analysis results from the algorithm model. This model can reflect various parameters and state changes during the experiment in real time, providing experimenters with a visual simulation of the experimental environment and process.

[0043] 4. Data display and optimization suggestions: The data display module uses a dashboard to display experimental data, environmental parameters, and other information in real time. Experimenters can use the dashboard to intuitively understand the progress and status of the experiment, and promptly identify and address abnormal situations.

[0044] The optimization suggestion module provides experimenters with experimental optimization suggestions based on the analysis results of the algorithm model and the visualization information of the digital twin model. These suggestions are designed to improve the accuracy and efficiency of the experiment while reducing the cost and risk of the experiment.

[0045] 5. System iteration and optimization: As experimental data accumulates and analysis results are optimized, the system can gradually iterate and optimize the algorithm model and digital twin model. This not only improves the accuracy and reliability of the system, but also provides more intelligent optimization suggestions for experimenters. Examples and Applications

[0046] Example 1:

[0047] This system was used in an organic chemistry laboratory. An experimenter needed to conduct an experiment to synthesize a certain organic compound. Before the experiment began, the experimenter set up the experimental framework and parameters, including the required reagents and reaction conditions, through the human-computer interface.

[0048] After the system starts, HD camera 3 begins collecting HD video data during the experiment. Sensors such as barometer 2, thermometer 1, infrared imaging device 4, and hygrometer 5 begin to monitor the pressure, temperature, and humidity of the experimental environment, as well as the temperature changes of the experimental table and container in real time. This data is transmitted in real time to the central processing unit via the data transmission module for storage and processing.

[0049] The data preprocessing module performs preprocessing operations such as cleaning, missing value processing, and normalization on the collected raw data to improve data quality and the accuracy of the algorithm model. The preprocessed data is used for algorithm model analysis and the construction of the digital twin model.

[0050] The algorithm model and analysis module used a linear discriminant analysis algorithm to analyze the preprocessed data. By analyzing the key steps and characteristics of the experimental process, the algorithm model identified the key factors affecting the experimental results and provided corresponding optimization suggestions.

[0051] The digital twin construction module also builds a digital twin model of the experimental environment and process based on preprocessed data and analysis results from the algorithm model. This allows experimenters to intuitively understand various parameters and state changes during the experiment, allowing them to more accurately grasp the experimental progress and results.

[0052] During the experiment, the experimenter recorded the experimental changes in real time through the human-computer interaction interface and entered the experimental results at the end of the experiment. This data, along with previous experimental data, is stored in the system, providing a basis for subsequent experimental analysis and optimization.

[0053] Example 2:

[0054] This system was also used in an inorganic chemistry laboratory. Experimenters were conducting an experiment to prepare a certain inorganic material. During the experiment, the system also collected high-definition video data, air pressure, temperature, humidity, and infrared imaging data, and performed preprocessing and analysis.

[0055] The algorithmic model and analysis module uses a linear regression algorithm to analyze preprocessed data. By analyzing the data's changing trends and characteristics during the experiment, the algorithmic model predicts the likely trends of the experimental results and provides corresponding optimization suggestions. These suggestions are designed to improve the accuracy and efficiency of the experiment while reducing experimental costs and risks.

[0056] The digital twin construction module also builds a digital twin model of the experimental environment and process based on preprocessed data and algorithm model analysis results. This digital twin model allows experimenters to intuitively understand various parameters and state changes during the experiment, allowing them to more accurately determine the success of the experiment and potential problems.

[0057] After the experiment, the experimenters entered the experimental results through the human-computer interface and compared and analyzed them with previous experimental data. By comparing the experimental results under different experimental conditions with the simulation results of the digital twin model, the experimenters came to more accurate experimental conclusions and optimization solutions.

[0058] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the scope of protection of the present invention.

[0059] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. To avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0060] In addition, the various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A chemical laboratory bench experimental process monitoring system based on digital twin technology, characterized in that: include: Data acquisition module, used to collect high-definition video data, air pressure data, temperature data, infrared imaging data, light intensity and humidity data during the experiment; Human-computer interaction interface, used to set up the experimental framework, record experimental changes in real time, and enter experimental results; A data transmission module, used to transmit the collected data to the central processing unit or cloud server in real time; Data display module, used to display experimental data in real time in the form of a dashboard; Data preprocessing module, used to clean the collected data, handle missing values, and perform normalization preprocessing operations; Algorithm model and analysis module, used to analyze pre-processed data, identify key steps and features in the experimental process, and provide optimization suggestions; The digital twin construction module is used to build a digital twin model of the experimental environment and process based on the analysis results of the pre-processed data and algorithm model; The optimization suggestion module is used to provide experimenters with experimental optimization suggestions based on the analysis results of the algorithm model and the visualization information of the digital twin model.

2. The chemical laboratory bench experimental process monitoring system based on digital twin technology according to claim 1, characterized in that: The data acquisition module also includes a barometer, a thermometer, an infrared imaging device and a hygrometer sensor.

3. The chemical laboratory bench experimental process monitoring system based on digital twin technology according to claim 1, characterized in that: The human-computer interaction interface also includes an experiment framework setting module, an experiment change recording module and an experiment result entry module.

4. The chemical laboratory bench experimental process monitoring system based on digital twin technology according to claim 1, characterized in that: The algorithm model and analysis module use linear discriminant analysis and linear regression algorithms to analyze the preprocessed data.

5. The chemical laboratory bench experimental process monitoring system based on digital twin technology according to claim 1, characterized in that: The digital twin construction module can reflect various parameters and state changes during the experiment in real time, providing experimenters with a visual experimental environment and process simulation.

6. The chemical laboratory bench experimental process monitoring system based on digital twin technology according to claim 1, characterized in that: The optimization suggestions provided by the optimization suggestion module include suggestions on adjusting experimental conditions, optimizing experimental steps, and improving experimental methods.

7. The chemical laboratory bench experimental process monitoring system based on digital twin technology according to claim 1, characterized in that: The system also includes a data storage module for storing collected data, preprocessed data, analysis results of the algorithm model, and digital twin model information.

8. The chemical laboratory bench experimental process monitoring system based on digital twin technology according to claim 1, characterized in that: The system also includes a system iteration and optimization module, which is used to gradually iterate and optimize the algorithm model and digital twin model based on the continuous accumulation of experimental data and continuous optimization of analysis results.

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