On-line monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime

Abstract

The application discloses an online monitoring system for oil shale pyrolysis process based on laser confocal rapid fluorescence lifetime, and belongs to the technical field of oil shale pyrolysis. In order to solve the problem that the traditional monitoring technology cannot realize real-time and dynamic monitoring of the pyrolysis process, and limit the in-depth understanding of the pyrolysis mechanism and the optimization of the pyrolysis efficiency, the application provides a high-temperature environment for the organic matter conversion reaction of the oil shale sample, carries out the pyrolysis reaction on the oil shale sample, and captures the fluorescence lifetime image in real time through the laser confocal microscope. Meanwhile, the dynamic regulation and control of the pyrolysis temperature are provided when the oil shale sample carries out the organic matter conversion reaction. The visual three-dimensional model is reconstructed based on the fluorescence lifetime image, the real-time and dynamic monitoring of the organic matter conversion in the oil shale pyrolysis process is provided, a unique real-time monitoring means for the oil shale pyrolysis process is provided, and the fluorescence lifetime image is captured in real time through the laser confocal microscope, so that the chemical conversion and morphological change of the organic matter in the pyrolysis process can be understood in depth.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil shale pyrolysis, and particularly relates to an online monitoring system for oil shale pyrolysis process based on laser confocal rapid fluorescence lifetime. BACKGROUND

[0002] Oil shale (also known as oil mother shale) is a kind of high-ash content sedimentary rock containing combustible organic matter. Oil shale is a kind of unconventional oil and gas resources, and is listed as a very important replacement energy in the 21st century due to its abundant resources and feasibility of development and utilization. The main use of oil shale is to extract shale oil, and many by-products can also be obtained, such as ammonium sulfate (which can be used as fertilizer), phenols and pyridines (which can be used as chemical raw materials for producing synthetic fibers, plastics, dyes and drugs). The discharged gas can be used as gas fuel, and the remaining shale ash can be used to manufacture cement clinker, ceramic fiber, ceramic aggregate and other building materials.

[0003] Among them, the pyrolysis of oil shale is a key process for converting organic matter into oil and gas. The traditional monitoring technology cannot realize real-time and dynamic monitoring of the pyrolysis process, which limits the in-depth understanding of the pyrolysis mechanism and the optimization of the pyrolysis efficiency. SUMMARY

[0004] The present application aims to provide an online monitoring system for oil shale pyrolysis process based on laser confocal rapid fluorescence lifetime to solve the problems raised in the background.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: an online monitoring system for oil shale pyrolysis process based on laser confocal rapid fluorescence lifetime, comprising:

[0006] An external component for:

[0007] providing a high-temperature environment for the organic matter conversion reaction of the oil shale sample, performing pyrolysis reaction on the oil shale sample and capturing the fluorescence lifetime image in real time through the laser confocal microscope, and providing dynamic regulation of the pyrolysis temperature while the oil shale sample is undergoing the organic matter conversion reaction, the external component being signal-connected with a real-time data processing module, a dynamic temperature control module and a three-dimensional reconstruction visualization platform, and real-time acquisition of reaction data and fluorescence lifetime images and sending to the real-time data processing module;

[0008] A real-time data processing module for:

[0009] Based on the reaction data and fluorescence lifetime images generated by the external component when the oil shale sample is undergoing the organic matter conversion reaction, data processing and analysis of the fluorescence lifetime change are performed, and the chemical conversion of the organic matter in the oil shale pyrolysis process is monitored in real time;

[0010] A dynamic temperature control module for:

[0011] According to the reaction data and the fluorescence lifetime image data obtained by the real-time data processing module, a pyrolysis temperature adjustment instruction is generated and sent to the peripheral component;

[0012] The three-dimensional reconstruction visualization platform is used for:

[0013] Based on the real-time obtained reaction data and fluorescence lifetime image, imaging processing is performed, and according to the fluorescence lifetime image data and the imaging result, a three-dimensional model of organic matter conversion in the oil shale pyrolysis process is reconstructed, and the three-dimensional model is visually displayed.

[0014] Further, the peripheral component comprises:

[0015] The laser confocal microscope is used to provide high-resolution and high-contrast microscopic images and capture fluorescence lifetime images in real time. The laser confocal microscope comprises a microscope, a laser light source, a scanning device, a detector, an image output device, an optical device, and a confocal system. The laser confocal microscope is signal-connected with the three-dimensional reconstruction visualization platform.

[0016] The high-temperature adaptive sample chamber is made of high-temperature resistant material and provides a high-temperature environment for the occurrence of organic matter conversion reaction of the oil shale sample. The high-temperature adaptive sample chamber also allows laser to pass through.

[0017] The pyrolysis reactor is used to provide the reaction conditions required for oil shale pyrolysis and adjust the pyrolysis temperature in real time through dynamic temperature control. The pyrolysis reactor comprises a reactor body, a heating element, a temperature sensor, and a control system. The temperature sensor and the control system are signal-connected with the real-time data processing module and the dynamic temperature control module.

[0018] Further, the real-time data processing module comprises:

[0019] The data preprocessing unit is used for:

[0020] The real-time data processing module receives the original reaction data and the fluorescence lifetime image sent by the peripheral component, performs preliminary data cleaning, format conversion, and standardization processing on the original reaction data and the fluorescence lifetime image, identifies and eliminates abnormal values, repeated values, or invalid data in the data, and converts the received data into a unified format.

[0021] Further, the real-time data processing module further comprises:

[0022] The fluorescence lifetime analysis unit is used for:

[0023] The pre-processed fluorescence lifetime image data is loaded, the time of the fluorescence photons reaching the detector is accurately measured based on the pre-processed fluorescence lifetime image data by using TCSPC, a fluorescence decay curve is constructed, the fluorescence lifetime is calculated, the fluorescence decay curve is fitted to obtain the fluorescence lifetime value, the chemical conversion of the organic matter in the oil shale pyrolysis process is monitored in real time, the fluorescence lifetime value is associated with the chemical conversion of the organic matter in the oil shale pyrolysis process, and a monitoring report is generated, wherein the content of the monitoring report includes the change trend of the fluorescence lifetime, the degree of chemical conversion of the organic matter, and the rate of the pyrolysis process.

[0024] Further, the dynamic temperature control module comprises:

[0025] The adjustment instruction generation unit is configured to:

[0026] The reaction data and the fluorescence lifetime image data provided by the real-time data processing module are taken as input parameters of the algorithm, a preset temperature adjustment algorithm is used to generate a pyrolysis temperature adjustment instruction, the adjustment instruction comprises a temperature set value, an adjustment rate, and an adjustment direction, and the generated pyrolysis temperature adjustment instruction is sent to the peripheral component to guide the dynamic regulation and control of the pyrolysis temperature by the peripheral component.

[0027] Further, the dynamic temperature control module further comprises:

[0028] The adjustment effect evaluation unit is configured to:

[0029] The actual temperature data of the peripheral component after receiving the temperature adjustment instruction is collected, the actual temperature data is compared with a preset temperature range, the effect of the dynamic temperature adjustment is evaluated, and the temperature adjustment instruction is adjusted according to the evaluation result.

[0030] Further, the three-dimensional reconstruction visualization platform comprises:

[0031] The three-dimensional imaging processing unit is configured to:

[0032] Based on the real-time obtained reaction data and the fluorescence lifetime image, a three-dimensional reconstruction algorithm is used to voxelize, surface reconstruct, and convert the received data into voxel data in a three-dimensional space, on the basis of voxelization, an isosurface extraction is performed to construct a three-dimensional surface model of the organic matter conversion in the pyrolysis process, a three-dimensional model of the organic matter conversion in the oil shale pyrolysis process is generated, and the three-dimensional model comprises the spatial distribution, morphological change, and possible chemical reaction path of the organic matter.

[0033] Further, the three-dimensional reconstruction visualization platform further comprises:

[0034] The visualization display unit is configured to:

[0035] Load the optimized three-dimensional model data, perform data analysis on the three-dimensional model, initialize the AR environment, build a virtual scene for displaying the three-dimensional model, render the analyzed three-dimensional model data into the virtual scene, visualize the reconstructed three-dimensional model based on AR, provide interactive functions for the visualization display by implementing user input processing logic, the interactive functions including rotation, scaling, translation, when the user performs an interactive operation, the visualization display unit updates the three-dimensional model state in the virtual scene in real time, and displays the updated visual feedback.

[0036] Further, the online monitoring system further comprises a pyrolysis environmental impact analysis module, the pyrolysis environmental impact analysis module comprises:

[0037] The fluorescence decay reference curve unit is configured to:

[0038] Obtain the initial fluorescence intensity in the pyrolysis process, and draw the fluorescence decay reference curve according to the initial fluorescence intensity, the set standard fluorescence lifetime and the following fluorescence decay function:

[0039]

[0040] wherein, I 0 is the fluorescence intensity decay function; I0 is the initial fluorescence intensity used in the pyrolysis; k i is the relative weight of the i-th fluorescence component; t is the time elapsed by the fluorescence; e is the natural constant; τ0 is the set standard fluorescence lifetime;

[0041] The decay comparison unit is configured to:

[0042] Compare the fluorescence decay curve constructed using the preprocessed fluorescence lifetime image data with the fluorescence decay reference curve, and calculate the similarity of the two curves using the formula:

[0043]

[0044] wherein, S is the similarity of the fluorescence decay curve and the fluorescence decay reference curve; n is the total number of corresponding comparison data points of the fluorescence decay curve and the fluorescence decay reference curve; I j is the fluorescence intensity of the j-th corresponding comparison data point on the fluorescence decay curve; I 0 j is the fluorescence intensity of the j-th corresponding comparison data point on the fluorescence decay reference curve;

[0045] The pyrolysis evaluation and anomaly analysis unit is configured to:

[0046] The similarity of the calculated fluorescence decay curve and the fluorescence decay reference curve is compared with a similarity threshold value, and if the similarity is less than the similarity threshold value, it indicates that the pyrolysis process is abnormal, the pyrolysis influence analysis of the environmental data is carried out based on the environmental data synchronously measured in the pyrolysis process, and a pyrolysis environmental data control scheme is output according to the analysis result.

[0047] Further, the temperature regulation algorithm preset by the dynamic temperature control module includes a required process temperature prediction model, and the required process temperature prediction model is constructed in the following manner:

[0048] Firstly, a convolutional neural network including an input layer, a hidden layer and an output layer is constructed, the hidden layer includes a convolutional layer, a pooling layer and a fully connected layer, and a convolution kernel in the convolutional layer, convolutional layer parameters and an excitation function are determined;

[0049] Secondly, pyrolysis process data of oil shale in historical records are acquired, the pyrolysis process data include historical reaction data, historical fluorescence lifetime image data and historical process temperature data, data cleaning is performed to remove abnormal data;

[0050] Thirdly, the pyrolysis process data are selected and grouped, and training data sets, verification data sets and test data sets are respectively established by using the pyrolysis process data in different groups;

[0051] Finally, the convolutional neural network is trained, verified, parameter-adjusted and tested by using the training data sets, the verification data sets and the test data sets in sequence, until the test result meets a predetermined accuracy requirement, and the required process temperature prediction model is obtained.

[0052] Compared with the prior art, the present application has the following advantages:

[0053] 1. The present application integrates peripheral components, a real-time data processing module, a dynamic temperature control module and a three-dimensional reconstruction visualization platform, can provide real-time and dynamic monitoring of the conversion of organic matter in the oil shale pyrolysis process, provides a unique real-time monitoring means for the oil shale pyrolysis process, and can capture fluorescence lifetime images in real time by using a laser confocal microscope, so as to deeply understand the chemical conversion and morphological change of organic matter in the pyrolysis process.

[0054] 2. The present application can accurately construct a fluorescence decay curve and calculate a fluorescence lifetime by loading preprocessed fluorescence lifetime image data, provides a key parameter for analyzing the chemical conversion of organic matter in the oil shale pyrolysis process, real-time monitors the chemical conversion of organic matter in the oil shale pyrolysis process, helps to analyze the performance of the pyrolysis process, can guide actual production operation, and improves the efficiency and economic benefit of the oil shale pyrolysis process.

[0055] 3. The application provides reaction data and fluorescence lifetime image data provided by the real-time data processing module, and the temperature adjustment instruction generation unit can generate accurate pyrolysis temperature adjustment instructions by using a preset temperature adjustment algorithm, ensuring that the pyrolysis process is carried out in an optimal temperature range, thereby optimizing the pyrolysis efficiency and reducing unnecessary energy consumption, and the adjustment effect evaluation unit can evaluate the effect of dynamic temperature adjustment by collecting actual temperature data of the peripheral component after receiving the temperature adjustment instruction and comparing the actual temperature data with the preset temperature range.

[0056] 4. The application constructs a three-dimensional model of organic matter conversion in the pyrolysis process by combining reaction data and fluorescence lifetime images, so that the spatial distribution, morphological changes and possible chemical reaction paths of organic matter can be intuitively displayed, providing a powerful visualization tool for researchers, and the unit can display the reconstructed three-dimensional model to the user in a more realistic and intuitive manner, so that the monitoring result is more real-time and dynamic. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 The figure is a schematic diagram of the module principle of the online monitoring system for the oil shale pyrolysis process of the application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the application.

[0059] To solve the technical problem that the traditional monitoring technology cannot realize real-time and dynamic monitoring of the pyrolysis process, and limits the in-depth understanding of the pyrolysis mechanism and the optimization of the pyrolysis efficiency, please refer to Figure 1 The application provides the following technical solutions:

[0060] The online monitoring system for the oil shale pyrolysis process based on laser confocal rapid fluorescence lifetime includes:

[0061] The peripheral component is used to:

[0062] The peripheral component provides a high-temperature environment for the organic matter conversion reaction of the oil shale sample, performs a pyrolysis reaction on the oil shale sample and captures a fluorescence lifetime image in real time by using a laser confocal microscope, and provides dynamic regulation of the pyrolysis temperature while the oil shale sample is performing the organic matter conversion reaction, and the peripheral component is signal-connected with the real-time data processing module, the dynamic temperature control module and the three-dimensional reconstruction visualization platform, and can collect reaction data and fluorescence lifetime images and send them to the real-time data processing module.

[0063] a real-time data processing module for:

[0064] based on the reaction data and fluorescence lifetime images generated by the peripheral component during the organic matter conversion reaction of the oil shale sample, processing the data and analyzing the changes in fluorescence lifetime to monitor the chemical conversion of organic matter in the oil shale pyrolysis process in real time;

[0065] a dynamic temperature control module for:

[0066] generating pyrolysis temperature adjustment instructions based on the reaction data and fluorescence lifetime image data obtained by the real-time data processing module, and sending the pyrolysis temperature adjustment instructions to the peripheral component;

[0067] a three-dimensional reconstruction visualization platform for:

[0068] based on the real-time obtained reaction data and fluorescence lifetime images, performing imaging processing, reconstructing a three-dimensional model of the organic matter conversion in the oil shale pyrolysis process based on the fluorescence lifetime image data and imaging results, and visualizing the three-dimensional model.

[0069] Specifically, by integrating the peripheral component, real-time data processing module, dynamic temperature control module and three-dimensional reconstruction visualization platform, a unique real-time monitoring method is provided for the oil shale pyrolysis process, which can capture fluorescence lifetime images in real time through a laser confocal microscope, thereby deeply understanding the chemical conversion and morphological changes of organic matter in the pyrolysis process.

[0070] In the above embodiment, the peripheral component provides a high-temperature environment for the oil shale sample to undergo organic matter conversion reaction. At the same time, this component can also capture fluorescence lifetime images in real time, and through signal connection with the real-time data processing module, dynamic temperature control module and three-dimensional reconstruction visualization platform, the reaction data and images are sent to the corresponding processing modules.

[0071] In the above embodiment, the real-time data processing module is responsible for processing the received reaction data and fluorescence lifetime images, analyzing the changes in fluorescence lifetime, thereby monitoring the chemical conversion of organic matter in the oil shale pyrolysis process in real time, improving the accuracy and real-time of monitoring.

[0072] In the above embodiment, the dynamic temperature control module generates pyrolysis temperature adjustment instructions based on the reaction data and fluorescence lifetime image data obtained by the real-time data processing module to optimize the pyrolysis process. This dynamic temperature control capability enables the system to adapt to different oil shale samples and reaction conditions, improving the pyrolysis efficiency and the quality of organic matter conversion.

[0073] In the above embodiment, the three-dimensional reconstruction visualization platform utilizes real-time obtained reaction data and fluorescence lifetime images for imaging processing, reconstructs a three-dimensional model of the conversion of organic matter in the oil shale pyrolysis process, and visualizes the model, which not only provides a user with an intuitive and vivid monitoring interface, but also helps further analyze the pyrolysis mechanism and optimize the pyrolysis process.

[0074] The peripheral component includes:

[0075] The laser confocal microscope is used for providing high-resolution and high-contrast microscopic images and capturing fluorescence lifetime images in real time, and includes a microscope, a laser light source, a scanning device, a detector, an image output device, an optical device, and a confocal system, and is signal-connected with the three-dimensional reconstruction visualization platform.

[0076] The high-temperature adaptive sample chamber is used for providing a high-temperature environment for the occurrence of the organic matter conversion reaction of the oil shale sample, is made of a high-temperature resistant material, and allows laser to penetrate at the same time.

[0077] The pyrolysis reactor is used for providing reaction conditions required for the pyrolysis of the oil shale, and adjusts the pyrolysis temperature in real time through dynamic temperature control, and includes a reactor body, a heating element, a temperature sensor, and a control system, wherein the temperature sensor and the control system are signal-connected with the real-time data processing module and the dynamic temperature control module.

[0078] In the above embodiment, the laser confocal microscope can provide high-resolution and high-contrast microscopic images, which enables the system to more accurately capture the subtle changes of the oil shale sample in the pyrolysis process. By capturing fluorescence lifetime images in real time, the laser confocal microscope can reveal the chemical conversion dynamics of organic matter in the pyrolysis process, providing rich information for analysis. The laser confocal microscope is signal-connected with the three-dimensional reconstruction visualization platform, so that the captured fluorescence lifetime images can be directly used for the reconstruction and visualization of the three-dimensional model, enhancing the intuitiveness and real-time of the monitoring.

[0079] In the above embodiment, the high-temperature adaptive sample chamber is made of a high-temperature resistant material and can withstand the high-temperature environment required for the pyrolysis of the oil shale, ensuring the continuity and stability of the experiment, and the sample chamber allows laser to penetrate, which ensures that the laser confocal microscope can observe the sample in real time under a high-temperature environment, thereby not affecting the monitoring of the pyrolysis process.

[0080] In the above embodiment, the pyrolysis reactor is adjusted in real time by the dynamic temperature control module, which provides flexible reaction conditions for oil shale pyrolysis, enabling the system to adapt to the pyrolysis needs of different samples. The built-in temperature sensor can monitor the temperature inside the reactor in real time, and through the signal connection of the control system with the real-time data processing module and the dynamic temperature control module, it ensures that the pyrolysis process is carried out under precise temperature conditions. Through real-time data feedback and dynamic regulation, the pyrolysis reactor can optimize the pyrolysis efficiency, improve the conversion rate of oil shale and the quality of the products.

[0081] The real-time data processing module comprises:

[0082] The data preprocessing unit is configured to:

[0083] The data preprocessing unit is configured to:

[0084] The fluorescence lifetime analysis unit is configured to:

[0085] The fluorescence lifetime analysis unit is configured to:

[0086] In the above embodiment, by performing preliminary data cleaning, format conversion and standardization processing on the original reaction data and fluorescence lifetime image, the data preprocessing unit can ensure that the data input to the subsequent analysis module is clean, accurate and uniform in format, avoiding analysis errors caused by data quality problems and improving the reliability of the entire system. Identifying and removing abnormal values, repeated values or invalid data in the data further improves the purity and accuracy of the data, providing a more reliable data basis for subsequent analysis. Converting the data to a uniform format enables the system to process data from different sources and formats, enhancing the flexibility and versatility of the system.

[0087] In the above embodiment, by loading the pre-processed fluorescence lifetime image data and using TCSPC technology to accurately measure the time of fluorescence photons reaching the detector, the unit can accurately construct the fluorescence decay curve and calculate the fluorescence lifetime, providing key parameters for analyzing the chemical conversion of organic matter in the oil shale pyrolysis process. The fluorescence lifetime analysis unit can monitor the chemical conversion of organic matter in the oil shale pyrolysis process in real time, and correlate the fluorescence lifetime value with the chemical conversion of organic matter. The correlation analysis helps to understand the change mechanism of organic matter in the pyrolysis process, and provides strong support for optimizing the pyrolysis conditions and controlling the product quality. The monitoring report generated includes the fluorescence lifetime change trend, the chemical conversion degree of organic matter, and the pyrolysis process rate, providing intuitive and comprehensive information for researchers and management personnel, which helps to analyze the performance of the pyrolysis process and guide the actual production operation, improving the efficiency and economic benefits of the oil shale pyrolysis process.

[0088] The dynamic temperature control module comprises:

[0089] The adjustment instruction generation unit is configured to:

[0090] The reaction data and fluorescence lifetime image data provided by the real-time data processing module are used as input parameters of the algorithm, and a preset temperature adjustment algorithm is used to generate pyrolysis temperature adjustment instructions, including temperature set value, adjustment rate and adjustment direction. The generated pyrolysis temperature adjustment instructions are sent to the peripheral component to guide the dynamic regulation of the pyrolysis temperature.

[0091] The adjustment effect evaluation unit is configured to:

[0092] The actual temperature data of the peripheral component after receiving the temperature adjustment instruction is collected, and the actual temperature data is compared with the preset temperature range to evaluate the effect of dynamic temperature adjustment, and the temperature adjustment instruction is adjusted according to the evaluation result.

[0093] In the above embodiment, by receiving the reaction data and fluorescence lifetime image data provided by the real-time data processing module, the adjustment instruction generation unit can use the preset temperature adjustment algorithm to generate accurate pyrolysis temperature adjustment instructions, ensuring that the pyrolysis process is carried out in the best temperature range, thereby optimizing the pyrolysis efficiency and reducing unnecessary energy consumption. The generated adjustment instructions include temperature set value, adjustment rate and adjustment direction, so that the system can flexibly adjust the temperature parameters according to the real-time changes of the pyrolysis process. The dynamic temperature control strategy helps to improve the stability and controllability of the pyrolysis process. The adjustment instruction generation unit can generate temperature adjustment instructions in real time and send them to the peripheral component to guide the real-time regulation of the pyrolysis temperature, ensuring that the pyrolysis process can quickly respond to changes in external conditions, thereby improving the response speed and adaptability of the system.

[0094] In the above embodiments, by collecting the actual temperature data of the peripheral components after receiving the temperature adjustment instructions and comparing it with the preset temperature range, the adjustment effect evaluation unit can evaluate the effect of dynamic temperature adjustment. If the actual temperature data does not conform to the preset range, the evaluation unit will make feedback adjustments to optimize the temperature adjustment instructions, thereby improving the accuracy and stability of temperature control. The feedback adjustment mechanism based on the evaluation results enables the system to continuously learn and improve, optimizing the temperature control strategy, and the continuous improvement process helps to improve the efficiency, stability and reliability of the pyrolysis process, ultimately improving the performance of the entire online monitoring system.

[0095] The three-dimensional reconstruction visualization platform comprises:

[0096] The three-dimensional imaging processing unit is configured to:

[0097] Based on the real-time obtained reaction data and fluorescence lifetime image, the three-dimensional reconstruction algorithm is used to voxelize, surface reconstruct the received data, convert the reaction data and fluorescence lifetime image into voxel data in three-dimensional space, on the basis of voxelization, isosurface extraction constructs a three-dimensional surface model of organic matter conversion in the pyrolysis process, generates a three-dimensional model of organic matter conversion in the oil shale pyrolysis process, the three-dimensional model includes the spatial distribution, morphological change and possible chemical reaction path of organic matter.

[0098] The visualization display unit is configured to:

[0099] Load the optimized three-dimensional model data, parse the three-dimensional model data, initialize the AR environment, build a virtual scene to display the three-dimensional model, render the parsed three-dimensional model data to the virtual scene, visualize the reconstructed three-dimensional model based on AR, provide interactive functions for visualization display by implementing user input processing logic, the interactive functions include rotation, scaling, translation, when the user performs interactive operation, the visualization display unit updates the three-dimensional model state in the virtual scene in real time, and displays the updated visual feedback.

[0100] In the above embodiments, by using the three-dimensional reconstruction algorithm, the unit can accurately convert the reaction data and fluorescence lifetime image into voxel data in three-dimensional space, through isosurface extraction and surface reconstruction, the unit can construct a three-dimensional surface model of organic matter conversion in the pyrolysis process, so that the spatial distribution, morphological change and possible chemical reaction path of organic matter can be intuitively displayed, providing a powerful visualization tool for researchers. By optimizing the three-dimensional model, the unit can further improve the accuracy and stability of the model. The optimized model more accurately reflects the actual situation of oil shale pyrolysis process, providing a more reliable data basis for subsequent monitoring and analysis.

[0101] In the above embodiments, through the AR technology, the unit can display the reconstructed three-dimensional model to the user in a more realistic and intuitive way, not only improving the user's immersion, but also making the monitoring results more easily understood and accepted. By implementing interactive functions such as rotation, scaling, and translation, the unit provides users with a more flexible and convenient operation experience. Users can freely adjust the viewing angle, zoom in and out of the model, and translate the observation position according to their own needs, so as to more comprehensively understand the details and characteristics of the oil shale pyrolysis process. When the user performs interactive operations, the unit can update the state of the three-dimensional model in the virtual scene in real time and display the updated visual feedback to the user. This real-time visual feedback not only improves the user's operation experience, but also makes the monitoring results more real-time and dynamic.

[0102] On the basis of the foregoing embodiments, the online monitoring system further comprises a pyrolysis environmental impact analysis module, which comprises:

[0103] a fluorescence decay reference curve unit for:

[0104] obtaining the initial fluorescence intensity in the pyrolysis process, and drawing a fluorescence decay reference curve according to the initial fluorescence intensity, a set standard fluorescence lifetime, and the following fluorescence decay function:

[0105]

[0106] wherein I 0 is the fluorescence intensity decay function; I0 is the initial fluorescence intensity used in pyrolysis; k i is the relative weight of the i-th fluorescence component; t is the time elapsed by fluorescence; e is the natural constant; τ0 is the set standard fluorescence lifetime;

[0107] a decay comparison unit for:

[0108] comparing the fluorescence decay curve constructed using the preprocessed fluorescence lifetime image data with the fluorescence decay reference curve, and calculating the similarity of the two using the formula:

[0109]

[0110] wherein S is the similarity of the fluorescence decay curve and the fluorescence decay reference curve; n is the total number of corresponding comparison data points of the fluorescence decay curve and the fluorescence decay reference curve; I j is the fluorescence intensity of the j-th corresponding comparison data point on the fluorescence decay curve; I 0 j is the fluorescence intensity of the j-th corresponding comparison data point on the fluorescence decay reference curve;

[0111] a pyrolysis evaluation and anomaly analysis unit for:

[0112] The similarity of the calculated fluorescence decay curve and the fluorescence decay reference curve is compared with the similarity threshold value, and if the similarity is less than the similarity threshold value, it indicates that there is an abnormality in the pyrolysis process, the pyrolysis influence analysis of the environmental data is carried out based on the environmental data synchronously measured in the pyrolysis process, and the pyrolysis environmental data control scheme is output according to the analysis result.

[0113] In the above embodiment, by introducing the fluorescence decay function, according to the standard fluorescence lifetime set by the good pyrolysis process, adopting the determined initial fluorescence intensity condition, through the multi-point time data substitution calculation, the fluorescence decay reference curve is obtained by drawing the curve according to the multi-point calculation result, which is used as a reference; the fluorescence decay curve constructed according to the real-time data is compared with the fluorescence decay reference curve; the similarity calculation formula is used for comparison, when the two curves are exactly the same, the similarity is 1; the smaller the similarity is, the more the fluorescence decay curve deviates from the fluorescence decay reference curve, combined with the similarity threshold value, whether the pyrolysis process reflected by the fluorescence decay curve is abnormal is judged, the algorithm scheme can enhance the objectivity and improve the reliability; if there is an abnormality, the pyrolysis influence analysis of the environmental data synchronously measured in the pyrolysis process is carried out, the environmental data refers to the surrounding condition data influenced by the pyrolysis process, such as climate, process temperature, etc., and the pyrolysis environmental data control scheme is output according to the analysis result, so as to adjust the pyrolysis process control, timely correct the deviation, and ensure the process efficiency, yield and product quality.

[0114] On the basis of the foregoing embodiment, the temperature regulation algorithm preset by the dynamic temperature control module includes a demand process temperature prediction model, and the demand process temperature prediction model is constructed in the following manner:

[0115] Firstly, a convolutional neural network including an input layer, a hidden layer and an output layer is constructed, the hidden layer includes a convolutional layer, a pooling layer and a fully connected layer, and the convolutional kernel in the convolutional layer, the convolutional layer parameters and the excitation function are determined;

[0116] Secondly, the pyrolysis process data of oil shale in the historical records is obtained, the pyrolysis process data includes historical reaction data, historical fluorescence lifetime image data and historical process temperature data, and the data is cleaned to remove abnormal data;

[0117] Thirdly, the pyrolysis process data is selected and grouped, and the training data set, the verification data set and the test data set are respectively established by using the pyrolysis process data in different groups;

[0118] Finally, the training data set, the verification data set and the test data set are sequentially used for data training, verification parameter adjustment and testing of the convolutional neural network, and the process is ended when the test result meets the predetermined accuracy requirement, and the demand process temperature prediction model is obtained.

[0119] In the above embodiment, by constructing the demand process temperature prediction model into the temperature adjustment algorithm preset in the dynamic temperature control module, the demand process temperature prediction model is used to predict the process temperature required at the next moment in the pyrolysis process, so as to control the process temperature of the pyrolysis process, realize the dynamics and adaptability of the process temperature control, improve the process temperature control precision, and guarantee the pyrolysis effect and efficiency; the scheme provides a construction and training method of the demand process temperature prediction model, adopts a convolutional neural network as a model basis, and through the above training, the prediction result of the demand process temperature prediction model has high precision, so that the prediction purpose can be achieved.

[0120] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can make equivalent replacement or change according to the technical scheme and inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. An online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime is characterized by: include: Peripheral components for: Providing a high-temperature environment for an organic matter conversion reaction to occur in an oil shale sample, subjecting the oil shale sample to a pyrolysis reaction, and capturing fluorescence lifetime images in real time through a laser confocal microscope. Simultaneously, dynamically controlling the pyrolysis temperature during the organic matter conversion reaction of the oil shale sample is provided. The peripheral component is signal-connected to a real-time data processing module, a dynamic temperature control module, and a three-dimensional reconstruction visualization platform, collecting reaction data and fluorescence lifetime images in real time and sending them to the real-time data processing module. Real-time data processing module for: Based on the reaction data and fluorescence lifetime images generated by the organic matter conversion reaction provided by the peripheral components to the oil shale samples, data processing and analysis of fluorescence lifetime changes are performed to monitor the chemical conversion of organic matter during the oil shale pyrolysis process in real time; Dynamic temperature control module for: generating a pyrolysis temperature adjustment instruction based on the reaction data and fluorescence lifetime image data acquired by the real-time data processing module, and sending the pyrolysis temperature adjustment instruction to the peripheral component; 3D reconstruction visualization platform for: Imaging processing is performed based on the real-time reaction data and fluorescence lifetime images. According to the fluorescence lifetime image data and imaging results, a three-dimensional model of organic matter transformation during oil shale pyrolysis is reconstructed and visualized.

2. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 1, characterized in that: The peripheral components include: A laser confocal microscope is used to provide high-resolution and high-contrast microscopic images and capture fluorescence lifetime images in real time. The laser confocal microscope includes a microscope, a laser light source, a scanning device, a detector, an image output device, an optical device, and a confocal system. The laser confocal microscope is connected to a three-dimensional reconstruction visualization platform. High-temperature adaptable sample chamber, used to provide a high-temperature environment for organic matter conversion reactions to occur in oil shale samples; The pyrolysis reactor is used to provide the reaction conditions required for the pyrolysis of oil shale and adjust the pyrolysis temperature in real time through dynamic temperature control. The pyrolysis reactor includes a reactor body, a heating element, a temperature sensor and a control system, wherein the temperature sensor and the control system are both connected to the real-time data processing module and the dynamic temperature control module signal.

3. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 1, characterized in that: The real-time data processing module includes: Data pre-processing unit, used for: Receive the raw reaction data and fluorescence lifetime images sent by peripheral components in real time, perform preliminary data cleaning, format conversion and standardization on the raw reaction data and fluorescence lifetime images, identify and eliminate outliers, duplicate values ​​and invalid data in the data, and convert the received data into a unified format.

4. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 3, characterized in that: The real-time data processing module further includes: Fluorescence lifetime analysis unit, used for: The preprocessed fluorescence lifetime image data is loaded, and based on the preprocessed fluorescence lifetime image data, the time it takes for fluorescence photons to reach the detector is measured by TCSPC, a fluorescence decay curve is constructed, the fluorescence lifetime is calculated, and the fluorescence decay curve is fitted to obtain a fluorescence lifetime value. The chemical conversion of organic matter during the pyrolysis of oil shale is monitored in real time, the fluorescence lifetime value is correlated with the chemical conversion of organic matter during the pyrolysis of oil shale, and a monitoring report is generated. The content of the monitoring report includes the changing trend of the fluorescence lifetime, the degree of chemical conversion of organic matter, and the rate of the pyrolysis process.

5. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 1, characterized in that: The dynamic temperature control module includes: The adjustment instruction generation unit is used to: The reaction data and fluorescence lifetime image data provided by the real-time data processing module are used as input parameters of the algorithm. The preset temperature adjustment algorithm is used to generate a pyrolysis temperature adjustment instruction. The adjustment instruction includes a temperature setting value, an adjustment rate, and an adjustment direction. The generated pyrolysis temperature adjustment instruction is sent to the peripheral component to guide the peripheral component to dynamically control the pyrolysis temperature.

6. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 5, characterized in that: The dynamic temperature control module further includes: Regulation effect evaluation unit, used for: Collect the actual temperature data of the peripheral components after receiving the temperature adjustment command, compare the actual temperature data with the preset temperature range, evaluate the effect of dynamic temperature adjustment, and make feedback adjustments to the temperature adjustment command based on the evaluation results.

7. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 1, characterized in that: The three-dimensional reconstruction visualization platform includes: 3D imaging processing unit, used for: Based on the real-time reaction data and fluorescence lifetime images, a three-dimensional reconstruction algorithm is used to voxelize and reconstruct the received data, converting the reaction data and fluorescence lifetime images into voxel data in three-dimensional space. Based on the voxelization, isosurface extraction is used to construct a three-dimensional surface model of organic matter transformation during pyrolysis, generating a three-dimensional model of organic matter transformation during oil shale pyrolysis. The three-dimensional model includes the spatial distribution of organic matter, morphological changes, and possible chemical reaction pathways.

8. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 7, characterized in that: The three-dimensional reconstruction visualization platform further includes: Visual display unit for: Load the optimized 3D model data, perform data analysis on the 3D model, initialize the AR environment, build a virtual scene to display the 3D model, render the analyzed 3D model data into the virtual scene, and visualize the reconstructed 3D model based on AR. Provide interactive functions for the visualization by implementing user input processing logic. The interactive functions include rotation, scaling, and translation. When the user performs interactive operations, the visualization display unit updates the 3D model status in the virtual scene in real time and displays the updated visual feedback.

9. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 4, characterized in that: It also includes a pyrolysis environment impact analysis module, which includes: Fluorescence decay reference curve unit, used for: The initial fluorescence intensity during the pyrolysis process was obtained, and the fluorescence decay reference curve was obtained by plotting the initial fluorescence intensity, the set standard fluorescence lifetime, and the following fluorescence decay function: Among them, I 0 is the fluorescence intensity decay function; I0 is the initial fluorescence intensity used for pyrolysis; k i is the relative weight of the th fluorescence component; t is the time of fluorescence; e is a natural constant; τ0 is the set standard fluorescence lifetime; Attenuation comparison unit, used for: The fluorescence decay curve constructed using the preprocessed fluorescence lifetime image data is compared with the fluorescence decay reference curve, and the similarity between the two is calculated using the formula: Where S is the similarity between the fluorescence decay curve and the fluorescence decay reference curve; n is the total number of corresponding comparison data points between the fluorescence decay curve and the fluorescence decay reference curve; I j is the fluorescence intensity of the corresponding comparison data point on the fluorescence decay curve; I 0 j is the fluorescence intensity of the corresponding comparison data point on the fluorescence decay reference curve; Pyrolysis Evaluation and Anomaly Analysis Unit for: The similarity between the calculated fluorescence decay curve and the fluorescence decay reference curve is compared with the similarity threshold. If the similarity is less than the similarity threshold, it means that there is an abnormality in the pyrolysis process. Based on the environmental data obtained by synchronous measurement during the pyrolysis process, the pyrolysis impact analysis of the environmental data is performed, and the pyrolysis environmental data control plan is output according to the analysis results.

10. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 5, characterized in that: The temperature adjustment algorithm preset by the dynamic temperature control module includes a demand process temperature prediction model, which is constructed in the following way: First, a convolutional neural network is constructed, which includes an input layer, a hidden layer, and an output layer. The hidden layer includes a convolutional layer, a pooling layer, and a fully connected layer. The convolution kernel, convolutional layer parameters, and activation function in the convolutional layer are determined. Secondly, the pyrolysis process data of oil shale in historical records is obtained. The pyrolysis process data includes historical reaction data, historical fluorescence lifetime image data, and historical process temperature data. The data is cleaned to remove abnormal data. Thirdly, the pyrolysis process data are selected and grouped, and the training data set, validation data set and test data set are respectively established based on the pyrolysis process data of different groups; Finally, the training data set, validation data set and test data set are used in sequence to perform data training, parameter verification and testing on the convolutional neural network until the test results meet the predetermined accuracy requirements, and the required process temperature prediction model is obtained.

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