A method and system for monitoring catalyst reaction processes

Abstract

This invention provides a method and system for monitoring catalyst reaction processes, belonging to the field of infrared thermometry technology. The method includes: acquiring single-frame thermal imaging images of a porous catalyst; identifying and segmenting the pores in the single-frame images to determine porous regions with different structures; sequentially acquiring infrared thermal imaging images of the porous catalyst according to a preset acquisition time, using the previous frame as a reference frame, acquiring the global extreme values ​​of each pore region in the current frame and the reference frame as temperature difference characteristic indicators, and determining the temperature difference matrix of each pore region in the current frame and the reference frame based on the temperature difference characteristic indicators; converting the temperature difference matrix of each pore region into a high-contrast thermal image based on the temperature difference matrix of each pore region; and sequentially outputting the high-contrast thermal images of each frame according to the acquisition time sequence to form an evolutionary thermal map of each pore region, showing the temperature difference and temperature change rate of each pore region. This monitoring method can automatically track the reaction front or analyze the dynamic process of temperature change over time.

Description

Technical Field

[0001] This invention relates to the field of infrared thermometry, and more specifically to a method and system for monitoring catalyst reaction processes. Background Technology

[0002] With the rapid development of computer vision, modern chemical engineering, energy materials, and environmental catalysis, higher demands are being placed on the precise monitoring of catalytic reaction processes. Temperature, as a key factor influencing the rate, selectivity, and stability of catalytic reactions, reveals not only the active regions and mechanistic pathways of the reaction but also provides important references for reactor design and industrial scale-up. Therefore, developing high-resolution, real-time, and intelligent temperature monitoring software is of significant scientific and engineering value for a deeper understanding of catalytic processes and the achievement of refined control.

[0003] Currently, traditional thermocouple temperature measurement methods are still widely used in catalytic reaction experiments, but they can only provide limited single-point temperature information and are difficult to reflect the complex two-dimensional temperature field distribution on the catalyst surface. Against this backdrop, infrared thermal imaging technology has been introduced into catalytic reaction research, becoming an important non-contact temperature measurement method. However, existing infrared thermal imaging systems still have many shortcomings in terms of functionality and adaptability, restricting their further application in catalytic research. Mainstream thermal imaging software is mostly based on static image analysis and cannot automatically track the reaction front or analyze the dynamic process of temperature changes over time, lacking real-time response capabilities to reaction behavior. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a method and system for monitoring catalytic reaction processes. This monitoring method can automatically track the reaction front or analyze the dynamic process of temperature change over time.

[0005] To achieve the above objectives, the present invention provides the following technical solution.

[0006] A method for monitoring a catalytic reaction process includes the following steps: Acquire single-frame images of porous catalyst thermal imaging, identify and segment pores in the single-frame images, obtain pore mask coordinates of multiple pores and adaptively measure the radius, calibrate the pore mask coordinates according to the pre-stored pore parameter database, and determine the porous regions of different structures. According to the preset acquisition time, thermal imaging infrared images of porous catalyst are acquired sequentially. The previous frame image is used as the reference frame. The global extreme values ​​of each pore region in the current frame and the reference frame are obtained as temperature difference feature indicators. The temperature difference matrix of each pore region in the current frame and the reference frame is determined based on the temperature difference feature indicators. Based on the temperature difference matrix of each channel region, the temperature difference matrix is ​​converted into a high-contrast thermal image; according to the acquisition time sequence, the high-contrast thermal images of each frame are output sequentially to form the evolution thermal image of each channel region, and the temperature difference and temperature change rate of each channel region.

[0007] Preferably, the hole identification and segmentation of a single frame image adopts an improved Mask R-CNN architecture; the improved Mask R-CNN architecture uses a lightweight ResNet-34-FPN as the backbone network, utilizes deformable convolution to enhance edge feature extraction, and introduces a U-Net structure as a mask prediction head; The improved Mask R-CNN architecture employs a loss function jointly optimized by Dice Loss and cross-entropy, and is trained using a labeled infrared catalyst image dataset; the loss function is: ; ; ; In the formula, These are the weighting coefficients; To compare the predicted mask with the actual mask; y i These are the actual mask pixel values. p i To predict probabilities.

[0008] Preferably, the adaptive measurement radius formula adopts a logarithmic adjustment model, as shown in the following equation: ; In the formula, The reference radius; The input image resolution; This represents the upper limit of the radius.

[0009] Preferably, the calibration of the aperture mask coordinates based on a pre-stored aperture parameter database includes the following steps: The alignment of the aperture mask is optimized using a similarity transformation matrix, as follows: , ; In the formula, To predict the coordinates of the apex of the channel; For reference to the theoretical coordinates of the duct; These are the scaling, rotation, and translation parameters, respectively.

[0010] Preferably, the construction of the high-contrast heatmap includes the following steps: Convert infrared images into a temperature matrix With reference matrix Then the temperature difference matrix is: ; Introducing environmental noise compensation items Eliminate environmental thermal radiation interference: ; The temperature difference matrix is ​​converted into a high-contrast thermal image using pseudo-color mapping technology.

[0011] Preferably, the reference frame is specified as input during fixed-point comparison analysis, and any keyframe is used as the reference frame for the static benchmark.

[0012] Preferably, the display threshold of the high-contrast heatmap is set by input or automatically optimized by a dynamic statistical threshold algorithm, and the display range is weighted and fused with the input setting to obtain the final visualization scale; The dynamic statistical threshold algorithm includes the following steps: The length of the continuously acquired temperature data is [length missing]. N A sliding window is used to calculate the temperature variance of the data within the window. σ ² and mean μ ; Based on the preset confidence coefficient k Generate dynamic threshold range [ μ - kσ , μ + kσ ]; The dynamic threshold range is fused with the user-defined fixed threshold through a weighting factor to form the final visual display scale.

[0013] Preferably, the calibration of the infrared image temperature value is as shown in the following formula: ; In the formula, The radiation coefficient, For ambient temperature compensation, For edge attenuation term, The attenuation coefficient is... d The distance from a pixel to the center of the image. d 0 represents the total distance.

[0014] Preferably, the temperature change rate is calculated as shown in the following equation: .

[0015] This invention also proposes a monitoring system for a catalyst reaction process, comprising: processor; A memory on which computer programs that can run on the processor are stored; The computer program, when executed by the processor, implements the steps of the method for monitoring the catalyst reaction process.

[0016] The beneficial effects of this invention are: This invention proposes a method for monitoring catalytic reaction processes, enabling real-time and accurate monitoring of the dynamic temperature field and automatic multi-region analysis during the reaction. This method employs deep learning-driven instance segmentation technology to automatically segment independent pore regions in multi-channel catalyst images, adaptively identifying temperature observation targets in reactors with different structures without manual calibration. Simultaneously, by combining a multi-target temperature tracking method based on inter-frame difference and optical flow estimation, it continuously tracks the temperature evolution of each region over time, dynamically marks regions of abrupt temperature changes, and automatically generates a thermal map of the reaction front evolution, effectively capturing instantaneous reaction behavior. For temperature calculation, this method converts infrared image pixel values ​​into physical temperatures based on a calibration model and integrates a sliding filter algorithm for dynamic noise reduction. Configurable filters and color mapping mechanisms can flexibly generate high-definition, resolution-adaptive thermal maps. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method according to an embodiment of the present invention.

[0018] Figure 2 This is a flowchart of the image segmentation process according to an embodiment of the present invention.

[0019] Figure 3 This is a flowchart of the dynamic temperature difference detection algorithm according to an embodiment of the present invention.

[0020] Figure 4 This is a flowchart illustrating the data encryption process according to an embodiment of the present invention.

[0021] Figure 5 This is an overall system architecture diagram of an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0023] Example 1 Most mainstream thermal imaging software focuses on static image analysis and cannot automatically track the reaction front or analyze the dynamic process of temperature changes over time, lacking real-time response capabilities to reaction behavior. Their regional adaptability is limited; when dealing with porous catalysts or complex reactor structures, each temperature measurement area often needs to be manually calibrated, which is cumbersome and lacks versatility. Furthermore, temperature measurement results are often isolated from experimental parameters, failing to achieve automatic synchronization and correlation analysis, increasing the data processing burden. Therefore, this embodiment proposes a method for monitoring catalytic reaction processes, the specific steps of which are as follows: Figure 1 As shown, it includes:

[0024] S1: Acquire a single-frame image of the porous catalyst thermal imaging, identify and segment the pores in the single-frame image, obtain the pore mask coordinates of multiple pores, and adaptively measure the radius.

[0025] S2: The coordinates of the pore mask are calibrated according to the pre-stored pore parameter database to determine the multi-pore regions of different structures.

[0026] S3: According to the preset acquisition time, sequentially acquire thermal imaging infrared images of the porous catalyst, take the previous frame as the reference frame, obtain the global extreme values ​​of each pore region in the current frame and the reference frame as temperature difference feature indicators, and determine the temperature difference matrix of each pore region in the current frame and the reference frame based on the temperature difference feature indicators.

[0027] S4: Based on the temperature difference matrix of each channel region, the temperature difference matrix is ​​converted into a high-contrast thermal image; according to the acquisition time sequence, the high-contrast thermal images of each frame are output sequentially to form the evolution thermal image of each channel region, and the temperature difference and temperature change rate of each channel region.

[0028] Specifically, the segmentation of the multi-channel regions in S1 and S2 is performed by a multi-channel temperature recognition and statistics system. This invention employs an instance segmentation technique based on an improved Mask R-CNN architecture; the specific image segmentation flowchart is shown below. Figure 2 As shown, a closed-loop processing system for intelligent thermal analysis of porous catalysts was constructed. The instance segmentation training and application flowchart details this complete process: an improved Mask R-CNN architecture is adopted, with a lightweight ResNet-34-FPN backbone network and deformable convolutions to enhance pore edge feature extraction. The mask prediction head is designed with a U-Net encoding / decoding structure to improve segmentation accuracy. During training, a labeled infrared catalyst dataset is used, combined with data augmentation strategies such as emissivity perturbation and elastic deformation, and a loss function jointly optimized by DiceLoss and cross-entropy is employed (validation set Dice≥0.95). The trained model outputs pore mask coordinates, which are then processed by morphological skeleton extraction and ellipse fitting to generate standardized parameters, achieving fully automated processing from image input to pore localization.

[0029] First, a high-precision segmentation model is trained on a labeled infrared catalyst dataset. Through data augmentation, ResNet backbone optimization, and mask prediction head fine-tuning, a strategy combining Dice Loss and cross-entropy is employed for optimization. The trained instance segmentation network accurately locates the catalyst pore structure. Its output pore mask coordinates are converted into a standard data structure (including two-dimensional coordinates and an adaptive measurement radius) via a dedicated parsing service. This data is automatically matched and calibrated against a pre-stored pore parameter database in Excel. In practical applications, the system can process input catalyst thermal images in real time, dynamically generating a scalable statistical list based on the user-selected pore set. The measurement radius is intelligently adjusted according to image resolution to ensure spatial consistency. The integrated temperature statistical analysis engine can calculate the maximum, minimum, and average temperatures of the target area in real time. Combined with a dual-buffer refresh mechanism, it enables dynamic tracking and accurate statistical analysis of pore temperatures during continuous playback or single-frame pause. The final processing results are presented synchronously through a visualization interface, which outputs temperature distribution heat maps with labeled pore numbers and generates exportable statistical reports. This achieves fully automated processing from instance segmentation network training, image processing, dynamic analysis to result visualization, providing a reliable intelligent solution for the thermal performance analysis of porous catalysts.

[0030] Specifically, the model's loss function is: ; ; ; In the formula, These are the weighting coefficients; To compare the predicted mask with the actual mask; y i These are the actual mask pixel values. p i To predict probabilities.

[0031] The adaptive measurement radius formula, employing a logarithmic adjustment model, is shown in the following equation: ; In the formula, The reference radius; The input image resolution; This represents the upper limit of the radius.

[0032] The aperture mask coordinates are calibrated based on a pre-stored aperture parameter database, including the following steps: The alignment of the aperture mask is optimized using a similarity transformation matrix, as follows: , ; In the formula, To predict the coordinates of the apex of the channel; For reference to the theoretical coordinates of the duct; These are the scaling, rotation, and translation parameters, respectively.

[0033] The dynamic temperature difference detection of this invention achieves real-time, high-sensitivity analysis of the temperature field based on the Multi-modal Frame Difference (MFD) algorithm. The detection process is driven by a clock-triggered mechanism: within each timing cycle, the current processing status is first checked; if not in pause mode, the infrared thermal image data of the current frame is automatically acquired. The selection of the reference frame employs a dual-mode strategy: it supports user-specified arbitrary keyframes as static references (suitable for fixed-point comparative analysis), while the default mode dynamically correlates with the previous frame data (meeting the needs of continuous process monitoring), thus balancing the flexibility and real-time performance of the analysis.

[0034] The core processing step calls the temperature difference calculation function to accurately solve the pixel-level temperature difference between the current frame and the reference frame through matrix operations, and extracts the global extremum. ( ) serves as a temperature difference characteristic indicator. A human-machine collaborative temperature difference range determination mechanism is adopted: it allows users to manually set the display threshold to focus on key temperature change ranges, and it can also automatically optimize the display range through a dynamic statistical threshold algorithm. The two are combined through a weighted fusion strategy to generate the final visual scale.

[0035] Specifically, the dynamic statistical thresholding algorithm for high-contrast heatmaps includes the following steps: First, establish a thresholding algorithm for continuously acquired temperature data with a length of [missing information]. N A sliding window is used to calculate the temperature variance of the data within the window. σ ² and mean μ Secondly, based on the preset confidence coefficient... k (Typical value is 2.58), generating dynamic threshold intervals [ μ - kσ , μ + kσ Finally, the dynamic threshold range and the user-defined fixed threshold are weighted by a factor. α =0.6 (0≤ α ≤1) are fused to form the final visual display scale. This method enables real-time adaptive monitoring of temperature changes during the reaction process, significantly improving detection sensitivity and accuracy.

[0036] In terms of result presentation, pseudo-color mapping technology is used to convert the temperature difference matrix into a high-contrast heat map, and a dynamic scale color bar is integrated (supporting the gradual display of positive and negative temperature differences) to achieve intuitive identification of the heating zone (red series) and the cooling zone (blue series). The playback control module uses double buffering technology to manage the frame sequence index, ensuring real-time performance while avoiding interface rendering stutters, keeping the visual latency of dynamic temperature difference detection within 50ms, significantly improving the user interaction experience and the ability to capture micro-temperature differences.

[0037] Specifically, such as Figure 3 As shown, the construction of a high-contrast heatmap includes the following steps: Convert infrared images into a temperature matrix With reference matrix Then the temperature difference matrix is: ; Introducing environmental noise compensation items Eliminate environmental thermal radiation interference: ; The temperature difference matrix is ​​converted into a high-contrast thermal image using pseudo-color mapping technology.

[0038] The calibration of infrared image temperature values ​​is shown in the following formula: ; In the formula, The radiation coefficient, For ambient temperature compensation, For edge attenuation term, The attenuation coefficient is... d The distance from a pixel to the center of the image. d 0 represents the total distance.

[0039] The rate of temperature change is calculated as shown in the following equation: .

[0040] Furthermore, this invention achieves multi-target temperature dynamic tracking and data correlation. Based on instance segmentation technology, this invention automatically identifies and independently numbers multi-channel regions, forming a multi-target temperature dynamic tracking mechanism. By independently tracking each identified region in a continuous frame sequence, this invention captures the time-series changes in temperature of each channel in real time, automatically calculating regional temperature differences, temperature change rates, and local anomalies. This tracking mechanism effectively improves the ability to capture transient thermal phenomena during the reaction process. Simultaneously, this invention achieves automatic synchronous correlation between temperature data and catalytic reaction parameters (such as gas composition, reaction time, flow rate, etc.), forming multi-modal data fusion. Through structured data output, users can easily perform subsequent reaction mechanism analysis and data mining, greatly enhancing the application depth and analysis efficiency of experimental data.

[0041] To ensure system security and data confidentiality, this invention introduces a hardware-bound authorization mechanism. This invention generates a unique license key by reading hardware fingerprint information such as the device's motherboard serial number and performs environmental verification during software startup and operation to ensure that only authorized devices can use the software functions normally. All temperature measurement data is automatically encrypted and stored to prevent unauthorized access, copying, or tampering, meeting the stringent data confidentiality requirements of industrial environments. Furthermore, the authorization control mechanism effectively prevents the illegal dissemination of the software, protecting the intellectual property rights of the research results. A detailed flowchart is shown below. Figure 4 As shown.

[0042] Furthermore, this invention provides a user-friendly interface that supports functions such as file import, channel selection, temperature statistics mode switching, and dynamic playback control. The interface design features a dynamic color bar on the temperature difference image, and the temperature scale adjusts in real time based on the data, allowing users to intuitively understand the temperature distribution and trends. Multi-channel areas are marked with dynamically scaling circles on the heat map, supporting the simultaneous display of multiple channels. Users can view temperature information and trend curves for different areas in real time through the interface. This interactive design enhances the software's operability and analytical efficiency, making complex temperature monitoring tasks intuitive and efficient.

[0043] The above is one embodiment of the method for monitoring a catalyst reaction process provided in this example. Based on the same idea, this example also provides a corresponding monitoring system for a catalyst reaction process. Specific limitations of the monitoring system for the catalyst reaction process can be found in the limitations of the monitoring method for the catalyst reaction process described above, and will not be repeated here. Each module in the above-described monitoring system for the catalyst reaction process can be implemented entirely or partially through software, hardware, or a combination thereof.

[0044] The specific system architecture diagram is as follows: Figure 5This system integrates multiple functional modules, including infrared image temperature calculation, dynamic temperature difference detection, instance segmentation and recognition, multi-region temperature statistics, reaction parameter linkage, and hardware authorization control. Specifically, the temperature calculation module converts pixel signals in infrared images into physical temperature values ​​and uses a sliding window filtering algorithm for noise reduction. The temperature difference detection module, based on an inter-frame difference algorithm, establishes a pixel-level temperature gradient matrix and employs a dual-mode adaptive threshold strategy (fixed threshold + dynamic statistical threshold) to achieve microsecond-level temperature transient detection, significantly improving the sensitivity of temperature transient detection. The region recognition module automatically identifies each pore region in the porous catalyst structure based on instance segmentation technology, requiring no manual calibration and exhibiting strong adaptability. The temperature statistics module calculates the average, maximum, and minimum temperatures for each identified region in real time, with results supported in CSV or JSON format. The parameter linkage module automatically associates and synchronously stores temperature data with parameters such as time, flow rate, and gas composition during the experiment, enabling structured management of multimodal experimental data. Regarding data security, the software operates based on a hardware-bound authorization mechanism, generating a unique key through the motherboard serial number to restrict the operating environment and automatically encrypt data, ensuring the integrity and confidentiality of experimental data. The modules mentioned above can operate independently or work together to form a complete infrared temperature field monitoring system, which is used to achieve multi-target temperature tracking and intelligent analysis in the catalyst reaction process.

[0045] The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0046] This embodiment also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 The provided method for monitoring the catalyst reaction process.

[0047] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0048] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0049] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring a catalytic reaction process, characterized in that, Includes the following steps: Acquire single-frame images of porous catalyst thermal imaging, identify and segment pores in the single-frame images, obtain pore mask coordinates of multiple pores and adaptively measure the radius, calibrate the pore mask coordinates according to the pre-stored pore parameter database, and determine the porous regions of different structures. According to the preset acquisition time, thermal imaging infrared images of porous catalyst are acquired sequentially. The previous frame image is used as the reference frame. The global extreme values ​​of each pore region in the current frame and the reference frame are obtained as temperature difference feature indicators. The temperature difference matrix of each pore region in the current frame and the reference frame is determined based on the temperature difference feature indicators. Based on the temperature difference matrix of each channel region, the temperature difference matrix is ​​converted into a high-contrast thermal image; according to the acquisition time sequence, the high-contrast thermal images of each frame are output sequentially to form the evolution thermal image of each channel region. The multi-channel regions are independently numbered. By independently tracking each identified channel region in the continuous frame sequence, the time series change of temperature of each channel is captured in real time, and the regional temperature difference, temperature change rate and local anomalies are automatically calculated. The construction of the high-contrast heatmap includes the following steps: The infrared image is temperature calibrated and converted into a temperature matrix. With reference matrix Then the temperature difference matrix is: ； Introducing environmental noise compensation items and radiation coefficient Eliminate environmental thermal radiation interference: ； The temperature difference matrix is ​​converted into a high-contrast thermal image using pseudo-color mapping technology.

2. The method for monitoring the catalyst reaction process according to claim 1, characterized in that, The hole recognition and segmentation of single-frame images adopts an improved Mask R-CNN architecture; the improved Mask R-CNN architecture uses a lightweight ResNet-34-FPN as the backbone network, utilizes deformable convolution to enhance edge feature extraction, and introduces a U-Net structure as a mask prediction head. The improved Mask R-CNN architecture employs a loss function jointly optimized by Dice Loss and cross-entropy, and is trained using a labeled infrared catalyst image dataset; the loss function is: ； ； ； In the formula, These are the weighting coefficients; To compare the predicted mask with the actual mask; y i These are the actual mask pixel values. p i To predict probabilities.

3. The method for monitoring the catalyst reaction process according to claim 1, characterized in that, The adaptive measurement radius formula adopts a logarithmic adjustment model, as shown in the following equation: ； In the formula, The reference radius; The input image resolution; This represents the upper limit of the radius.

4. The method for monitoring the catalyst reaction process according to claim 1, characterized in that, The calibration of the duct mask coordinates based on the pre-stored duct parameter database includes the following steps: The alignment of the aperture mask is optimized using a similarity transformation matrix, as follows: ， ； In the formula, To predict the coordinates of the apex of the channel; For reference to the theoretical coordinates of the duct; These are the scaling, rotation, and translation parameters, respectively.

5. The method for monitoring the catalyst reaction process according to claim 1, characterized in that, The reference frame is specified as input during fixed-point comparison analysis, and any keyframe is used as the reference frame for the static benchmark.

6. The method for monitoring the catalyst reaction process according to claim 1, characterized in that, The display threshold of the high-contrast heatmap is set by input or the display range is automatically optimized by a dynamic statistical threshold algorithm, and the final visualization scale is obtained by weighted fusion with the input setting. The dynamic statistical threshold algorithm includes the following steps: The length of the continuously acquired temperature data is [length missing]. N A sliding window is used to calculate the temperature variance of the data within the window. σ ² and mean μ ; Based on the preset confidence coefficient k Generate dynamic threshold range [ μ - kσ , μ + kσ ]; The dynamic threshold range is fused with the user-defined fixed threshold through a weighting factor to form the final visual display scale.

7. The method for monitoring the catalyst reaction process according to claim 1, characterized in that, The calibration of the infrared image temperature value is shown in the following formula: ； In the formula, The radiation coefficient, For ambient temperature compensation, For edge attenuation term, The attenuation coefficient is... d The distance from a pixel to the center of the image. d 0 represents the total distance.

8. The method for monitoring the catalyst reaction process according to claim 1, characterized in that, The temperature change rate is calculated as shown in the following equation: 。 9. A monitoring system for a catalytic reaction process, characterized in that, include: processor; A memory on which computer programs that can run on the processor are stored; When the computer program is executed by the processor, it implements the steps of the method for monitoring the catalyst reaction process as described in any one of claims 1 to 8.

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