A method and system for monitoring an industrial brine degassing process
By setting primary and secondary monitoring indicators, combining physical and machine learning models, collecting and visualizing data in real time, marking problem points, and conducting human-computer interactive intervention, the problem of lagging traditional monitoring has been solved, and efficient and safe monitoring of the industrial brine degassing process has been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JOC INT TECHNICAL ENG CO LTD
- Filing Date
- 2023-12-13
- Publication Date
- 2026-06-23
AI Technical Summary
Monitoring of traditional industrial brine degassing processes relies on limited sensors and manual monitoring, which cannot comprehensively and in real time capture changes in key parameters. This leads to delays in problem identification and intervention, affecting the safety and efficiency of the degassing process.
Set primary and secondary monitoring indicators, build physical and machine learning models, combine response commands to collect and visualize data in real time, mark problem data points, and conduct secondary intervention through human-computer interaction interface to achieve automation and instant adjustment.
It improves the comprehensiveness and real-time nature of monitoring the degassing process, enhances the automation of problem response, and ensures the safe and efficient operation of the system.
Smart Images
Figure CN117720157B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of industrial wastewater treatment technology, specifically to a monitoring method and system for the degassing process of industrial brine. Background Technology
[0002] In the industrial sector, particularly in brine treatment processes, degassing is a critical step. It removes gases, especially carbon dioxide, from the brine to prevent corrosion or other problems from occurring in the system. In actual production, this is achieved by introducing nitrogen or by vacuum degassing to ensure the quality of the final product and compliance with industry standards.
[0003] Traditionally, monitoring of industrial brine degassing processes has relied mainly on limited sensor monitoring or manual monitoring, which cannot comprehensively and in real time capture changes in key parameters during the degassing process. In addition, there is a lack of effective automated mechanisms for rapid identification and timely intervention of problems, resulting in delayed problem handling and affecting the safety of the degassing process.
[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of this disclosure and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention
[0005] This invention provides a method and system for monitoring the degassing process of industrial brine, thereby effectively solving the problems pointed out in the background art.
[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0007] A method for monitoring an industrial brine degassing process, the method comprising:
[0008] Based on the requirements of the industrial brine degassing process, primary and secondary monitoring indicators for the degassing process are set.
[0009] Set problem-handling strategies for the primary and secondary monitoring indicators, and pre-set corresponding response passwords;
[0010] Collect real-time data of the parameters corresponding to each monitoring indicator and generate real-time data change images;
[0011] Construct a monitoring model for the degassing process and embed the response password;
[0012] Problem data points are marked in the real-time data change image, and the monitoring model is optimized based on the parameter information of the problem data points;
[0013] The monitoring model is set up with a human-computer interaction interface to monitor the degassing process and perform secondary intervention.
[0014] Furthermore, based on the requirements of the industrial brine degassing process, primary and secondary monitoring indicators for the degassing process are set, including:
[0015] Based on the requirements of the industrial brine dehydration process, the monitoring indicators are determined;
[0016] Based on historical problem information of the degassing process, the risk occurrence coefficient of each monitoring indicator is determined;
[0017] Based on the aforementioned hazard occurrence coefficient, the primary monitoring indicators and secondary monitoring indicators are set.
[0018] Furthermore, real-time data of the corresponding parameters for each monitoring indicator are collected, and real-time data change images are generated, including:
[0019] Collect real-time data of the parameters corresponding to each of the monitoring indicators, and construct a real-time data set for each of the monitoring indicators;
[0020] The data in the real-time dataset is preprocessed;
[0021] Use data visualization tools to transform real-time data into images;
[0022] By integrating the images of each monitoring indicator, a real-time data change image of the degassing process is obtained.
[0023] Furthermore, a monitoring model for the degassing process is constructed, and the response password is embedded, including:
[0024] A physical model and a machine learning model of the degassing process are constructed, and the two models are combined to obtain the monitoring model;
[0025] The response password is embedded in the machine learning model, and the response password is associated with the output of the machine learning model;
[0026] The degassing process is monitored in real time based on the monitoring model, and the relevant parameters of the degassing process are adjusted according to the problem situation.
[0027] Furthermore, a physical model and a machine learning model of the degassing process are constructed, and the two models are combined to obtain the monitoring model, including:
[0028] Based on the physical mechanism of the degassing process, the physical model is established;
[0029] A machine learning model for the degassing process is established based on a regression algorithm.
[0030] The physical model and the machine learning model are jointly trained and optimized;
[0031] The physical model and the machine learning model are combined in a serial manner to obtain the monitoring model.
[0032] Further, problematic data points are marked in the real-time data change image, and the monitoring model is optimized based on the parameter information of the problematic data points, including:
[0033] The monitoring model identifies and marks problematic data points in the real-time data.
[0034] For the marked problematic data points, extract the specific values and feature information of the parameters corresponding to each of the monitoring indicators;
[0035] The monitoring model is optimized based on the parameter information of the problematic data points.
[0036] Furthermore, a human-computer interaction interface for the monitoring model is set up to monitor the degassing process and perform secondary intervention, including:
[0037] Develop a human-computer interaction interface for the monitoring model, which displays real-time monitoring data, model output, and parameter information of problem data points;
[0038] The human-computer interface integrates control elements for secondary intervention, thereby enabling secondary intervention in the degassing process.
[0039] Furthermore, the secondary intervention includes: manually adjusting relevant operating parameters and manually terminating the degassing process.
[0040] A monitoring system for an industrial brine degassing process, the system comprising:
[0041] The monitoring indicator setting module sets primary and secondary monitoring indicators for the degassing process based on the requirements of the industrial brine degassing process.
[0042] The strategy password setting module sets the problem handling strategies for the primary and secondary monitoring indicators and presets corresponding response passwords.
[0043] The real-time data acquisition module collects real-time data of the parameters corresponding to each monitoring indicator and generates real-time data change images.
[0044] The monitoring model construction module constructs a monitoring model for the degassing process and embeds the response password.
[0045] The monitoring model optimization module marks problem data points in the real-time data change image and optimizes the monitoring model based on the parameter information of the problem data points.
[0046] The interactive interface setting module sets the human-computer interaction interface of the monitoring model to monitor the degassing process and perform secondary intervention.
[0047] Furthermore, the real-time data acquisition module includes:
[0048] The data acquisition unit collects real-time data of the parameters corresponding to each of the monitoring indicators and constructs a real-time data set for each of the monitoring indicators.
[0049] The preprocessing unit preprocesses the data in the real-time dataset;
[0050] The visualization unit uses data visualization tools to transform real-time data into images;
[0051] The image integration unit integrates the images of each monitoring indicator to obtain a real-time data change image of the degassing process.
[0052] The technical solution of this invention can achieve the following technical effects:
[0053] It effectively solves the shortcomings of traditional solutions, such as one-sided monitoring parameters and delayed problem response, improves the comprehensiveness and real-time nature of degassing process monitoring, and enhances the degree of automation of intervention.
[0054] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description
[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0056] Figure 1 A schematic diagram of a monitoring method for industrial brine degassing process;
[0057] Figure 2 A flowchart illustrating the process for setting primary and secondary monitoring indicators for the degassing process;
[0058] Figure 3 A flowchart illustrating the process of obtaining real-time data changes during the degassing process;
[0059] Figure 4A flowchart illustrating the process of constructing a monitoring model for the degassing process and embedding a response password;
[0060] Figure 5 A flowchart illustrating the process of combining physical models and machine learning models to obtain a monitoring model;
[0061] Figure 6 This is a flowchart illustrating the process of optimizing the monitoring model based on the parameter information of the problem data points;
[0062] Figure 7 This is a schematic diagram of a monitoring system for an industrial brine degassing process. Detailed Implementation
[0063] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.
[0065] Example 1
[0066] like Figure 1 As shown, the present invention provides a method for monitoring the degassing process of industrial brine, the method comprising:
[0067] S100: Based on the requirements of the industrial brine degassing process, set the primary and secondary monitoring indicators for the degassing process;
[0068] Specifically, primary and secondary monitoring indicators are based on various parameters during the degassing process, such as temperature, pressure, pH value, gas concentration, and reaction rate. These indicators are meticulously divided into primary and secondary levels according to their importance and impact on system control. Primary monitoring indicators typically cover parameters directly related to system safety and stability, such as gas concentration, especially the concentration of harmful gases (such as carbon dioxide), as high concentrations can pose a direct threat to the safety of the system and operators. Secondary monitoring indicators may include parameters that have a smaller impact on the system state and can be adjusted moderately, such as monitoring the system's acidity or alkalinity. These parameters may need to be adjusted within a certain range but will not immediately have a significant impact on the safety of the degassing process. This hierarchical monitoring indicator system helps to conduct more targeted monitoring and treatment to ensure the efficient operation of the degassing process.
[0069] S200: Set the problem handling strategies for primary and secondary monitoring indicators, and preset corresponding response passwords;
[0070] Specifically, the problem handling strategy should correspond to the level of the indicator. For serious problems in the first-level monitoring indicators, a high-priority handling strategy should be formulated, which may include immediately terminating the degassing process and urgently eliminating the source of the fault to ensure system safety. For problems in the second-level monitoring indicators, a relatively conventional handling strategy should be adopted, such as fine-tuning parameters to maintain the stable operation of the system. Each problem handling strategy should correspond to a preset response password so that the corresponding response measures can be triggered quickly and accurately when a problem occurs.
[0071] S300: Collects real-time data of the parameters corresponding to each monitoring indicator and generates real-time data change images;
[0072] S400: Construct a monitoring model for the degassing process and embed a response password;
[0073] S500: Marks problem data points in real-time data change images and optimizes the monitoring model based on the parameter information of each problem data point;
[0074] Specifically, by marking problematic data points, the specific time points and parameters at which abnormalities or problems occur during the degassing process can be quickly located. This helps to quickly identify the root cause of the problem. Based on the parameter information of the problematic data points, the monitoring model can be optimized, enabling the model to more intelligently adapt to the actual changes in the degassing process. This intelligent optimization can improve the accuracy and robustness of the monitoring model, making it better adaptable to different working conditions and environments, helping to make quick decisions and adjustments, and improving the efficiency of problem handling.
[0075] S600: Set up a monitoring model human-machine interface to monitor the degassing process and perform secondary intervention.
[0076] The technical solution of this invention effectively solves the shortcomings of traditional solutions, such as one-sided monitoring parameters and delayed problem response, improves the comprehensiveness and real-time performance of degassing process monitoring, and enhances the degree of automation of intervention.
[0077] As a preferred embodiment of the above, such as Figure 2 As shown, in step S100, according to the requirements of the industrial brine degassing process, primary and secondary monitoring indicators for the degassing process are set, including:
[0078] S110: Determine the monitoring indicators according to the requirements of industrial brine dehydration process;
[0079] S120: Determine the risk factor of each monitoring indicator based on historical problem information of the degassing process;
[0080] S130: Based on the hazard occurrence coefficient, set primary and secondary monitoring indicators.
[0081] Specifically, the hazard occurrence factor is a parameter used to assess the impact of a specific monitoring indicator on system safety and stability. It reflects the severity of the danger or problem that may occur when a monitoring indicator becomes abnormal or exceeds the set range during the degassing process. Generally, the hazard occurrence factor can be determined by considering historical problem information. This factor is usually a weight or score. For example, considering the monitoring indicator of gas concentration, if a high concentration of harmful gas poses a direct threat to the safety of the system and operators, then the hazard occurrence factor of this indicator may be set relatively high. Conversely, for parameters with relatively small impact, the hazard occurrence factor may be low. Therefore, setting the hazard occurrence factor can help to better determine primary and secondary monitoring indicators, so that when monitoring and dealing with problems, the factors with the greatest impact on system safety and stability can be given priority in a more targeted manner.
[0082] As a preferred embodiment of the above, such as Figure 3 As shown, step S300 involves collecting real-time data of the parameters corresponding to each monitoring indicator and generating a real-time data change image, including:
[0083] S310: Collect real-time data of the parameters corresponding to each monitoring indicator and construct a real-time data set for each monitoring indicator;
[0084] S320: Preprocess the data in the real-time dataset;
[0085] S330: Use data visualization tools to transform real-time data into images;
[0086] S340: Integrates the images of various monitoring indicators to obtain real-time data change images of the degassing process.
[0087] Specifically, in this step, sensors and other devices are used to collect data on various monitoring indicators in real time, forming a real-time dataset containing the current values of key indicators during the degassing process. Data preprocessing is then performed, including outlier removal, data smoothing, and normalization, to ensure the quality and reliability of the real-time data. This helps improve the accuracy of subsequent data analysis and visualization. Data visualization tools, such as chart generation software or plotting libraries in programming languages, are used to transform the preprocessed real-time data into intuitive images, such as line charts and scatter plots, to visually display the trends and changes of the monitoring indicators. By integrating the images generated from various monitoring indicators, a comprehensive real-time data change image is constructed to fully display the changing trends of each indicator during the degassing process. This comprehensive image provides a clear understanding of the status of the entire degassing process, facilitating timely detection of anomalies and decision-making.
[0088] As a preferred embodiment of the above, such as Figure 4 As shown, in step S400, a monitoring model for the degassing process is constructed, and a response password is embedded;
[0089] S410: Construct a physical model and a machine learning model for the degassing process, and combine the two models to obtain a monitoring model;
[0090] S420: Embed the response password into the machine learning model and associate the response password with the output of the machine learning model;
[0091] S430: The degassing process is monitored in real time based on a monitoring model, and relevant parameters of the degassing process are adjusted according to the problem situation.
[0092] Specifically, firstly, a physical model based on the physical mechanism of the degassing process is established, considering the relationships between various physical variables. Then, a machine learning model is built to capture the complex correlations within the degassing process by learning from historical data. Finally, the physical and machine learning models are integrated to form a comprehensive monitoring model that can more comprehensively and accurately describe the characteristics of the degassing process. Pre-defined response passwords are embedded in the machine learning model, allowing the model to associate its output with the corresponding response password. This helps establish a connection between the monitoring model and subsequent operations, enabling timely feedback and automatic adjustment of problem situations. The constructed monitoring model is used to monitor the degassing process in real time, acquiring real-time data and performing model inferences. Based on the monitoring results, the model can adjust relevant parameters of the degassing process, enabling immediate intervention and ensuring the system operates safely and efficiently.
[0093] As a preferred embodiment of the above, such as Figure 5 As shown, in step S410, a physical model and a machine learning model of the degassing process are constructed, and the two models are combined to obtain a monitoring model, including:
[0094] S411: Based on the physical mechanism of the degassing process, a physical model is established;
[0095] S421: Establish a machine learning model for the degassing process based on regression algorithm;
[0096] S431: Jointly train and optimize the physical model and the machine learning model;
[0097] S441: Combine the physical model and the machine learning model using a serial combination method to obtain the monitoring model.
[0098] Specifically, in this step, by deeply understanding the physical mechanism of the degassing process and considering relevant principles such as fluid mechanics and thermodynamics, a physical model describing the degassing process is established. Regression algorithms, such as linear regression and support vector regression, are used to build a machine learning model by learning from historical data. The machine learning model based on regression algorithms is chosen because regression algorithms are suitable for predicting continuous output variables, while in the monitoring of the degassing process, more attention is paid to continuous variables such as temperature and pressure. At the same time, the degassing process involves complex relationships between multiple parameters, and regression algorithms can better capture such nonlinear relationships. Especially when considering higher-order terms and interaction terms, regression algorithms are usually computationally efficient and relatively fast in training, making them suitable for monitoring systems with high real-time requirements. The established physical model and machine learning model are jointly trained. By continuously optimizing the model parameters, the two can complement each other and improve the overall performance of the monitoring model. This joint training can effectively combine physical mechanisms and data learning, making the monitoring model closer to the actual degassing process. By adopting a serial combination method, the outputs of the physical model and the machine learning model are gradually integrated to form a comprehensive monitoring model. The serial combination allows for the gradual integration of the outputs of the two models, making the relationship between the two models clearer and helping to better combine the advantages of physical mechanisms and data learning throughout the degassing process.
[0099] As a preferred embodiment of the above, such as Figure 6 As shown, step S500 involves marking problem data points in the real-time data change image and optimizing the monitoring model based on the parameter information of the problem data points, including:
[0100] S510: Identify and mark problematic data points in real-time data through monitoring models;
[0101] S520: For the marked problem data points, extract the specific values and characteristic information of the parameters corresponding to each monitoring indicator;
[0102] S530: Optimize the monitoring model based on the parameter information of the problem data points.
[0103] Specifically, the monitoring model identifies abnormal or deviating data points in real-time data and marks them as problem data points. Pre-set primary and secondary monitoring indicators, as well as problem handling strategies, can be used to identify problem data points. For problem data points, specific parameter values and feature information related to the primary and secondary monitoring indicators are extracted from the real-time data. This may include specific values of parameters such as temperature, pressure, pH value, and gas concentration, as well as their time-series characteristics. Model optimization may include updating the model weights, adjusting the model hyperparameters, or fine-tuning the embedded passwords to better adapt to changes in actual operation.
[0104] As a preferred embodiment of the above, step S600 involves setting up a human-machine interface for the monitoring model to monitor the degassing process and perform secondary intervention, including:
[0105] S610: The human-computer interaction interface for developing monitoring models. The human-computer interaction interface displays real-time monitoring data, model output, and parameter information of problem data points.
[0106] S620: Integrates secondary intervention control elements on the human-machine interface to perform secondary intervention on the degassing process.
[0107] Specifically, the human-computer interface for the monitoring model should be developed. This interface should clearly display real-time monitoring data, the output of the monitoring model, and parameter information for each problematic data point. This includes real-time values of monitoring indicators, trend charts, and predicted results from the model output, so that operators can fully understand the status of the degassing process. Secondary intervention control elements should be integrated into the human-computer interface, allowing operators to directly participate in the monitoring and control process to achieve secondary intervention in the degassing process. This integration enables operators to make timely decisions and adjustments based on the output of the monitoring model and real-time data, providing manual support for the system and ensuring that the degassing process operates safely and stably.
[0108] As a preferred embodiment of the above, the secondary intervention includes: manually adjusting relevant operating parameters and manually terminating the degassing process.
[0109] Example 2
[0110] Based on the same inventive concept as the monitoring method for an industrial brine degassing process described in the foregoing embodiments, this invention also provides a monitoring system for an industrial brine degassing process, such as... Figure 7 As shown, the system includes:
[0111] The monitoring indicator setting module sets primary and secondary monitoring indicators for the degassing process based on the requirements of the industrial brine degassing process.
[0112] The strategy password setting module allows you to set the problem handling strategies for primary and secondary monitoring indicators, and preset corresponding response passwords.
[0113] The real-time data acquisition module collects real-time data of the parameters corresponding to each monitoring indicator and generates real-time data change images.
[0114] The monitoring model building module constructs a monitoring model for the degassing process and embeds a response password.
[0115] The monitoring model optimization module marks problem data points in the real-time data change image and optimizes the monitoring model based on the parameter information of each problem data point.
[0116] The interactive interface settings module allows you to set up the human-machine interface for the monitoring model, enabling you to monitor the degassing process and intervene in a secondary manner.
[0117] The adjustment system described above in this invention can effectively realize the monitoring method of industrial brine degassing process, and the technical effects it can achieve are as described in the above embodiments, which will not be repeated here.
[0118] As a preferred embodiment of the above, the real-time data acquisition module includes:
[0119] The data acquisition unit collects real-time data of the parameters corresponding to each monitoring indicator and constructs a real-time data set for each monitoring indicator.
[0120] The preprocessing unit preprocesses the data in the real-time dataset;
[0121] The visualization unit uses data visualization tools to transform real-time data into images;
[0122] The image integration unit integrates the images of various monitoring indicators to obtain real-time data change images of the degassing process.
[0123] Similarly, the above-mentioned optimization schemes for the system can also achieve the optimization effects corresponding to the methods in Embodiment 1, which will not be repeated here.
[0124] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.
Claims
1. A method of monitoring an industrial brine degassing process, characterized by, The method includes: Based on the requirements of the industrial brine degassing process, primary and secondary monitoring indicators for the degassing process are set, including: Based on the requirements of the industrial brine dehydration process, the monitoring indicators are determined; Based on historical problem information of the degassing process, the risk occurrence coefficient of each monitoring indicator is determined; Based on the aforementioned hazard occurrence coefficient, the primary monitoring indicator and the secondary monitoring indicator are set. Set problem-handling strategies for the primary and secondary monitoring indicators, and pre-set corresponding response passwords; Collect real-time data of the parameters corresponding to each monitoring indicator and generate real-time data change images; Construct a monitoring model for the degassing process and embed the response password, including: A physical model and a machine learning model of the degassing process are constructed, and the two models are combined to obtain the monitoring model, including: Based on the physical mechanism of the degassing process, the physical model is established; A machine learning model for the degassing process is established based on a regression algorithm. The physical model and the machine learning model are jointly trained and optimized; The physical model and the machine learning model are combined in a sequential manner to obtain the monitoring model; The response password is embedded in the machine learning model, and the response password is associated with the output of the machine learning model; The degassing process is monitored based on the monitoring model, and the relevant parameters of the degassing process are adjusted according to the problem situation. Problem data points are marked in the real-time data change image, and the monitoring model is optimized based on the parameter information of the problem data points; The monitoring model is set up with a human-computer interaction interface to monitor the degassing process and perform secondary intervention.
2. The method of monitoring an industrial brine deaeration process according to claim 1, characterized in that, Collect real-time data of the parameters corresponding to each monitoring indicator and generate real-time data change images, including: Collect real-time data of the parameters corresponding to each of the monitoring indicators, and construct a real-time data set for each of the monitoring indicators; The data in the real-time dataset is preprocessed; Use data visualization tools to transform real-time data into images; By integrating the images of each monitoring indicator, a real-time data change image of the degassing process is obtained.
3. The method of monitoring an industrial brine deaeration process of claim 1, wherein Problem data points are marked in the real-time data change image, and the monitoring model is optimized based on the parameter information of the problem data points, including: The monitoring model identifies and marks problematic data points in the real-time data. For the marked problematic data points, extract the specific values and feature information of the parameters corresponding to each of the monitoring indicators; The monitoring model is optimized based on the parameter information of the problematic data points.
4. The method of monitoring an industrial brine deaeration process of claim 1, wherein, The monitoring model is configured with a human-computer interaction interface to monitor the degassing process and perform secondary intervention, including: Develop a human-computer interaction interface for the monitoring model, which displays real-time monitoring data, model output, and parameter information of problem data points; The human-computer interface integrates control elements for secondary intervention, thereby enabling secondary intervention in the degassing process.
5. The method of monitoring an industrial brine deaeration process according to claim 4, characterized in that, The secondary intervention includes: manually adjusting relevant operating parameters and manually terminating the degassing process.
6. A monitoring system of an industrial brine degassing process, applied to the monitoring method according to any one of claims 1 to 5, characterized in that, The system includes: The monitoring indicator setting module sets primary and secondary monitoring indicators for the degassing process based on the requirements of the industrial brine degassing process. The strategy password setting module sets the problem handling strategies for the primary and secondary monitoring indicators and presets corresponding response passwords. The real-time data acquisition module collects real-time data of the parameters corresponding to each monitoring indicator and generates real-time data change images. The monitoring model construction module constructs a monitoring model for the degassing process and embeds the response password. The monitoring model optimization module marks problem data points in the real-time data change image and optimizes the monitoring model based on the parameter information of the problem data points. The interactive interface setting module sets the human-computer interaction interface of the monitoring model to monitor the degassing process and perform secondary intervention.
7. The monitoring system of an industrial brine deaeration process according to claim 6, characterized in that, The real-time data acquisition module includes: The data acquisition unit collects real-time data of the parameters corresponding to each of the monitoring indicators and constructs a real-time data set for each of the monitoring indicators. The preprocessing unit preprocesses the data in the real-time dataset; The visualization unit uses data visualization tools to transform real-time data into images; The image integration unit integrates the images of each monitoring indicator to obtain a real-time data change image of the degassing process.