Rare Earth Extraction Production Process Simulation System and Method

Abstract

The present invention is applicable to the field of rare earth extraction, and provides a rare earth extraction production process simulation system and method. The system includes: a spectral imaging analysis module, a production control decision-making module, an environment simulation module, and an early warning support module. This solution reduces the time and cost required for experiments. Since the process is completely carried out in a virtual simulation environment and no actual materials need to be consumed to obtain the optimal process parameters, it allows for rapid iteration and optimization, accelerates the improvement process, and directly applying the best production details to the actual process can construct complex simulations of various conditions for the production scenario, enabling us to predict the performance of rare earth elements under different operating conditions, thereby improving production efficiency and the theoretical design of products, enhancing the intelligent level and flexibility of the entire rare earth extraction production, and greatly optimizing the production decision-making process of enterprises.

Description

Technical Field

[0001] The present invention belongs to the field of rare earth extraction, and particularly relates to a rare earth extraction production process simulation system and method. Background Art

[0002] Rare earth resources are known as the "industrial vitamin". As an essential supply material for the development of high-tech industries such as intelligent equipment manufacturing and new energy, their importance is self-evident. Facing the requirement of intelligent transformation in the rare earth extraction production process, there is an urgent need for a stable, efficient, intelligent and flexible extraction process simulation method to assist production decision-making, so as to reduce raw material waste in the extraction process and improve product purity. Aiming at the actual production process of rare earth extraction, a digital twin framework applicable to the whole life cycle of this process is studied, and software such as Unity, 3DMaxs and python development software are used to build a digital twin platform for the rare earth extraction process. Through this system, it is possible to view the process technology status, process parameters, equipment operation status, etc. during the rare earth extraction production process.

[0003] Combined with the actual situation of rare earth extraction production, a digital twin framework applicable to the whole life cycle of this process is studied, and software such as Unity, 3DMaxs and python development software are used to initially build a digital twin platform for the rare earth extraction process, realizing real-time mapping and interactive control between the digital twin system and the extraction factory. At the same time, automatic collection and on-line detection of data in the rare earth extraction and separation production process are realized. Secondly, a closed-loop control system for key links of equipment and processes in the rare earth extraction and separation production process is developed. Then, a control system for processes such as material dissolution, extraction and separation, precipitation, and calcination based on DCS is established. Finally, an interlock and optimization control system for each workshop of enterprise production is established. It can quickly explore and analyze the actual situation of the extraction factory production, and through simulation experiments, predict the production situation in advance and perform relevant control optimization to guide relevant production operations and production planning.

[0004] Currently, complex chemical processes traditionally require a large amount of experimental materials and time to test different extraction conditions, and the influence of various operating conditions (such as pressure, temperature and concentration) on the extraction process may be very complex and difficult to predict. When real conditions change, it is crucial to adjust process parameters immediately to maintain production efficiency and product quality. Summary of the Invention

[0005] The purpose of the present invention is to provide a rare earth extraction production process simulation system and method, aiming to solve the technical problems existing in the prior art determined in the background art.

[0006] The present invention is implemented as follows. A rare earth extraction production process simulation system, the system includes:

[0007] A spectral imaging analysis module, which is used to identify the color characteristics of system elements through spectral imaging, establish a rare earth element prediction model at the same time, and analyze the element component content based on the element color characteristics;

[0008] A production control decision-making module, which is used to monitor in real time and dynamically collect production parameters, and generate optimized production parameters according to the obtained element component content;

[0009] An environment simulation module, which is used to build a virtual environment for rare earth element extraction according to the obtained real-time production parameters, substitute the optimized production parameters into the virtual environment, simulate the production process and results, analyze the process quality of the simulation results, establish a parameter library, save both the simulation results and the substituted production parameters in the parameter library, and when the process quality is qualified, synchronize the substituted production parameters to the production line;

[0010] An early warning support module, which is used to identify the current process quality in real time, and when the process quality is abnormal, send a warning message, analyze the abnormal state, and judge the process flow where the abnormality occurs.

[0011] As a further solution of the present invention, the spectral imaging analysis module includes:

[0012] A spectral imaging acquisition unit, which is used to capture the spectral image data of raw materials and products in the production process, formulate and execute a data acquisition and transmission protocol, transmit the captured spectral image data, and preprocess the data at the same time;

[0013] A model construction and training unit, which is used to identify and quantify the spectral characteristics of rare earths, establish a rare earth element prediction model based on this, and analyze the element component content through the color characteristics of each element in the obtained spectral image data.

[0014] As a further solution of the present invention, the production control decision-making module includes:

[0015] A real-time parameter acquisition unit, which is used to collect all production parameters related to production from the production line;

[0016] A data optimization unit, which is used to identify and screen out the relevant parameters that affect production quality and production efficiency, analyze each relevant parameter using an optimization algorithm, judge whether the relevant parameters need to be optimized and adjusted, and obtain several complete parameter groups after optimization and adjustment.

[0017] As a further solution of the present invention, the environment simulation module includes:

[0018] A virtual environment construction unit, which is used to create a virtual model of all production-related physical and chemical parameters required for rare earth element extraction, and simulate the current actual production status by using the real-time collected production parameters and spectral imaging analysis results;

[0019] A dynamic simulation evaluation unit, which is used to substitute several of the optimized and adjusted complete parameter groups into the virtual model respectively, simulate each group of complete parameters through the virtual model respectively, and analyze the product quality generated by the simulation;

[0020] A quality evaluation and analysis unit, which is used to analyze the product quality simulated by each group of complete parameters, set a quality error, obtain a group of complete parameters with the optimal product quality, and substitute it into the production line;

[0021] A parameter library creation unit, which is used to establish a parameter library, store the obtained optimal product quality and the complete parameter group used for this product quality in the parameter library, and bind them to each other when storing.

[0022] As a further solution of the present invention, the warning support module includes:

[0023] A real-time monitoring unit, which is used to continuously detect all operating parameters on the production line and identify the production trend under these parameters;

[0024] A warning trigger unit, which is used to analyze the production trend, and when a production trend deviating from the production target appears, send out a warning message and mark the parameters leading to this production trend.

[0025] Another object of the present invention is to provide a simulation method for rare earth extraction production process, and the method includes:

[0026] Identify the element color characteristics through the spectral imaging recognition system, and at the same time establish a rare earth element prediction model, and analyze the element component content according to the element color characteristics;

[0027] Real-time monitor and dynamically collect production parameters, and at the same time generate optimized and adjusted production parameters according to the obtained element component content;

[0028] Build a virtual environment for rare earth element extraction according to the obtained real-time production parameters, substitute the optimized and adjusted production parameters into the virtual environment, simulate the production process and results, analyze the process quality of the simulation results, and establish a parameter library, store the simulation results and the substituted production parameters in the parameter library, and when the process quality is qualified, synchronize the substituted production parameters to the production line;

[0029] Real-time identify the current process quality, and when the process quality is abnormal, send out a warning message, analyze the abnormal state, and judge the process flow where the abnormality occurs.

[0030] As a further aspect of the present invention, for establishing the rare earth element prediction model and analyzing the element component content based on the element color characteristics, it specifically includes:

[0031] Capturing the spectral image data of raw materials and products during the production process, formulating and implementing a data acquisition and transmission protocol to transmit the captured spectral image data, and simultaneously preprocessing the data;

[0032] Identifying and quantifying the spectral characteristics of rare earths, establishing a rare earth element prediction model based on this, and analyzing the element component content through the color characteristics of each element in the obtained spectral image data.

[0033] As a further aspect of the present invention, for real-time monitoring and dynamically collecting production parameters, and simultaneously generating optimized production parameters according to the obtained element component content, it specifically includes:

[0034] Collecting all production parameters related to production from the production line;

[0035] Identifying and screening out the relevant parameters that affect production quality and production efficiency, and using an optimization algorithm to analyze each relevant parameter to determine whether the relevant parameter needs to be optimized and adjusted, and obtaining several complete parameter groups after optimization and adjustment.

[0036] As a further aspect of the present invention, for building a virtual environment for rare earth element extraction and substituting the optimized production parameters into the virtual environment to simulate the production process and results, it specifically includes:

[0037] Creating a virtual model of all physical and chemical parameters related to production required for rare earth element extraction, and simulating the current actual production state by using the real-time collected production parameters and the results of spectral imaging analysis;

[0038] Respectively substituting several of the complete parameter groups after optimization and adjustment into the virtual model, and respectively simulating each group of complete parameters through the virtual model, and analyzing the product quality generated by the simulation;

[0039] Analyzing the product quality simulated by each group of complete parameters, setting a quality error, obtaining a group of complete parameters with the optimal product quality, and substituting it into the production line;

[0040] Establishing a parameter library, storing the obtained optimal product quality and the complete parameter group used for this product quality in the parameter library, and binding the two to each other during storage.

[0041] As a further aspect of the present invention, for real-time identifying the current process quality, and when the process quality is abnormal, sending out a warning message, it specifically includes:

[0042] Continuously detect all operating parameters on the production line and identify the production trend under these parameters;

[0043] Analyze the production trend. When a production trend deviating from the production target appears, send a warning message and mark the parameters that lead to this production trend.

[0044] The beneficial effects of the present invention are:

[0045] This solution reduces the time and cost required for experiments. Since the process is completely carried out in a virtual simulation environment and no actual materials need to be consumed to obtain the optimal process parameters, it allows for rapid iteration and optimization, accelerates the improvement process, and directly applies the best production details to the actual process.

[0046] It is possible to construct complex simulations of various conditions for the production scenario, enabling us to predict the performance of rare earth elements under different operating conditions, thereby improving production efficiency and the theoretical design of products, enhancing the intelligent level and flexibility of the entire rare earth extraction production, and greatly optimizing the production decision-making process of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a structural block diagram of a rare earth extraction production process simulation system provided by an embodiment of the present invention;

[0048] Figure 2 It is a structural block diagram of a spectral imaging analysis module provided by an embodiment of the present invention;

[0049] Figure 3 It is a structural block diagram of a production control decision module provided by an embodiment of the present invention;

[0050] Figure 4 It is a structural block diagram of an environment simulation module provided by an embodiment of the present invention;

[0051] Figure 5 It is a structural block diagram of a warning support module provided by an embodiment of the present invention;

[0052] Figure 6 It is a flowchart of a rare earth extraction production process simulation method provided by an embodiment of the present invention;

[0053] Figure 7 It is a flowchart of establishing a rare earth element prediction model and analyzing the elemental component content based on the element color characteristics provided by an embodiment of the present invention;

[0054] Figure 8 It is a flowchart of real-time monitoring and dynamically collecting production parameters, and at the same time generating optimized production parameters according to the obtained elemental component content provided by an embodiment of the present invention;

[0055] Figure 9 A flowchart for establishing a virtual environment for rare earth element extraction provided by an embodiment of the present invention, substituting the optimized production parameters into the virtual environment, and simulating the production process and results;

[0056] Figure 10 A flowchart for the real-time identification of the current process quality, and when the process quality is abnormal, a warning message is issued. Detailed implementation manners

[0057] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present 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 only used to explain the present invention, and are not used to limit the present invention.

[0058] It can be understood that the terms "first", "second", etc. used in this application can be used in this document to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish the first element from another element. For example, without departing from the scope of this application, the first xx script can be called the second xx script, and similarly, the second xx script can be called the first xx script.

[0059] Figure 1 A structural block diagram of a rare earth extraction production process simulation system provided by an embodiment of the present invention, as Figure 1 shown, the rare earth extraction production process simulation system, the system includes:

[0060] A spectral imaging analysis module 100, configured to identify the element color characteristics of the system through spectral imaging, and at the same time establish a rare earth element prediction model, and analyze the element component content according to the element color characteristics;

[0061] This module is responsible for capturing the spectral data of raw materials and products during the production process through advanced spectral imaging technology, and providing accurate identification of the element color characteristics of the system. With its high-resolution imaging ability and real-time data capture characteristics, this module significantly improves the accuracy and efficiency of the analysis of rare earth element component content during the production process. Through a customized data acquisition and transmission protocol, it can process the captured spectral image data in a timely and efficient manner, ensure data integrity and provide stable input for the analysis model.

[0062] With the collection and input of a large amount of data, this module not only gradually enhances its ability to identify and quantify the spectral characteristics of rare earths, but also self-evolves by adopting advanced statistical and deep learning methods, thus continuously improving the viewing accuracy and response speed. This self-optimization of capabilities ensures that the model predictions become more and more accurate over time and with data accumulation, thereby greatly increasing the dependence on real-time monitoring and regulation of rare earth element content.

[0063] The spectral imaging analysis module 100 not only effectively reduces the analysis and prediction deviation throughout the rare earth extraction production process, but also strengthens the processing automation, ultimately promoting the intelligentization and sustainable development of the entire production process. This progress plays an indispensable role in improving the quality and efficiency of production management in the rare earth extraction industry.

[0064] The production control decision-making module 200 is used to monitor in real time and dynamically collect production parameters, and at the same time generate optimized production parameters according to the obtained element component content;

[0065] This module has the complex tasks of real-time parameter collection from the production line and performing data optimization. Its main function is to capture comprehensive production parameters from the production line in real time. These parameters cover the entire production process from start to end, including but not limited to the temperature, pressure, flow rate, density of materials, and more different physical and chemical indicators. These data are crucial for subsequent analysis and decision-making.

[0066] Furthermore, what is important is not only collecting data, but more critically, using advanced optimization algorithms to identify which parameters are the key factors affecting production quality and efficiency. This can not only identify existing problems, but also predict potential risks. This module is like the brain of a production process. Compared with traditional methods, it can detect abnormalities in the production process faster and take preventive measures to avoid potential quality problems.

[0067] For the parameters determined to affect production performance, this unit will apply mathematical and statistical models to analyze them for optimized adjustment, scraping the analysis data to predict the possible impacts of various settings on production. Therefore, after these parameters are carefully analyzed, they will be transformed into a series of optimized parameter combinations to achieve the purpose of improving production efficiency and quality. These optimized parameter combinations play a very important role in the process of extracting rare earths.

[0068] The significant progress of this module lies in its high degree of automation and decision support capabilities. In the relatively traditional manual monitoring and parameter adjustment process, the response time is slow, and it is easy to cause inefficiency due to human errors. The implementation of the production control decision-making module has greatly improved the intelligent level of production line management, implemented data-driven decision-making, improved the accuracy of production plans, reduced consumables and waste, and reduced the impact on the environment.

[0069] This module can adjust parameters in a timely manner to respond to the dynamic changes in the production process, and can appropriately predict and adapt to the impacts of the complex interactions that may occur in biological and chemical reaction processes. In this way, resource utilization is greatly optimized, the optimal state of the production line is dynamically ensured, and the sustainability and high efficiency of rare earth extraction operations are maintained. This is a transformative technological advancement in traditional rare earth extraction processes.

[0070] The environmental simulation module 300 is used to build a virtual environment for rare earth element extraction according to the acquired real-time production parameters, substitute the optimized production parameters into the virtual environment, simulate the production process and results, analyze the process quality of the simulation results, establish a parameter library, store both the simulation results and the substituted production parameters in the parameter library, and when the process quality is qualified, synchronize the substituted production parameters to the production line;

[0071] This module bridges theory and actual operation in parallel, and collaboratively works to virtually map the production process and perform quality optimization control.

[0072] First, a virtual model of the physical and chemical parameters of the detailed production process is created. This model can receive the element color feature data from the spectral imaging analysis module and the dynamic production parameters collected by the real-time parameter acquisition unit. It ensures the accuracy of reflecting the current production process and achieves a high degree of synchronization with the actual production state.

[0073] Immediately afterwards, the virtual environment is used to conduct application tests on multiple optimized parameter combinations, which is equivalent to conducting multiple production trial-and-errors in a risk-free environment. The module evaluates the impacts of each parameter adjustment on product quality, thereby providing a basis for setting the best process parameters for actual production.

[0074] Meanwhile, by carefully analyzing the quality of the simulated rare earth element products, an error threshold is set, and then the parameter combination that optimizes production quality is traced back and determined. After obtaining the optimal parameters, instructions are sent to the production line to adjust the real process, in order to achieve the results predicted by the simulation.

[0075] Finally, not only the determined best production information is saved in the database, but also a parameter library with learning ability is established. By synchronously storing product quality data and the corresponding parameter combinations, this unit provides rich historical data for future optimization decisions and process error analysis.

[0076] The environmental simulation module reduces the time and cost required for experiments because the process is completely carried out in a virtual simulation environment, and the best process parameters can be obtained without consuming actual materials. The module allows designers to perform rapid iterations and optimizations, accelerating the improvement process and directly applying the best production details to the actual process. Moreover, it can construct complex simulations of various conditions for the production scenario, enabling us to predict the performance of rare earth elements under different operating conditions, thereby improving production efficiency and the theoretical design of products. This module essentially enhances the intelligent level and flexibility of the entire rare earth extraction production, greatly optimizing the production decision-making process of enterprises and demonstrating the significant impact of technology in promoting the upgrading of traditional industries.

[0077] The early warning support module 400 is used to identify the current process quality in real time. When the process quality is abnormal, it issues a warning message and analyzes the abnormal state to determine the process flow where the abnormality occurs.

[0078] Figure 2 The structural block diagram of the spectral imaging analysis module provided by the embodiment of the present invention is as Figure 2 shown, and the spectral imaging analysis module includes:

[0079] The spectral imaging acquisition unit 110 is used to capture the spectral image data of raw materials and products during the production process, formulate and execute a data acquisition and transmission protocol, transmit the captured spectral image data, and preprocess the data at the same time.

[0080] The model construction and training unit 120 is used to identify and quantify the spectral characteristics of rare earths, establish a rare earth element prediction model based on this, and analyze the elemental component content through the color characteristics of each element in the obtained spectral image data.

[0081] Figure 3 The structural block diagram of the production control decision module provided by the embodiment of the present invention is as Figure 3 shown, and the production control decision module includes:

[0082] The real-time parameter acquisition unit 210 is used to collect all production-related parameters from the production line.

[0083] The data optimization unit 220 is used to identify and screen out the relevant parameters that affect production quality and production efficiency, analyze each relevant parameter using an optimization algorithm to determine whether the relevant parameter needs to be optimized and adjusted, and obtain several complete parameter groups after optimization and adjustment.

[0084] Figure 4 The structural block diagram of the environmental simulation module provided by the embodiment of the present invention is as Figure 4 shown, and the environmental simulation module includes:

[0085] The virtual environment construction unit 310 is used to create a virtual model of all production-related physical and chemical parameters required for rare earth element extraction, and simulate the current actual production status by using the real-time collected production parameters and spectral imaging analysis results;

[0086] The dynamic simulation evaluation unit 320 is used to substitute several of the optimized and adjusted complete parameter sets into the virtual model respectively, simulate each set of complete parameters through the virtual model, and analyze the product quality generated by the simulation;

[0087] The quality evaluation and analysis unit 330 is used to analyze the product quality simulated by each set of complete parameters, set a quality error, obtain a set of complete parameters with the optimal product quality, and substitute it into the production line;

[0088] The parameter library creation unit 340 is used to establish a parameter library, store the obtained optimal product quality and the complete parameter set used for this product quality in the parameter library, and bind them to each other when storing.

[0089] Figure 5 It is a structural block diagram of the early warning support module provided by an embodiment of the present invention, as Figure 5 shown, the early warning support module includes:

[0090] The real-time monitoring unit 410 is used to continuously detect all operation parameters on the production line and identify the production trend under these parameters;

[0091] The early warning trigger unit 420 is used to analyze the production trend, and when there is a production trend deviating from the production target, send out a warning message and mark the parameters causing this production trend.

[0092] Figure 6 It is a flowchart of the rare earth extraction production process simulation method provided by an embodiment of the present invention, as Figure 6 shown, the rare earth extraction production process simulation method, the method includes:

[0093] S100, identify the element color characteristics through the spectral imaging recognition system, and at the same time establish a rare earth element prediction model, and analyze the element component content according to the element color characteristics;

[0094] This step is responsible for capturing the spectral data of raw materials and products during the production process through advanced spectral imaging technology, and providing accurate identification of the element color characteristics of the system. With its high-resolution imaging ability and real-time data capture characteristics, this step significantly improves the accuracy and efficiency of the analysis of rare earth element component content during the production process. Through a customized data acquisition and transmission protocol, the captured spectral image data can be processed in a timely and efficient manner, ensuring data integrity and providing stable input for the analysis model.

[0095] With the collection and input of a large amount of data, this step not only gradually enhances its ability to identify and quantify the spectral characteristics of rare earths, but also self-evolves by adopting advanced statistical and deep learning methods, thus continuously improving the viewing accuracy and response speed. This self-optimization of the ability ensures that the model prediction becomes more and more accurate over time and with data accumulation, thus greatly increasing the dependence on real-time monitoring and regulation of the rare earth element content.

[0096] This step not only effectively reduces the analysis and prediction deviation in the entire rare earth extraction production process, but also strengthens the process automation, ultimately promoting the intelligence and sustainable development of the entire production process. This progress plays an indispensable role in improving the quality and efficiency of production management in the rare earth extraction industry.

[0097] S200, real-time monitor and dynamically collect production parameters, and at the same time generate optimized production parameters according to the obtained element component content;

[0098] This step has the complex tasks of real-time parameter collection from the production line and data optimization execution. Its main function is to capture comprehensive production parameters from the production line in real time, and these parameters cover the entire production process from start to end, including but not limited to the temperature, pressure, flow rate, density of materials, and more different physical and chemical indicators. These data are crucial for subsequent analysis and decision-making.

[0099] Furthermore, what matters is not only collecting data, but more crucially, using advanced optimization algorithms to identify which parameters are the key factors affecting production quality and efficiency. This can not only identify existing problems, but also predict potential risks. This step is like the brain of a production process. Compared with traditional methods, it can discover abnormalities in the production process faster and take preventive measures to avoid potential quality problems.

[0100] For the parameters determined to affect production performance, this unit will apply mathematical and statistical models to analyze them for optimized adjustment, and scrape the analysis data to predict the possible impacts of various settings on production. Therefore, after these parameters are carefully analyzed, they will be transformed into a series of optimized parameter combinations to achieve the purpose of improving production efficiency and quality. These optimized parameter combinations play a very important role in the process of extracting rare earths.

[0101] The significant progress of this step lies in its high degree of automation and decision support capabilities. In the relatively traditional manual monitoring and parameter adjustment process, the response time is slow, and it is easy to cause inefficiency due to human errors. The implementation of this step has greatly improved the intelligence level of production line management, implemented data-driven decision-making, improved the accuracy of production plans, reduced consumables and waste, and reduced the impact on the environment.

[0102] This step can timely adjust parameters to respond to the dynamic changes in the production process, and can appropriately predict and adapt to the impacts of the complex interactions that may occur in biological and chemical reaction processes. In this way, the resource utilization is greatly optimized, the optimal state of the production line is dynamically ensured, and the sustainability and high efficiency of the rare earth extraction operation are maintained. This is a transformative technological advancement in the traditional rare earth extraction process.

[0103] S300. According to the obtained real-time production parameters, build a virtual environment for rare earth element extraction, substitute the optimized production parameters into the virtual environment, simulate the production process and results, analyze the process quality of the simulation results, and establish a parameter library. Save both the simulation results and the substituted production parameters in the parameter library. And when the process quality is qualified, synchronize the substituted production parameters to the production line.

[0104] This step bridges theory and actual operation in parallel, and collaboratively conducts virtual mapping of the production process and quality optimization control.

[0105] First, create a virtual model of the physical and chemical parameters of the detailed production process. This model can receive element color feature data and dynamic production parameters. It ensures to reflect the accuracy of the current production process and achieve a high degree of synchronization with the actual production state.

[0106] Immediately afterwards, use the virtual environment to conduct application tests on multiple optimized parameter combinations, which is equivalent to conducting multiple production trials and errors in a risk-free environment. This step evaluates the impacts of each parameter adjustment on product quality, thereby providing a basis for setting the best process parameters for actual production.

[0107] Meanwhile, through a detailed analysis of the quality of the simulated rare earth element products, set an error threshold, and thus trace back and determine the parameter combination for optimizing production quality. After obtaining the optimal parameters, send instructions to the production line to adjust the actual process in order to achieve the results predicted by the simulation.

[0108] Finally, not only save the determined best production information in the database, but also establish a parameter library with learning ability. By synchronously storing product quality data and the corresponding parameter combinations, this unit provides rich historical data for future optimization decisions and process error analysis.

[0109] This step reduces the time and cost required for experiments because the process is carried out entirely in a virtual simulation environment, and the best process parameters can be obtained without consuming actual materials. The step allows designers to perform rapid iterations and optimizations, accelerating the improvement process and directly applying the best production details to the actual process. Moreover, it can construct complex simulations of various conditions for the production scenario, enabling us to predict the performance of rare earth elements under different operating conditions, thereby improving production efficiency and the theoretical design of products. This step substantially enhances the intelligence level and flexibility of the entire rare earth extraction production, greatly optimizing the production decision-making process of the enterprise and demonstrating the significant impact of technology in promoting the upgrading of traditional industries.

[0110] S400, real-time identify the current process quality. And when the process quality is abnormal, send a warning message and analyze the abnormal state to determine the process flow where the abnormality occurs.

[0111] Figure 7 For the flowchart of establishing a rare earth element prediction model provided by the embodiment of the present invention to analyze the element component content according to the element color characteristics, as Figure 7 shown, the establishment of the rare earth element prediction model to analyze the element component content according to the element color characteristics specifically includes:

[0112] S110, capture the spectral image data of raw materials and products during the production process, formulate and execute a data acquisition and transmission protocol, transmit the captured spectral image data, and preprocess the data at the same time;

[0113] S120, identify and quantify the spectral characteristics of rare earths, establish a rare earth element prediction model based on this, and analyze the element component content through the color characteristics of each element in the obtained spectral image data;

[0114] Specifically, to identify and quantify the spectral characteristics of rare earths, establish a rare earth element prediction model based on this, and analyze the element component content through the color characteristics of each element in the obtained spectral image data, the specific steps are as follows:

[0115] Based on the spectral image data of raw materials and products, calculate and construct a rare earth element electronic transition database based on the density functional mechanism;

[0116] Based on the rare earth element electronic transition database, adaptively extract the characteristic wavelength range through the attention mechanism, and optimize the characteristic wavelength selection using the quantum annealing algorithm to obtain the range of characteristic wavelengths and spectral intensity values of the element;

[0117] Based on the range of the characteristic wavelengths and the spectral intensity values of the elements, filter technology is used to obtain the spectral intensity values after noise removal, and the spectral intensity values after noise removal are normalized to obtain the processed spectral intensity values. During the denoising process, a spectral domain attention discrimination mechanism is adopted to verify the feature preservation of the spectral intensity values after noise removal;

[0118] Based on the processed spectral intensity values, a spectral response function of the element is constructed by means of polynomial fitting;

[0119] Within the range of the characteristic wavelengths of the element, the product of the spectral intensity value and the spectral response function is calculated wavelength by wavelength to obtain the weighted spectral intensity value;

[0120] The Simpson integration method is used to integrate the weighted spectral intensity values to obtain the integration result of the spectral intensity values;

[0121] The integration result of the spectral intensity values is converted through a calibration curve to obtain the element component content.

[0122] Figure 8 For the flowchart of the real-time monitoring and dynamic acquisition of production parameters provided by the embodiments of the present invention, and at the same time generating optimized production parameters according to the obtained element component content, as Figure 8 shown, the real-time monitoring and dynamic acquisition of production parameters, and at the same time generating optimized production parameters according to the obtained element component content specifically include:

[0123] S210, collect all production parameters related to production from the production line;

[0124] S220, identify and screen out the relevant parameters that affect production quality and production efficiency, and use an optimization algorithm to analyze each relevant parameter to determine whether the relevant parameter needs to be optimized and adjusted, and obtain several complete parameter groups after optimization and adjustment;

[0125] Specifically, identifying and screening out the relevant parameters that affect production quality and production efficiency, and using an optimization algorithm to analyze each relevant parameter to determine whether the relevant parameter needs to be optimized and adjusted, and obtaining several complete parameter groups after optimization and adjustment, the specific steps are as follows:

[0126] By identifying and screening out the relevant parameters that affect production quality and production efficiency, obtain the production quality score value, quality index, production efficiency, production cost, quality weight, and production efficiency weight;

[0127] Extract all the production quality scores within the current production cycle from the production quality score value and sort them according to the production batch number;

[0128] Classify and label the quality indicators for each batch, generate a multi-dimensional quality indicator matrix, and obtain a structured quality data set;

[0129] Based on the quality indicators, assign dynamic weights to each indicator and obtain a dynamic weight vector for the indicators;

[0130] Based on the structured quality data set and the dynamic weight vector of the indicators, obtain a batch weighted quality sequence;

[0131] Calculate the quality deviation value through the total number of batches and the batch weighted quality sequence;

[0132] Square the quality deviation value to obtain a quality deviation square sequence;

[0133] Based on the quality deviation square sequence, use the unbiased estimation method to obtain the quality variance;

[0134] Multiply the quality variance by the quality weight to obtain the adjusted quality variance, multiply the production efficiency by the production efficiency weight to obtain the adjusted production efficiency, and multiply the production cost by the cost weight to obtain the adjusted production cost;

[0135] Construct a comprehensive optimization function using the adjusted quality variance, adjusted production efficiency, and adjusted production cost;

[0136] Optimize the parameters of the comprehensive optimization function through iterative training and obtain the optimized comprehensive optimization function;

[0137] Use the optimized comprehensive optimization function as a basis to judge and screen the relevant parameters to obtain the screened relevant parameters;

[0138] Optimize the screened relevant parameters through the optimized comprehensive optimization function to obtain the optimized and adjusted parameters;

[0139] Integrate the optimized and adjusted parameters to obtain the optimized and adjusted complete parameter set.

[0140] Figure 9 For the flowchart provided by the embodiment of the present invention to build a virtual environment for rare earth element extraction, substitute the optimized and adjusted production parameters into the virtual environment, and simulate the production process and results, as Figure 9 shown, the building of the virtual environment for rare earth element extraction, substituting the optimized and adjusted production parameters into the virtual environment, and simulating the production process and results specifically include:

[0141] S310, create a virtual model of all production-related physical and chemical parameters required for rare earth element extraction, and use the real-time collected production parameters and spectral imaging analysis results to simulate the current actual production state;

[0142] S320. Substitute several of the optimized and adjusted complete parameter sets into the virtual model respectively, simulate each set of complete parameters through the virtual model respectively, and analyze the product quality generated by the simulation.

[0143] S330. Analyze the product quality simulated by each set of complete parameters, set a quality error, obtain a set of complete parameters with the optimal product quality, and substitute it into the production line.

[0144] S340. Establish a parameter library, store the obtained optimal product quality and the complete parameter set used for this product quality in the parameter library, and bind the two to each other when storing.

[0145] Specifically, create a virtual model of all the physical and chemical parameters related to the production required for rare earth element extraction, and use the real-time collected production parameters and spectral imaging analysis results to simulate the current actual production state. The specific steps are as follows:

[0146] Determine the total production time.

[0147] Based on the physical and chemical parameters related to the production required for rare earth element extraction, determine the key production states and key production times during the production process.

[0148] Based on the physical and chemical parameters related to the production required for rare earth element extraction, determine the activation energy difference value and the real-time temperature value.

[0149] Combine the activation energy difference value and the real-time temperature value, and amplify the temperature sensitivity through an exponential function to obtain a preliminary energy level weight factor, and then generate an activation energy weight sequence.

[0150] Based on the physical and chemical parameters related to the production required for rare earth element extraction, determine the real-time spectral feature vector, the target spectral feature vector, and the spectral adjustment coefficient.

[0151] Determine the spectral offset based on the real-time spectral feature vector and the target spectral feature vector.

[0152] Input the spectral offset into the hyperbolic tangent function for non-linear compression to obtain the compressed offset.

[0153] Use the spectral adjustment coefficient to perform scale adaptation on the compressed offset to obtain a normalized spectral offset influence factor.

[0154] Extract the activation energy weight of each reaction stage from the activation energy weight sequence, and multiply the activation energy weight of each reaction stage by the normalized spectral offset influence factor to obtain a preliminary compliance contribution value.

[0155] Sum up all the preliminary contribution degree values with weights to generate a set of contribution degree coefficients;

[0156] Construct a bell-shaped distribution function centered on the critical production time;

[0157] Substitute the real-time timestamp into the bell-shaped distribution function to obtain the time diffusion parameter;

[0158] Perform coupled verification on the event diffusion parameter based on the set of contribution degree coefficients, and output the event coupling parameter group after the verification;

[0159] Calculate and obtain the production status based on the contribution degree coefficient of the critical production status, the critical production time, and the time diffusion parameter;

[0160] Construct a virtual model in the form of accumulation for all production statuses within the total production time range;

[0161] Input the current time into the virtual model to obtain the current actual production status.

[0162] Figure 10 The flowchart for the real-time identification of the current process quality in the embodiments of the present invention, and when the process quality is abnormal, a warning message is issued, as Figure 10 shown, the real-time identification of the current process quality, and when the process quality is abnormal, a warning message is issued, specifically including:

[0163] S410, continuously detect all operation parameters on the production line, and identify the production trend under these parameters;

[0164] S420, analyze the production trend, and when there is a production trend deviating from the production target, issue a warning message, and mark the parameters that cause this production trend;

[0165] Analyze the production trend, and when there is a production trend deviating from the production target, issue a warning message, and mark the parameters that cause this production trend. The specific steps are as follows:

[0166] Through the analysis of the production trend, obtain the current value fluctuation amount and the dynamic reference benchmark value of the operation parameters;

[0167] Divide the current value fluctuation amount of each operation parameter by the dynamic reference benchmark value to obtain the normalized fluctuation amount;

[0168] Use the S-shaped curve function to smooth the normalized fluctuation amount to obtain the smoothed fluctuation amount;

[0169] Perform normalization processing on the smoothed fluctuation amount to generate a preliminary weight distribution of the fluctuation amount;

[0170] By analyzing the production trend, the real-time characteristics of the parameters are obtained, and the ideal state characteristics and the deviation compression adjustment factor are set;

[0171] Calculate the Euclidean distance between the real-time characteristics of the parameters and the ideal state characteristics in the multi-dimensional space to obtain the original deviation;

[0172] Substitute the original deviation into the hyperbolic tangent function, and at the same time use the deviation compression adjustment factor to control the sensitivity of the deviation estimation to generate the parameter state deviation coefficient;

[0173] Construct a parameter weight function using the preliminary weight distribution of the fluctuation amount and the parameter state deviation coefficient;

[0174] Derive the operating parameters based on the parameter weight function, and calculate and obtain the second derivative of the operating parameters;

[0175] For each pair of operating parameters, multiply the second derivatives of the two operating parameters to obtain a preliminary synergy value, and perform amplitude compression on the filtered synergy value to obtain the synergy strength index;

[0176] Perform amplitude compression on the filtered synergy value to obtain the synergy strength index;

[0177] By analyzing the production trend, further obtain the position coordinates of the parameter control unit and the spatial influence coefficient;

[0178] Based on the position coordinates of the parameter control unit, calculate the geometric distance between each pair of parameter control units;

[0179] Import the geometric distance between each pair of parameter control units into the exponential decay function and optimize it through the spatial influence coefficient to obtain the spatial influence decay coefficient;

[0180] Construct an interaction function based on the synergy strength index and the spatial decay coefficient, and calculate and obtain the interaction value between the parameters based on the operating parameters through the interaction function;

[0181] Combine the second derivative of the parameter and the interaction value between the parameters to obtain the deviation from the production target value, and accumulate all the deviation from the production target values to obtain the deviation threshold;

[0182] Based on the deviation threshold, analyze the production trend, and when the production trend deviates from the production target, send a warning message and mark the parameters that cause the production trend operation.

[0183] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown in sequence according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0184] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0185] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0186] The above-described embodiments merely represent several implementation manners of the present invention. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

[0187] The above is only the preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A rare earth extraction production process simulation method, characterized in that: The method comprises the following steps: Identify the color characteristics of system elements through spectral imaging, establish a rare earth element prediction model, and derive the element component content based on the color characteristics of the elements; Real-time monitoring and dynamic collection of production parameters, and generation of optimized and adjusted production parameters based on the obtained element component content; According to the real-time production parameters obtained, a virtual environment for rare earth element extraction is built, and the optimized and adjusted production parameters are substituted into the virtual environment, the production process and results are simulated, and the process quality of the simulation results is analyzed, and a parameter library is established. The simulation results and the substituted production parameters are retained in the parameter library, and when the process quality is qualified, the substituted production parameters are synchronized to the production line; Identify the current process quality in real time, and when the process quality is abnormal, issue a warning message, analyze the abnormal state, and determine the abnormal process flow; The method of establishing a rare earth element prediction model and analyzing the element component content based on the element color characteristics specifically includes: Capture the spectral image data of raw materials and products in the production process, formulate and implement data acquisition and transmission protocols, transmit the captured spectral image data, and pre-process the data; Identify and quantify the spectral characteristics of rare earths, and establish a rare earth element prediction model based on this. Through the color characteristics of each element in the obtained spectral image data, analyze the element component content; Among them, the spectral characteristics of rare earths are identified and quantified, and a rare earth element prediction model is established based on this. The element component content is analyzed through the color characteristics of each element in the obtained spectral image data. The specific steps are as follows: Based on the spectral image data of raw materials and products, the electronic transition database of rare earth elements is calculated and constructed based on the density functional mechanism; Based on the rare earth element electronic transition database, the characteristic wavelength interval is adaptively extracted through the attention mechanism, and the characteristic wavelength selection is optimized using the quantum annealing algorithm to obtain the characteristic wavelength range and spectral intensity value of the element; Based on the characteristic wavelength range and spectral intensity value of the element, the filtering technology is used to obtain the spectral intensity value after noise removal, and the spectral intensity value after noise removal is normalized to obtain the processed spectral intensity value. In the denoising process, the spectral domain attention discrimination mechanism is used to verify the feature preservation of the spectral intensity value after noise removal. Based on the processed spectral intensity values, the spectral response function of the element is constructed by polynomial fitting method; Within the characteristic wavelength range of the element, the product of the spectral intensity value and the spectral response function is calculated wavelength by wavelength to obtain a weighted spectral intensity value; The weighted spectral intensity value is integrated using the Simpson integration method to obtain the integration result of the spectral intensity value; The integral results of the spectral intensity values ​​were converted through the calibration curve to obtain the elemental component content.

2. The rare earth extraction production process simulation method according to claim 1, characterized in that: The real-time monitoring and dynamic collection of production parameters, and the generation of optimized and adjusted production parameters based on the obtained element component contents, specifically include: Collect all production parameters related to production from the production line; Identify and screen out the relevant parameters that affect production quality and production efficiency, and use the optimization algorithm to analyze each relevant parameter to determine whether the relevant parameters need to be optimized and adjusted, and obtain several complete parameter groups after optimization and adjustment.

3. The rare earth extraction production process simulation method according to claim 2, characterized in that: Identify and screen out the relevant parameters that affect production quality and production efficiency, and use the optimization algorithm to analyze each relevant parameter to determine whether the relevant parameters need to be optimized and adjusted, and obtain several complete parameter groups after optimization and adjustment. The specific steps are as follows: By identifying and screening out the relevant parameters that affect production quality and production efficiency, we can obtain production quality scores, quality indicators, production efficiency, production costs, quality weights, and production efficiency weights; Extract all production quality scores in the current production cycle from the production quality score values ​​and sort them according to the production batch numbers; Classify and label the quality indicators of each batch, generate a multidimensional quality indicator matrix and obtain a structured quality data set; Based on the quality index, a dynamic weight is assigned to each indicator, and an indicator dynamic weight vector is obtained; Based on the structured quality data set and the indicator dynamic weight vector, a batch weighted quality sequence is obtained; The quality deviation value is calculated by the total number of batches and the batch weighted quality sequence; The mass deviation value is squared to obtain a mass deviation square sequence; Based on the mass deviation square sequence, the mass variance is obtained by using an unbiased estimation method; The quality variance is multiplied by the quality weight to obtain the adjusted quality variance, the production efficiency is multiplied by the production efficiency weight to obtain the adjusted production efficiency, and the production cost is multiplied by the cost weight to obtain the adjusted production cost; A comprehensive optimization function is constructed using the adjusted quality variance, the adjusted production efficiency and the adjusted production cost; By means of iterative training, the parameters of the comprehensive optimization function are optimized, and the optimized comprehensive optimization function is obtained; Using the optimized comprehensive optimization function as a basis, the relevant parameters are judged and screened to obtain the screened relevant parameters; The selected related parameters are optimized through the optimized comprehensive optimization function to obtain the optimized and adjusted parameters; The optimized and adjusted parameters are integrated to obtain a complete optimized and adjusted parameter set.

4. The rare earth extraction production process simulation method according to claim 3, characterized in that: The virtual environment for rare earth element extraction is constructed, and the optimized and adjusted production parameters are substituted into the virtual environment to simulate the production process and results, specifically including: Create a virtual model of all production-related physical and chemical parameters required for rare earth element extraction, and use real-time collected production parameters and spectral imaging analysis results to simulate the current actual production status; Substituting the several complete parameter groups after optimization and adjustment into the virtual model respectively, simulating each complete parameter group respectively through the virtual model, and analyzing the quality of the products generated by the simulation; Analyze the product quality simulated by each set of complete parameters, set the quality error, obtain a set of complete parameters with the best product quality, and substitute it into the production line; A parameter library is established, and the obtained optimal product quality and the complete parameter group used for the product quality are stored in the parameter library, and the two are bound to each other during storage.

5. The rare earth extraction production process simulation method according to claim 4, characterized in that: Create a virtual model of all production-related physical and chemical parameters required for rare earth element extraction, and use the real-time collected production parameters and spectral imaging analysis results to simulate the current actual production status. The specific steps are as follows: Determine total production time; Determine the critical production status and critical production time in the production process based on the production-related physical and chemical parameters required for rare earth element extraction; Based on the production-related physical and chemical parameters required for rare earth element extraction, the activation energy difference and real-time temperature value are determined; Combining the activation energy difference and the real-time temperature value, and amplifying the temperature sensitivity through an exponential function, we can obtain a preliminary energy level weight factor and generate an activation energy weight sequence. Based on the production-related physical and chemical parameters required for rare earth element extraction, a real-time spectral feature vector, a target spectral feature vector and a spectral adjustment coefficient are determined; Determine a spectrum offset according to the real-time spectrum feature vector and the target spectrum feature vector; The spectral offset is input into a hyperbolic tangent function for nonlinear compression to obtain a compressed offset; The spectral adjustment coefficient is used to scale the compressed offset to obtain a normalized spectral offset impact factor; Extract the activation energy weight of each reaction stage from the activation energy weight sequence, and multiply the activation energy weight of each reaction stage by the normalized spectral shift influence factor to obtain a preliminary coincidence contribution value; Perform weighted sum processing on all preliminary contribution values ​​to generate a contribution coefficient set; Construct a bell-shaped distribution function centered on the critical production time; Substituting the real time timestamp into the bell-shaped distribution function to obtain a time diffusion parameter; Based on the contribution coefficient set, the event diffusion parameters are coupled and verified, and after the verification is completed, the event coupling parameter group is output; Calculate and obtain the production status based on the contribution coefficient of the key production status, the key production time and the time diffusion parameter; Construct a virtual model by accumulating all production states within the total production time; Input the current time into the virtual model to obtain the current actual production status.

6. The rare earth extraction production process simulation method according to claim 5, characterized in that: The real-time identification of the current process quality and the issuance of warning information when the process quality is abnormal include: Continuously detect all operating parameters on the production line and identify production trends under these parameters; Analyze the production trend, and when there is a production trend that deviates from the production target, issue a warning message and mark the parameters that cause the production trend.

7. The rare earth extraction production process simulation method according to claim 6, characterized in that: Analyze the production trend, and when there is a production trend that deviates from the production target, issue a warning message and mark the parameters that cause the production trend. The specific steps are as follows: By analyzing the production trend, the current value fluctuation and dynamic reference value of the operating parameters are obtained; Divide the current value fluctuation of each operating parameter by the dynamic reference benchmark value to obtain a normalized fluctuation; The normalized fluctuation is smoothed by using the S-curve function to obtain the smoothed fluctuation; Normalize the smoothed volatility to generate a preliminary weight distribution of volatility; By analyzing the production trend, the real-time characteristics of the parameters are obtained, and the ideal state characteristics and deviation compression adjustment factors are set; Calculate the Euclidean distance between the real-time characteristics of the parameters and the ideal state characteristics in the multidimensional space to obtain the original deviation; Substitute the original deviation into the hyperbolic tangent function, and use the deviation compression adjustment factor to control the sensitivity of the deviation estimation to generate the parameter state deviation coefficient; The parameter weight function is constructed using the preliminary weight distribution of the fluctuation amount and the parameter state deviation coefficient; deriving the operating parameters based on the parameter weight function, and calculating and obtaining the second-order derivatives of the operating parameters; For each pair of operating parameters, the second-order derivatives of the two operating parameters are multiplied to obtain a preliminary synergy value; The product of the second-order derivatives is filtered using an activation function to obtain a filtered synergy value, and the filtered synergy value is amplitude compressed to obtain a synergy strength index; Through the analysis of production trends, the position coordinates and spatial influence coefficients of the parameter control unit are further obtained; Based on the position coordinates of the parameter control units, a geometric distance between each pair of parameter control units is calculated; The geometric distance between each pair of parameter control units is introduced into the exponential attenuation function, and optimized through the spatial influence coefficient to obtain the spatial influence attenuation coefficient; An interaction function is constructed based on the synergy intensity index and the spatial attenuation coefficient, and based on the operating parameters, the interaction value between the parameters is calculated and obtained through the interaction function; Combining the second-order derivative of the parameter and the interaction value between the parameters, the deviation from the production target value is obtained, and all the deviations from the production target values ​​are accumulated to obtain the deviation threshold value; Based on the deviation threshold, the production trend is analyzed, and when the production trend deviates from the production target, a warning message is issued, and the parameters that cause the production trend operation are marked.

8. A rare earth extraction production process simulation system, characterized in that: The system applies the rare earth extraction production process simulation method as described in any one of claims 1 to 7 above, and the system comprises: Spectral imaging analysis module, used to identify the color characteristics of system elements through spectral imaging, and establish a rare earth element prediction model to obtain the element component content based on the element color characteristics analysis; The production control decision module is used to monitor and dynamically collect production parameters in real time, and generate optimized and adjusted production parameters based on the obtained element component content; The environmental simulation module is used to build a virtual environment for rare earth element extraction based on the real-time production parameters obtained, and substitute the optimized and adjusted production parameters into the virtual environment, simulate the production process and results, analyze the process quality of the simulation results, and establish a parameter library. The simulation results and the substituted production parameters are retained in the parameter library, and when the process quality is qualified, the substituted production parameters are synchronized to the production line; The early warning support module is used to identify the current process quality in real time, and when the process quality is abnormal, it will issue a warning message, analyze the abnormal state, and determine the abnormal process flow.

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