Metallurgy reactor intelligent regulation and control method and device based on real-time simulation and monitoring

Abstract

The invention provides an intelligent regulation and control method and device for a metallurgical reactor based on real-time simulation and monitoring, and relates to the technical field of metallurgical intelligent control. The method comprises the following steps: acquiring image data, geometric parameters, melt physical property parameters, process parameters and monitoring data of a smelting process of a metallurgical reactor, and establishing a data set by using the acquired data and analogue simulation results; constructing an image parameter identification model A, a clearance height prediction model B, a multi-physical field prediction model C and a process parameter optimization model D of the metallurgical reactor; and acquiring image data of the smelting process of the metallurgical reactor of the current heat, and intelligently regulating and controlling the technological parameters of the metallurgical reactor based on the established model. The invention relates to a high-efficiency intelligent regulation and control method for a metallurgical reactor, which is based on real-time analogue simulation monitoring and can accurately reflect physical field information in the reactor.

Description

Technical Field

[0001] The present invention relates to the field of metallurgical intelligent control technology, and in particular to an intelligent control method and device for a metallurgical reactor based on real-time simulation and monitoring. Background Art

[0002] Metallurgical reactors such as blast furnaces, a Kanbara Reactor (KR) agitators for hot metal desulfurization, converters, crystallizers, electrolytic cells, etc., as chemical reaction carriers and containers in the metallurgical industry, play an important role in the metallurgical industry. How to control the efficient and stable production of metallurgical reactors is the key to reducing costs and increasing efficiency in the metallurgical industry. At present, production control in the production process of metallurgical reactors is mostly done manually based on information such as temperature and oxygen blowing volume monitored in real time by various sensors on site. However, the information monitored by existing sensors is very limited. It can only monitor a small amount of physical information in a small area and cannot complete the monitoring of detailed physical fields in the entire metallurgical reactor. It is difficult to accurately reflect the overall operation of the metallurgical reactor with only a small amount of monitoring information, which makes it difficult to stably achieve efficient utilization of metallurgical reactor performance through manual control on this basis.

[0003] Simulation refers to the method of using computers to solve the partial differential equations of various physical quantities inside the metallurgical reactor to obtain the information of various physical fields. By coupling multiple physical fields, high-precision simulation of metallurgical reactors can be achieved, and the results obtained have an important guiding role in the regulation of metallurgical reactors. Compared with sensor monitoring, this method can obtain detailed physical field information in the entire metallurgical reactor, but the disadvantage is that the efficiency of simulation is low, and when the production process of the metallurgical reactor changes, it is impossible to quickly solve the physical field information in the metallurgical reactor after the change.

[0004] The calculation model of the metallurgical reactor is complex to change, and it is difficult to consider the impact of the increase in the age of the metallurgical reactor on the geometric model. The results obtained by simulating the initial geometric model can accurately reflect the internal physical field information of the metallurgical reactor in the early stage, but cannot accurately reflect the internal physical field information of the metallurgical reactor at different ages.

[0005] In the prior art, there is a lack of an efficient and intelligent control method for metallurgical reactors that is based on real-time simulation monitoring and can accurately reflect the physical field information in the reactor. Summary of the invention

[0006] In order to solve the technical problem that the production process of the metallurgical reactor can only monitor a small part of the physical information in a small area, and cannot accurately reflect the physical field information inside the metallurgical reactor, which makes it difficult to stably achieve efficient utilization of the performance of the metallurgical reactor through manual control on this basis, the embodiment of the present invention provides an intelligent control method and device for the metallurgical reactor based on real-time simulation and monitoring. The technical solution is as follows:

[0007] On the one hand, a method for intelligent control of a metallurgical reactor based on real-time simulation and monitoring is provided, the method being implemented by an intelligent control device, the method comprising:

[0008] During the smelting process, the metallurgical reactor is monitored by monitoring equipment. max Heat data collection, obtaining first image data before the metallurgical melt is charged into the metallurgical reactor, first geometric parameters of the metallurgical reactor before the metallurgical melt is charged into the metallurgical reactor, second image data after the metallurgical melt is charged into the metallurgical reactor, second geometric parameters of the metallurgical reactor after the metallurgical melt is charged into the metallurgical reactor, melt physical property parameters, first process parameters and physical field monitoring data;

[0009] Using the first image data and N of the first geometric parameters a The heat data constructs a data set a; according to the second image data, the second geometric parameters and N of the melt physical property parameters a The furnace data constructs data set b;

[0010] According to the data set a, a model is constructed using the 3DGS algorithm to obtain an image parameter recognition model A; based on OpenCV, a model is constructed according to the data set b to obtain a clearance height prediction model B;

[0011] Based on the image parameter recognition model A and the headroom prediction model B, calculation is performed according to the first image data and the second image data to obtain the melt headroom, the topological scheme of the reactor geometry, the reactor geometry model and the structured grid of the reactor geometry model;

[0012] Based on the reactor geometry model, the structured grid and the preset metallurgical industry specifications, according to the melt physical property parameters, the first process parameters and the physical field monitoring data, CFD software is used to perform simulation to obtain multi-physical field simulation data;

[0013] Construct N according to the multi-physics field simulation data, the reactor geometry model, the melt physical property parameters, the melt clearance height and the first process parameters. a The dataset c and N of the group data maxA data set d of group data; using the data set c, training a first deep learning prediction model to obtain a multi-physics field prediction model C;

[0014] Inputting the data set d into the multi-physics field prediction model C for data prediction to obtain multi-physics field prediction data; constructing a data set e according to the multi-physics field prediction data, the reactor geometry model, the melt physical property parameters, the melt clearance height and the first process parameter; using the data set e, training the second deep learning prediction model to obtain a process parameter optimization model D;

[0015] Through the monitoring equipment, the third image data of the current heat of metallurgical melt before being loaded into the metallurgical reactor, the fourth image data after the metallurgical melt is loaded into the metallurgical reactor, the physical field data of the current heat and the target physical field data are obtained; based on the preset physical field threshold range, according to the third image data of the current heat of metallurgical melt before being loaded into the metallurgical reactor, the fourth image data after the metallurgical melt is loaded into the metallurgical reactor, the melt physical property parameters, the physical field data of the current heat and the target physical field data, the reactor process parameters are controlled through the image parameter recognition model A, the clearance height prediction model B, the multi-physical field prediction model C and the process parameter optimization model D to obtain the second process parameters; the metallurgical reactor is controlled according to the second process parameters.

[0016] On the other hand, an intelligent control device of a metallurgical reactor based on real-time simulation and monitoring is provided, and the device is applied to an intelligent control method of a metallurgical reactor based on real-time simulation and monitoring, and the device comprises:

[0017] Training data acquisition module, used to monitor the metallurgical reactor during the smelting process through monitoring equipment max Heat data collection, obtaining first image data before the metallurgical melt is charged into the metallurgical reactor, first geometric parameters of the metallurgical reactor before the metallurgical melt is charged into the metallurgical reactor, second image data after the metallurgical melt is charged into the metallurgical reactor, second geometric parameters of the metallurgical reactor after the metallurgical melt is charged into the metallurgical reactor, melt physical property parameters, first process parameters and physical field monitoring data;

[0018] A data set construction module is used to use the first image data and N of the first geometric parameters a The heat data constructs a data set a; according to the second image data, the second geometric parameters and N of the melt physical property parameters a The heat data constructs data set b; the N a The maximum value N of the smelting furnaces in the current age of the metallurgical reactor max one third of

[0019] The first model training module is used to construct a model based on the data set a using the 3DGS algorithm to obtain an image parameter recognition model A; based on OpenCV, construct a model based on the data set b to obtain a clearance height prediction model B;

[0020] A topology scheme building module, for calculating, based on the image parameter recognition model A and the headroom prediction model B, the melt headroom, a topology scheme of the reactor geometry, a reactor geometry model, and a structured grid of the reactor geometry model according to the first image data and the second image data;

[0021] A multi-physics field simulation module, for performing simulation using CFD software based on the reactor geometry model, the structured grid and preset metallurgical industry specifications, according to the melt physical property parameters, the first process parameters and the physical field monitoring data, to obtain multi-physics field simulation data;

[0022] The second model training module is used to construct N according to the multi-physics field simulation data, the reactor geometry model, the melt physical property parameters, the melt clearance height and the first process parameters. a The dataset c and N of the group data max A data set d of group data; using the data set c, training the first deep learning prediction model to obtain a multi-physics field prediction model C;

[0023] The third model training module is used to input the data set d into the multi-physics field prediction model C for data prediction to obtain multi-physics field prediction data; construct a data set e according to the multi-physics field prediction data, the reactor geometry model, the melt physical property parameters, the melt clearance height and the first process parameter; use the data set e to train the second deep learning prediction model to obtain a process parameter optimization model D;

[0024] The metallurgical reactor control module is used to obtain, through monitoring equipment, third image data of the current heat of metallurgical melt before it is loaded into the metallurgical reactor, fourth image data after it is loaded into the metallurgical reactor, physical field data of the current heat, and target physical field data; based on a preset physical field threshold range, according to the third image data of the current heat of metallurgical melt before it is loaded into the metallurgical reactor, fourth image data after it is loaded into the metallurgical reactor, physical property parameters of the melt, physical field data of the current heat, and target physical field data, the reactor process parameter is controlled through the image parameter recognition model A, the clearance height prediction model B, the multi-physical field prediction model C, and the process parameter optimization model D to obtain a second process parameter; and the metallurgical reactor is controlled according to the second process parameter.

[0025] On the other hand, an intelligent control device is provided, which includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned intelligent control methods of metallurgical reactors based on real-time simulation and monitoring is implemented.

[0026] On the other hand, a computer-readable storage medium is provided, in which at least one instruction is stored, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned intelligent control methods of metallurgical reactors based on real-time simulation and monitoring.

[0027] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0028] The present invention proposes an intelligent control method for a metallurgical reactor based on real-time simulation and monitoring, combining image recognition, machine learning and simulation methods, using geometric image recognition technology and parametric automatic modeling and meshing technology, and obtaining a large amount of simulation data; using machine learning algorithms to achieve real-time simulation prediction of metallurgical reactors, and on this basis, completing the dynamic monitoring of multiple physical fields inside the metallurgical reactor; establishing a deep learning optimization prediction model to achieve intelligent control of metallurgical reactors. The present invention can achieve real-time simulation of multiple physical fields in metallurgical reactors and intelligent control of metallurgical reactors, solving the problems of difficulty in acquiring monitoring information of physical fields in metallurgical reactors during production, low simulation efficiency and inability to acquire information in real time, and dependence on experience and difficulty in control of metallurgical reactors. The present invention is an efficient and intelligent control method for metallurgical reactors based on real-time simulation monitoring and capable of accurately reflecting physical field information in reactors. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 It is a flow chart of an intelligent control method of a metallurgical reactor based on real-time simulation and monitoring provided by an embodiment of the present invention;

[0031] Figure 2 It is a block diagram of an intelligent control device for a metallurgical reactor based on real-time simulation and monitoring provided by an embodiment of the present invention;

[0032] Figure 3 It is a structural schematic diagram of an intelligent control device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0034] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0035] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0036] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0037] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0038] The embodiment of the present invention provides an intelligent control method for a metallurgical reactor based on real-time simulation and monitoring. The method can be implemented by an intelligent control device, which can be a terminal or a server. Figure 1 The flowchart of the intelligent control method of the metallurgical reactor based on real-time simulation and monitoring is shown. The processing flow of the method may include the following steps:

[0039] S1. During the smelting process, the metallurgical reactor is monitored by monitoring equipment. max Furnace data collection obtains the first image data of the metallurgical melt before it is charged into the metallurgical reactor, the first geometric parameters of the metallurgical reactor before it is charged into the metallurgical reactor, the second image data after the metallurgical melt is charged into the metallurgical reactor, the second geometric parameters of the metallurgical reactor after the metallurgical melt is charged into the metallurgical reactor, the melt physical property parameters, the first process parameters and the physical field monitoring data.

[0040] In a feasible implementation manner, the present invention collects N (N=0,1,2…N max(Maximum age of metallurgical reactor)) After each furnace, the image data of the metallurgical reactor before and after the metallurgical melt is loaded, the three-dimensional structure and geometric parameters of the metallurgical reactor, the physical properties of the metallurgical melt in the reactor, the process parameters used in production and the monitoring data monitored by the monitoring equipment under the process.

[0041] Metallurgical reactors include but are not limited to blast furnaces, KR stirrers, converters, ladles, tundishes, crystallizers and other metallurgical reactors. The geometric parameters of the metallurgical reactor should include the length, width, height, etc. of the metallurgical reactor, and the parameter data necessary to construct the mathematical model of the metallurgical reactor. The acquired image data should cover but not be limited to the three views of the interior of the metallurgical reactor before and after the metallurgical melt is loaded; the acquired melt physical properties should include but are not limited to the density, temperature, viscosity, etc. of the metallurgical melt. The first process parameters acquired should include: such as oxygen lance height, water inlet immersion depth, etc. The parameters monitored on site should include but are not limited to the internal liquid level fluctuations of the metallurgical reactor, the temperature of the metallurgical melt, the distribution and intensity of the magnetic field, etc.

[0042] Among them, N max It is the maximum value of smelting furnace N in the current age of the metallurgical reactor.

[0043] In a feasible implementation, monitoring data is collected using multiple types of monitoring equipment, including: images, surveillance cameras: measuring the level of metallurgical melts, ultrasonic level meters; temperature measurement, thermocouples, infrared thermometers: pressure transmissions: used to measure liquid and gas density, pressure and flow; oxygen sensors, measuring oxygen concentration in the production process, etc., totaling N max Group data.

[0044] S2, using the first image data and N of the first geometric parameters a The heat data is used to construct a data set a; according to the second image data, the second geometric parameters and the melt physical property parameters N a The furnace data constructs data set b.

[0045] Among them, N a The maximum value N of the smelting furnaces in the current age of the metallurgical reactor max one third of.

[0046] In a feasible embodiment, the N used in this step is a The number of furnaces is the total number of furnaces N max One third of a Heat data comes from N max The early, mid and late data of the furnace data, randomly select data from the early, mid and late stages of the total furnace data to construct corresponding data sets a and b.

[0047] Based on the image data and geometric parameters of the metallurgical reactor before being loaded with the metallurgical melt, a dataset a is constructed, and the dataset a is divided into a training set and a test set in a ratio of 8:2.

[0048] Based on the image data and geometric parameters of the metallurgical reactor after the metallurgical melt is loaded, the melt clearance height is calculated according to the melt physical property parameters, and a data set b is constructed. The data set b is divided into a training set and a test set in a ratio of 8:2.

[0049] S3. Based on data set a, the 3DGS algorithm is used to build a model to obtain image parameter recognition model A. Based on OpenCV, the model is built according to data set b to obtain clearance height prediction model B.

[0050] In a feasible implementation, an algorithm for three-dimensional reconstruction and rendering (3D Gaussian Splatting, 3DGS) is used to establish an image parameter recognition model A. Model A is trained based on the training set of data set a, and the trained model A is used to extract the internal geometric parameters of the metallurgical reactor; the three-dimensional structure of the metallurgical reactor is constructed according to the geometric parameters. Based on the deep learning algorithm, a deep learning prediction model B is established, and the trained model B is used to predict the clearance height of the melt in the metallurgical reactor.

[0051] The test sets in data sets a and b are used to verify the accuracy of image parameter recognition model A and clearance height prediction model B respectively.

[0052] For model A: The three-dimensional structure and geometric parameters obtained from model A are used to build a mathematical model. In the same coordinate system, 100 point coordinates are randomly selected in the test case. The relative error A of the coordinate values ​​in the x, y, and z directions is x , A y , A z Less than or equal to 5%, and more than 95% of each model in the test set data meets A x , A y , A z If the error is less than or equal to 5%, model A is considered accurate, otherwise it is considered inaccurate and model A should be readjusted and trained, and the accuracy of the model should be verified again until the model meets the requirements. For model B: if the relative error B1 between the predicted clearance height of the melt and the actual clearance height of more than 95% of the image data in the test set is less than or equal to 5%, the predicted model B is considered accurate, otherwise it should be readjusted and trained as above until it meets the requirements.

[0053] Among them, the accuracy rate A x , A y , A z , the calculation formula of B1 is as follows (1), (2), (3), (4):

[0054] (1);

[0055] (2);

[0056] (3);

[0057] (4);

[0058] S4. Based on the image parameter recognition model A and the clearance height prediction model B, calculations are performed according to the first image data and the second image data to obtain the melt clearance height, the topological scheme of the reactor geometry, the reactor geometry model and the structured grid of the reactor geometry model.

[0059] Optionally, based on the image parameter recognition model A and the headroom prediction model B, calculation is performed according to the first image data and the second image data to obtain the melt headroom, the topological scheme of the reactor geometry, the reactor geometry model and the structured grid of the reactor geometry model, including:

[0060] Inputting the first image data into the image parameter recognition model A to generate parameters, thereby obtaining third geometric parameters;

[0061] Inputting the second image data into the headroom height prediction model B to predict the melt headroom height, and obtaining the melt headroom height;

[0062] A topological scheme for constructing a metallurgical reactor geometric model according to a third geometric parameter and a melt clearance height;

[0063] Based on Python and OpenFOAM, the mesh parameterization code of the metallurgical reactor geometry was written according to the topological scheme to obtain the pre-processing program;

[0064] The third geometric parameter is input into a pre-processing program to generate a mesh, thereby obtaining a reactor geometric model and a structured mesh of the reactor geometric model.

[0065] In a feasible implementation mode, model A is used to extract the third geometric parameters of all furnaces within the service life of the metallurgical reactor, and the melt clearance height in the metallurgical reactor is predicted by combining model B. All three-dimensional structures are approximated by simple geometric bodies (cylinders, spheres, cuboids, etc.) to obtain a topological solution that satisfies the metallurgical reactor model after all times of use.

[0066] Use Python combined with OpenFOAM to write a pre-processing program for the automatic generation of metallurgical reactor models and their structured mesh parameterization.

[0067] For metallurgical reactors after using different furnaces, by acquiring images before loading metallurgical melt, the three-dimensional structure and geometric parameters of the metallurgical reactor are obtained using model A, and the obtained geometric parameters are input into the above-mentioned pre-processing program to quickly batch generate metallurgical reactor models and structured grids after using different furnaces.

[0068] S5. Based on the reactor geometry model, structured grid and preset metallurgical industry specifications, according to the melt physical property parameters, the first process parameters and the physical field monitoring data, CFD software is used for simulation to obtain multi-physical field simulation data.

[0069] Optionally, based on the reactor geometry model, the structured grid and the preset metallurgical industry specifications, according to the melt property parameters, the first process parameters and the physical field monitoring data, CFD software is used to perform simulation to obtain multi-physical field simulation data, including:

[0070] Based on the preset metallurgical industry specifications, determine the operating range according to the first process parameters, melt physical property parameters and physical field monitoring data; formulate a simulation plan according to the gradient based on the operating range;

[0071] Based on the simulation scheme, according to the reactor geometric model and structured grid, CFD software is used to perform simulation to obtain multi-physics field simulation data.

[0072] In a feasible implementation method, multiple groups of simulation plans are formulated with reference to the operating range determined by the metallurgical industry specifications, according to actual production technical conditions and reasonable gradient conditions.

[0073] Use CFD software to read the generated reactor geometry models and structured grids of different furnaces, and perform calculations according to the established simulation plan. Use CFD software such as Fluent, OpenFOAM, etc. and the pre-processing software to generate metallurgical reactor models and grids, and set boundary conditions and initial values ​​for calculations according to the established simulation plan.

[0074] The simulation results are verified using the physical field monitoring data obtained by the on-site monitor. If the accuracy of the simulation results does not meet the use requirements, the calculation model and the grid are readjusted until the accuracy of the simulation results meets the requirements. It is characterized in that the simulation calculation results need to be verified. The verification method is to compare the values ​​monitored by the reactor production on-site monitor (such as the melt temperature of the tundish at a certain moment; the end temperature of the converter; the S content of the KR agitator end point, etc.) with the corresponding simulation calculation values. The verification method is to use the same production plan, arbitrarily take multiple groups of simulation results and corresponding monitoring results under different furnace ages. If more than 95% of the results have a relative error M1 less than 5%, the simulation results are considered correct. Otherwise, the simulation results are considered inaccurate, and the calculation model and the number of grids need to be readjusted and recalculated until the accuracy meets the requirements. If the accuracy still does not meet the requirements after adjusting the calculation model and the number of grids, it is necessary to re-optimize the topology structure and write the pre-processing software to improve the grid quality and ensure the calculation accuracy. The calculation formula for the relative error is as follows (5):

[0075] (5)

[0076] S6, constructing N according to the multi-physics field simulation data, the reactor geometry model, the melt physical property parameters, the melt clearance height and the first process parameters a The dataset c and N of the group data max A data set d of group data; using the data set c, the first deep learning prediction model is trained to obtain a multi-physics field prediction model C.

[0077] In a feasible implementation manner, the present invention uses post-processing software (such as Tecplot) to post-process the calculated results, extracts data of multiple physical fields in the results such as temperature field, flow field, solute field, magnetic field, etc., and compares the extracted data with the corresponding N a The reactor geometry model, melt properties, melt headroom and N max The first process parameter of the group is used to construct a data set c, and the data set c is divided into a training set and a test set at a ratio of 9:1. Similarly, a data set d is constructed and divided into a training set and a test set at a ratio of 9:1. The data in data set c is N a Heat group data, the data in data set d is N max Total heat group data.

[0078] All the data in the test set in the data set c are used to verify the prediction model C. The verification standard is to randomly select the prediction results of 100 points inside the metallurgical container. If the relative error C1 between the prediction results (temperature field, solute field, etc.) of the 100 points and the simulation calculation results of the 100 points at the corresponding positions in the test set in c is less than or equal to 5% and more than 95% of the test data in the test set meet the requirements, the recognition model C is considered to be accurate. Otherwise, the recognition model C is considered to be inaccurate. The recognition model C should be readjusted and trained, and the accuracy of the model should be verified again until the model meets the requirements. The calculation formula of the relative error C1 is as follows (6):

[0079] (6);

[0080] S7. Input the data set d into the multi-physics field prediction model C for data prediction to obtain multi-physics field prediction data; construct a data set e based on the multi-physics field prediction data, the reactor geometry model, the melt physical properties, the melt clearance height and the first process parameters; use the data set e to train the second deep learning prediction model to obtain the process parameter optimization model D.

[0081] In a feasible implementation manner, the data in the test set of the data set e is used to obtain the optimized second process parameters through the prediction model D, and the second process parameters are passed into the model C. If the relative error D1 between the multi-physical field prediction result of the model C and the target physical field is less than 5% and more than 95% of the data in the test set meet the relative error D1 of less than 5%, then the model D is considered accurate, otherwise the prediction model D is considered inaccurate, and the recognition model D should be readjusted and trained, and the accuracy of the model should be verified again until the model meets the requirements; arbitrarily take 100 points at the same position in the target physical field and the predicted physical field.

[0082] The calculation formula of the relative error D1 is as follows (7):

[0083] (7);

[0084] Among them, the model structure of the first deep learning prediction model and the second deep learning prediction model is a convolutional neural network, a long short-term memory neural network or a bidirectional gated recurrent neural network.

[0085] In a feasible implementation, different models and methods are used in different containers and working conditions. Commonly used deep learning prediction models include convolutional neural networks, long short-term memory neural networks, principal component analysis, multi-head attention mechanisms, bidirectional gated loop algorithms, etc. Depending on the complexity of the model, it can be a combination of multiple neural network models, and the hyperparameters are adjusted according to the model characteristics.

[0086] S8. Obtain, through monitoring equipment, the third image data of the current heat of metallurgical melt before it is loaded into the metallurgical reactor, the fourth image data after it is loaded into the metallurgical reactor, the physical field data of the current heat, and the target physical field data; based on a preset physical field threshold range, according to the third image data of the current heat of metallurgical melt before it is loaded into the metallurgical reactor, the fourth image data after it is loaded into the metallurgical reactor, the melt physical property parameters, the physical field data of the current heat, and the target physical field data, control the reactor process parameters through the image parameter recognition model A, the clearance height prediction model B, the multi-physical field prediction model C, and the process parameter optimization model D to obtain the second process parameters; control the metallurgical reactor according to the second process parameters.

[0087] Optionally, based on a preset physical field threshold range, according to the third image data before the metallurgical melt of the current furnace is loaded into the metallurgical reactor, the fourth image data after the metallurgical melt is loaded into the metallurgical reactor, the melt physical property parameters, the current furnace physical field data and the target physical field data, the reactor process parameters are regulated by the image parameter recognition model A, the clearance height prediction model B, the multi-physical field prediction model C and the process parameter optimization model D to obtain the second process parameters, including:

[0088] Inputting the third image data into the image parameter recognition model A to obtain the current furnace geometric parameters; performing data processing through the pre-processing program according to the current furnace geometric parameters to obtain the current reactor geometric model;

[0089] Inputting the fourth image data into the headroom height prediction model B to obtain the headroom height of the melt of the current furnace;

[0090] Input the current reactor geometry model, the current furnace melt clearance height, the current furnace physical field data, the target physical field data and the melt property parameters into the process parameter optimization model D to obtain the predicted process parameters;

[0091] Input the current reactor geometry model, the current furnace melt clearance height, melt physical property parameters and predicted process parameters into the multi-physics field prediction model C to obtain predicted multi-physics field data;

[0092] According to the preset physical field threshold range, the predicted multi-physical field data is verified to obtain the physical field verification result;

[0093] When the physical field verification result is passed, the predicted process parameter is determined as the second process parameter.

[0094] In a feasible implementation manner, an image of a metallurgical reactor before and after a certain heat of metallurgical melt is loaded in actual production, and physical property parameters of the metallurgical melt in the reactor are obtained, a geometric model of the metallurgical reactor is obtained using model A and pre-processing software, and a headroom height of the melt in the metallurgical reactor is obtained using model B. Based on the above results and the initial physical field / physical field to be optimized and the target physical field monitored on site, they are input into model D, and the optimized process parameters for achieving the target physical field are output. The prediction results of model D are used as inputs of model C, and model C is run in parallel with actual production to obtain the prediction results of multiple physical fields in the metallurgical reactor using the production process through real-time monitoring;

[0095] The maximum production capacity of the metallurgical reactor is restricted by the maximum carrying capacity of the equipment, and the threshold range of the relevant physical field value is determined according to the power and equipment size of the metallurgical reactor, the refractory material and the furnace age. The present invention sets the threshold range of the relevant physical field, such as the temperature field, the flow field, etc., according to the current production conditions of the metallurgical reactor itself.

[0096] After the actual production starts, if the physical field results output by model C exceed the preset physical field threshold range at a certain moment, the physical field monitoring information and physical field threshold information at this moment are input into model D to obtain the process parameters that can achieve the target physical field and enable stable production again, and the physical field threshold range is verified until the process parameters meet the production requirements under the target physical field.

[0097] In a feasible implementation manner, the present invention is based on the application of the KR agitator as follows:

[0098] After 300 heats of use, a total of 300 sets of image data of the KR agitator before and after being loaded with molten iron, the three-dimensional structure and geometric parameters of the KR agitator, the physical properties and weight of the molten iron, and the process parameters (immersion depth of the agitator paddle, speed of the agitator paddle) used in each heat of production, the temperature measured under the process, and the S content obtained by manual sampling were obtained.

[0099] The images of the KR stirrer before loading molten iron after different heats and the geometric parameters of the stirrer were obtained to construct a data set a, and the model A was established using the 3DGS (3D Gaussian Splatting) algorithm.

[0100] A total of 90 sets of data were extracted, including furnaces 1-10, 40-50, 80-90, 100-110, 140-150, 180-190, 200-210, 240-250, and 280-290. Image data and geometric parameters of the KR stirrer before the stirrer is loaded with molten iron. The image data include the three views of the KR stirring head and the three views of the KR stirring tank. The geometric parameters include the geometric parameters of the KR stirring head and the geometric parameters of the KR stirring tank. The constructed data a is divided into training set and test set in a ratio of 8:2. The 3DGS algorithm is used to establish the 3D model A of the KR stirrer. The model can quickly identify the 3D structure and geometric parameters of the KR stirrer by inputting the three views of the stirring head and the stirring tank. The test set data in the data set a is used to verify the model A. The relative error A1 of the 18 groups of results constructed by the 3D structure and geometric parameters obtained by using the image data of a total of 18 heats in the test set and the model constructed by the actual size in the same coordinate system is less than 5%, which proves that the model is accurate.

[0101] The images of the KR agitator after being loaded with molten iron after different heats and the geometric parameters of the agitator, the tonnage and density of the loaded molten iron are obtained, and the clearance height of the molten pool in the KR agitator (i.e., the initial liquid level height) is calculated based on the obtained geometric parameters of the agitator, the tonnage and density of the molten iron. The calculated clearance height and the images of the KR agitator after being loaded with molten iron are used to construct a dataset b, and a deep learning algorithm is used to establish model B, and model B is verified.

[0102] In this step, a total of 90 groups of data were extracted from furnaces 1-10, 40-50, 80-90, 100-110, 140-150, 180-190, 200-210, 240-250, and 280-290. The geometric parameters of the KR stirrer after the stirrer is loaded with molten iron, the image data after the molten iron is loaded, and the tonnage and density of the loaded molten iron. The image data should be able to clearly reflect the relative position of the molten iron and the top of the stirring tank. The geometric parameters include the geometric parameters of the KR stirring head and the geometric parameters of the KR stirring tank. The clearance height of the molten pool is calculated by obtaining the geometric parameters of the stirring tank and the tonnage and density of the molten iron. The image after the molten iron is loaded and the calculated clearance height of the molten pool are used to construct a data set b and divide the training set and the test set into a ratio of 8:2. The deep learning algorithm is used to establish a prediction model B for the clearance height of the molten pool. The model can predict the clearance height of the molten pool in the current furnace by inputting the image after the molten iron is loaded. The test set data in the data set b are used to test the model B. The relative error B1 between the clearance height of the molten pool predicted by the 18 groups of image data in the test set and the actual clearance height is less than 5%, which proves that the model is accurate.

[0103] The established model A was used to identify the image data of 300 batches of KR agitator, and the three-dimensional structure of the KR agitator in all batches was obtained. A topological method for automatic generation of structured grids was developed based on the obtained three-dimensional structure. According to the developed topological scheme, the Python programming language combined with the open source software OpenFOAM was used to write the pre-processing software for automatic integrated generation of model and structured grid parameterization.

[0104] The software written in this step can quickly complete the rapid generation of the KR stirrer model and structured grid by inputting the KR stirrer model parameters, and the generated grid has good quality, and the model and grid generation process is within the second range. And as the KR stirrer furnace age increases, when the KR stirring head and stirring tank are worn and eroded, the corresponding geometric model and structured grid can be regenerated within seconds by modifying the corresponding geometric parameters.

[0105] According to the acquired on-site KR production process: agitator speed, agitator head immersion depth, desulfurizer composition and physical parameters of the desulfurizer: mass, initial molten iron temperature, density and viscosity, etc., multiple groups of orthogonal simulation schemes are formulated according to reasonable gradient conditions. The written pre-processing software is used to quickly generate 90 groups of models and structured grids with furnace ages of 1-10 furnaces, 40-50 furnaces, 80-90 furnaces, 100-110 furnaces, 140-150 furnaces, 180-190 furnaces, 200-210 furnaces, 240-250 furnaces, and 280-290 furnaces, and the simulation calculation is carried out according to the formulated simulation scheme, and the obtained simulation results are verified by monitoring the results using the on-site monitor.

[0106] The number of KR agitator grids generated in this step is between 800,000 and 1,000,000. The simulation software used is Fluent2023R1. The calculation models used are RNG k-ε turbulence model, VOF two-phase flow model, DPM discrete phase model and unreacted nuclear chemical reaction model. The time step is 0.005 seconds, and each case is calculated to 1000s. A total of 1800 orthogonal simulation schemes were conducted in 90 models. The calculation results were verified by using the molten iron temperature monitored on site and the S content in the stirring tank at different times. The molten iron temperature simulated by 90 models with furnace ages of 1-10, 40-50, 80-90, 100-110, 140-150, 180-190, 200-210, 240-250 and 280-290 were compared with the monitored values ​​and measured values ​​under the same process parameters on site. After comparative calculation, it was found that the relative error M1 of 88 of the 90 simulation results was below 5%, proving that the simulation results were accurate and reliable.

[0107] The KR production process, molten iron physical properties, KR agitator geometric model and simulation calculation results are used to construct a data set c. A deep learning algorithm is used to establish a multi-physical field model C, and model C is verified.

[0108] In this step, the KR production process includes: agitator speed, agitator head immersion depth, desulfurizer composition and quality, etc., molten iron physical properties include: initial molten iron temperature, S content, etc., and a total of 90 heats of KR agitator geometric models, where the geometric models of different heats are automatically generated using the pre-processing program parameterization and the simulation calculation results (temperature field, flow field, solute field) of 900 groups in step 4 to construct a data set c and divide the training set and test set into a ratio of 9:1, and use a deep learning algorithm to establish a multi-physical field prediction model C. The prediction model can complete the full-time prediction results of the multi-physical field under the model and production technology by inputting the KR production process, molten iron physical properties and the geometric model of the agitator, and modify the production process input to the model at a certain moment, and obtain the multi-physical field prediction results after the production process is modified midway. Model c was tested using 90 groups of data in the test set in data set c, of which the relative errors C1 of the solute field and temperature field of 88 groups of results and the actual clearance monitoring value were less than 5%, proving that the model is accurate.

[0109] The maximum carrying capacity of the KR agitator on site was obtained, and the geometric models of 300 sets of data were generated using model A and pre-processing software. Based on the prediction model C, 300 sets of KR agitator geometric models of different furnaces were input, and each set of models was matched with 20 sets of production processes and 10 sets of different molten pool depths and molten iron physical properties, totaling 60,000 sets of input values. 60,000 sets of multi-physical field full-time prediction results were obtained, and the input and output data were constructed into a data set d. The deep learning algorithm was used to establish the prediction model D of the production process, and the model D was verified.

[0110] The maximum load capacity of the KR stirrer in this step includes the maximum speed of the stirrer, the depth of the stirring tank, the temperature that the refractory material can withstand, etc., the KR production process: the speed of the stirrer, the immersion depth of the stirring head, the composition and quality of the desulfurizer, etc., and the production, the physical properties of the molten iron include: the initial molten iron temperature, the S content, etc., and the geometric model of the KR stirrer corresponding to the furnace, where the geometric models of different furnaces are automatically generated using the parameterized program written and the simulation prediction results in step 7 (temperature field, flow field, solute field) include data that exceeds the maximum load capacity of the equipment, construct a data set d and divide it into a training set and a test set at a ratio of 9:1, and use a deep learning algorithm to establish a multi-physical field prediction model D. The prediction model can predict the production process of achieving the target physical field under the current geometric model by inputting the physical properties of the molten iron, the depth of the molten pool, the geometric model of the stirrer, the physical field at the initial or time to be optimized, and the target physical field. The model d was verified using the test set data in the data set d, and the model was tested using 6000 sets of data in the test set. A total of 5973 sets of results were obtained, and the production (tuning) technical solutions were returned to model C to obtain (temperature field, flow field, solute field) and the relative error D1 of the target physical field was less than 5%, proving that the model is accurate. The above-established models A, B, C, D and the pre-processing program interfaces were unified to achieve data transmission between them.

[0111] In a feasible implementation manner, images of the 83rd furnace of the on-site KR stirrer before and after the metallurgical melt is loaded are obtained, and the images before the 83rd furnace is loaded with the metallurgical melt are transmitted to model A to output the real three-dimensional structure and geometric parameters of the current KR stirrer, and the images after the molten iron is loaded are transmitted to model B to output the clearance height of the current molten pool, and the geometric parameter results output by model A are transmitted to the compiled software to obtain the geometric mathematical model of the current KR stirrer, and the obtained geometric model is automatically transmitted to model D in step 7, and the temperature information of 1300°C and the initial S content information of 400ppm monitored by the on-site monitor are converted into temperature field and solute field and transmitted to model D, and at the same time, the target temperature field of more than 1250°C, the solute field S content of less than 8ppm, the maximum load capacity of the equipment, the stirring tank depth of 4640mm, the maximum speed of the stirrer of 120r / min and the physical property parameters of the loaded molten iron density of 6700 kg∙m −3 Viscosity 6.486×10 −3 kg∙m −1 ∙s −1. Model D outputs the production process that meets the maximum capacity of the equipment under this goal: the immersion depth of the stirring paddle is 1700mm, the stirring speed is 110r / min, the desulfurizer is 2370kg, and the stirring time is 700s. The technical solution output by model D, as well as the stirrer model, melt physical property parameters and initial physical field information are automatically transferred to model C. Model C outputs the physical field results of the stirrer at all times under this solution. The results show that the melt temperature is not lower than 1250℃ within 700s, and the melt does not splash out of the stirring tank. After 700 seconds, the sulfur content of the molten iron is 7ppm, achieving the production goal. The technical solution output by model D was used in on-site production and achieved corresponding production results.

[0112] The images of the 137th furnace of the KR agitator on site before and after the molten iron is loaded are obtained. The images before the 137th furnace is loaded with molten iron are transmitted to model A to output the real three-dimensional structure and geometric parameters of the current reactor. The images after the metallurgical melt is loaded are transmitted to model B to output the clearance height of the metallurgical melt. The geometric parameter results output by model A are transmitted to the compiled software to obtain the geometric mathematical model of the current KR agitator. The obtained geometric model is automatically transmitted to model D in step 7. The temperature information 1300℃ and the initial S content information 420ppm monitored by the on-site monitor are converted into temperature field and solute field and transmitted to model D. At the same time, the target temperature field above 1250℃, the solute field S content below 8ppm, the maximum load capacity of the equipment, the stirring tank depth of 4640mm, the maximum rotation speed of the agitator of 120r / min and the physical property parameters of the loaded molten iron density of 6700 kg∙m are input into model D. −3 Viscosity 6.486×10 −3 kg∙m −1 ∙s −1. Model D outputs the production process that meets the maximum capacity of the equipment under this goal, with a stirring paddle immersion depth of 1700mm, a stirring speed of 115r / min, a desulfurizer of 2370kg, and a stirring time of 700s. The technical solution output by model D, as well as the agitator model, melt physical property parameters, and initial physical field information are automatically transmitted to model C. Model C outputs the physical field results of the agitator at all times under this solution. The results show that the melt temperature is not lower than 1250℃ within 700s, but it shows that molten iron flew out of the stirring tank at 430s. The abnormal result at 430s is transmitted to model D, and the target phase field distribution result is also transmitted. Model D outputs the corresponding optimized production process, adjusting the stirring paddle immersion depth to 2000mm, reducing the speed to 100r / min, and extending the stirring time to 720s. The output technical solution was automatically transferred to model C. When no molten iron splashing occurred at 425s, the technical solution was adjusted to obtain the corresponding prediction results. The results showed that molten iron splashing occurred at subsequent moments. In addition, at 720s, the S content of the molten iron was 8ppm and the temperature was 1258℃, which met the generation requirements. The solution was used in on-site production and achieved production results consistent with the prediction results.

[0113] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0114] The present invention proposes an intelligent control method for a metallurgical reactor based on real-time simulation and monitoring, combining image recognition, machine learning and simulation methods, using geometric image recognition technology and parametric automatic modeling and meshing technology, and obtaining a large amount of simulation data; using machine learning algorithms to achieve real-time simulation prediction of metallurgical reactors, and on this basis, completing the dynamic monitoring of multiple physical fields inside the metallurgical reactor; establishing a deep learning optimization prediction model to achieve intelligent control of metallurgical reactors. The present invention can achieve real-time simulation of multiple physical fields in metallurgical reactors and intelligent control of metallurgical reactors, solving the problems of difficulty in acquiring monitoring information of physical fields in metallurgical reactors during production, low simulation efficiency and inability to acquire information in real time, and dependence on experience and difficulty in control of metallurgical reactors. The present invention is an efficient and intelligent control method for metallurgical reactors based on real-time simulation monitoring and capable of accurately reflecting physical field information in reactors.

[0115] Figure 2 The block diagram of an intelligent control device for a metallurgical reactor based on real-time simulation and monitoring is shown according to an exemplary embodiment. The device is used for an intelligent control method for a metallurgical reactor based on real-time simulation and monitoring. Figure 2The device includes a training data acquisition module 210, a data set construction module 220, a first model training module 230, a topology solution construction module 240, a multi-physics field simulation module 250, a second model training module 260, a third model training module 270 and a metallurgical reactor control module 280. Among them:

[0116] The training data acquisition module 210 is used to monitor the metallurgical reactor N during the smelting process through monitoring equipment. max Heat data collection, obtaining first image data before the metallurgical melt is charged into the metallurgical reactor, first geometric parameters of the metallurgical reactor before the metallurgical melt is charged into the metallurgical reactor, second image data after the metallurgical melt is charged into the metallurgical reactor, second geometric parameters of the metallurgical reactor after the metallurgical melt is charged into the metallurgical reactor, melt physical property parameters, first process parameters and physical field monitoring data;

[0117] The data set construction module 220 is used to use the first image data and N of the first geometric parameters a The heat data is used to construct a data set a; according to the second image data, the second geometric parameters and the melt physical property parameters N a Heat data to build data set b; N a The maximum value N of the smelting furnaces in the current age of the metallurgical reactor max one third of

[0118] The first model training module 230 is used to construct a model based on the data set a using the 3DGS algorithm to obtain an image parameter recognition model A; and to construct a model based on the data set b based on OpenCV to obtain a clearance height prediction model B;

[0119] A topology scheme building module 240 is used to calculate the melt headroom, the topology scheme of the reactor geometry, the reactor geometry model and the structured grid of the reactor geometry model based on the image parameter recognition model A and the headroom prediction model B according to the first image data and the second image data;

[0120] The multi-physics simulation module 250 is used to perform simulation using CFD software based on the reactor geometry model, the structured grid and the preset metallurgical industry specifications, according to the melt property parameters, the first process parameters and the physical field monitoring data, to obtain multi-physics simulation data;

[0121] The second model training module 260 is used to construct N according to the multi-physics field simulation data, the reactor geometry model, the melt physical property parameters, the melt clearance height and the first process parameters. a The dataset c and N of the group data max A data set d of group data; using the data set c, the first deep learning prediction model is trained to obtain a multi-physics field prediction model C;

[0122] The third model training module 270 is used to input the data set d into the multi-physics field prediction model C for data prediction to obtain multi-physics field prediction data; construct a data set e according to the multi-physics field prediction data, the reactor geometry model, the melt physical property parameters, the melt clearance height and the first process parameter; use the data set e to train the second deep learning prediction model to obtain the process parameter optimization model D;

[0123] The metallurgical reactor control module 280 is used to obtain, through monitoring equipment, the third image data of the current heat of metallurgical melt before it is loaded into the metallurgical reactor, the fourth image data after the metallurgical melt is loaded into the metallurgical reactor, the physical field data of the current heat, and the target physical field data; based on a preset physical field threshold range, according to the third image data of the current heat of metallurgical melt before it is loaded into the metallurgical reactor, the fourth image data after the metallurgical melt is loaded into the metallurgical reactor, the melt physical property parameters, the physical field data of the current heat, and the target physical field data, the reactor process parameters are controlled through the image parameter recognition model A, the clearance height prediction model B, the multi-physical field prediction model C, and the process parameter optimization model D to obtain the second process parameters; and the metallurgical reactor is controlled according to the second process parameters.

[0124] Among them, N max It is the maximum value of smelting furnace N in the current age of the metallurgical reactor.

[0125] Among them, N a The maximum value N of the smelting furnaces in the current age of the metallurgical reactor max one third of.

[0126] Optionally, the topology solution building module 240 is further configured to:

[0127] Inputting the first image data into the image parameter recognition model A to generate parameters, thereby obtaining third geometric parameters;

[0128] Inputting the second image data into the headroom height prediction model B to predict the melt headroom height, and obtaining the melt headroom height;

[0129] A topological scheme for constructing a metallurgical reactor geometric model according to a third geometric parameter and a melt clearance height;

[0130] Based on Python and OpenFOAM, the mesh parameterization code of the metallurgical reactor geometry was written according to the topological scheme to obtain the pre-processing program;

[0131] The third geometric parameter is input into a pre-processing program to generate a mesh, thereby obtaining a reactor geometric model and a structured mesh of the reactor geometric model.

[0132] Optionally, the multi-physics simulation module 250 is further configured to:

[0133] Based on the preset metallurgical industry specifications, determine the operating range according to the first process parameters, melt physical property parameters and physical field monitoring data; formulate a simulation plan according to the gradient based on the operating range;

[0134] Based on the simulation scheme, according to the reactor geometric model and structured grid, CFD software is used to perform simulation to obtain multi-physics field simulation data.

[0135] Among them, the model structure of the first deep learning prediction model and the second deep learning prediction model is a convolutional neural network, a long short-term memory neural network or a bidirectional gated recurrent neural network.

[0136] Optionally, the metallurgical reactor control module 280 is further configured to:

[0137] Input the third image data into the image parameter recognition model A to obtain the current furnace geometric parameters; perform data processing through a pre-processing program according to the current furnace geometric parameters to obtain the current reactor geometric model;

[0138] Inputting the fourth image data into the headroom height prediction model B to obtain the headroom height of the melt of the current furnace;

[0139] Input the current reactor geometry model, the current furnace melt clearance height, the current furnace physical field data, the target physical field data and the melt property parameters into the process parameter optimization model D to obtain the predicted process parameters;

[0140] Input the current reactor geometry model, the current furnace melt clearance height, melt physical property parameters and predicted process parameters into the multi-physics field prediction model C to obtain predicted multi-physics field data;

[0141] According to the preset physical field threshold range, the predicted multi-physical field data is verified to obtain the physical field verification result;

[0142] When the physical field verification result is passed, the predicted process parameter is determined as the second process parameter.

[0143] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0144] The present invention proposes an intelligent control method for a metallurgical reactor based on real-time simulation and monitoring, combining image recognition, machine learning and simulation methods, using geometric image recognition technology and parametric automatic modeling and meshing technology, and obtaining a large amount of simulation data; using machine learning algorithms to achieve real-time simulation prediction of metallurgical reactors, and on this basis, completing the dynamic monitoring of multiple physical fields inside the metallurgical reactor; establishing a deep learning optimization prediction model to achieve intelligent control of metallurgical reactors. The present invention can achieve real-time simulation of multiple physical fields in metallurgical reactors and intelligent control of metallurgical reactors, solving the problems of difficulty in acquiring monitoring information of physical fields in metallurgical reactors during production, low simulation efficiency and inability to acquire information in real time, and dependence on experience and difficulty in control of metallurgical reactors. The present invention is an efficient and intelligent control method for metallurgical reactors based on real-time simulation monitoring and capable of accurately reflecting physical field information in reactors.

[0145] Figure 3 is a schematic diagram of the structure of an intelligent control device provided by an embodiment of the present invention, such as Figure 3 As shown, the intelligent control device may include the above Figure 2 The intelligent control device of the metallurgical reactor based on real-time simulation and monitoring is shown. Optionally, the intelligent control device 310 may include a first processor 2001 .

[0146] Optionally, the intelligent control device 310 may further include a memory 2002 and a transceiver 2003 .

[0147] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0148] Combine the following Figure 3 The components of the intelligent control device 310 are specifically introduced as follows:

[0149] The first processor 2001 is the control center of the intelligent control device 310, which can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0150] Optionally, the first processor 2001 can perform various functions of the intelligent control device 310 by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002.

[0151] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 3 CPU0 and CPU1 are shown in FIG.

[0152] In a specific implementation, as an embodiment, the intelligent control device 310 may also include multiple processors, such as Figure 3 The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0153] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0154] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and may be accessed through the interface circuit ( Figure 3 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0155] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0156] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 3 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0157] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently and communicate with the first processor 2001 through the interface circuit ( Figure 3 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0158] It should be noted that Figure 3 The structure of the intelligent control device 310 shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0159] In addition, the technical effects of the intelligent control device 310 can refer to the technical effects of the intelligent control method of the metallurgical reactor based on real-time simulation and monitoring described in the above method embodiment, which will not be repeated here.

[0160] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0161] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0162] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0163] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0164] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0165] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0166] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0167] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0168] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0169] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0170] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0171] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0172] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent control method for a metallurgical reactor based on real-time simulation and monitoring, characterized in that: The method comprises: During the smelting process, the metallurgical reactor is monitored by monitoring equipment. max Heat data collection, obtaining first image data before the metallurgical melt is charged into the metallurgical reactor, first geometric parameters of the metallurgical reactor before the metallurgical melt is charged into the metallurgical reactor, second image data after the metallurgical melt is charged into the metallurgical reactor, second geometric parameters of the metallurgical reactor after the metallurgical melt is charged into the metallurgical reactor, melt physical property parameters, first process parameters and physical field monitoring data; Using the first image data and N of the first geometric parameters a The heat data constructs a data set a; according to the second image data, the second geometric parameters and N of the melt physical property parameters a The furnace data constructs data set b; According to the data set a, a model is constructed using the 3DGS algorithm to obtain an image parameter recognition model A; based on OpenCV, a model is constructed according to the data set b to obtain a clearance height prediction model B; Based on the image parameter recognition model A and the headroom prediction model B, calculation is performed according to the first image data and the second image data to obtain the melt headroom, the topological scheme of the reactor geometry, the reactor geometry model and the structured grid of the reactor geometry model; Based on the reactor geometry model, the structured grid and the preset metallurgical industry specifications, according to the melt physical property parameters, the first process parameters and the physical field monitoring data, CFD software is used to perform simulation to obtain multi-physical field simulation data; Construct N according to the multi-physics field simulation data, the reactor geometry model, the melt physical property parameters, the melt clearance height and the first process parameters. a The dataset c and N of the group data max A data set d of group data; using the data set c, training a first deep learning prediction model to obtain a multi-physics field prediction model C; Inputting the data set d into the multi-physics field prediction model C for data prediction to obtain multi-physics field prediction data; constructing a data set e according to the multi-physics field prediction data, the reactor geometry model, the melt physical property parameters, the melt clearance height and the first process parameter; using the data set e, training the second deep learning prediction model to obtain a process parameter optimization model D; Through the monitoring equipment, the third image data of the current heat of metallurgical melt before being loaded into the metallurgical reactor, the fourth image data after the metallurgical melt is loaded into the metallurgical reactor, the physical field data of the current heat and the target physical field data are obtained; based on the preset physical field threshold range, according to the third image data of the current heat of metallurgical melt before being loaded into the metallurgical reactor, the fourth image data after the metallurgical melt is loaded into the metallurgical reactor, the melt physical property parameters, the physical field data of the current heat and the target physical field data, the reactor process parameters are controlled through the image parameter recognition model A, the clearance height prediction model B, the multi-physical field prediction model C and the process parameter optimization model D to obtain the second process parameters; the metallurgical reactor is controlled according to the second process parameters.

2. The intelligent control method of a metallurgical reactor based on real-time simulation and monitoring according to claim 1, characterized in that: The N max It is the maximum value of smelting furnace N in the current age of the metallurgical reactor.

3. The intelligent control method of a metallurgical reactor based on real-time simulation and monitoring according to claim 1, characterized in that: The N a The maximum value N of the smelting furnaces in the current age of the metallurgical reactor max one third of.

4. The intelligent control method of a metallurgical reactor based on real-time simulation and monitoring according to claim 1, characterized in that: The method based on the image parameter recognition model A and the headroom prediction model B calculates according to the first image data and the second image data to obtain the melt headroom, the topological scheme of the reactor geometry, the reactor geometry model and the structured grid of the reactor geometry model, including: Inputting the first image data into the image parameter recognition model A to generate parameters and obtain third geometric parameters; Inputting the second image data into the headroom height prediction model B to predict the melt headroom height, and obtaining the melt headroom height; Constructing a metallurgical reactor geometry model and a topological scheme of a structured grid of the reactor geometry model according to the third geometry parameter and the melt clearance height; Based on Python and OpenFOAM, a mesh parameterization code is written for the metallurgical reactor geometry according to the topological scheme to obtain a pre-processing program; The third geometric parameter is input into the pre-processing program to generate a mesh, thereby obtaining a reactor geometric model and a structured mesh of the reactor geometric model.

5. The intelligent control method of metallurgical reactor based on real-time simulation and monitoring according to claim 1, characterized in that: The method comprises: performing simulation based on the reactor geometry model, the structured grid and the preset metallurgical industry specifications, according to the melt physical property parameters, the first process parameters and the physical field monitoring data, using CFD software to obtain multi-physical field simulation data, including: Based on the preset metallurgical industry specifications, the operating range is determined according to the first process parameters, the melt physical property parameters and the physical field monitoring data; and a simulation plan is formulated according to the gradient according to the operating range; Based on the simulation scheme, according to the reactor geometric model and the structured grid, CFD software is used to perform simulation to obtain multi-physics field simulation data.

6. The intelligent control method of metallurgical reactor based on real-time simulation and monitoring according to claim 1 is characterized in that: The model structures of the first deep learning prediction model and the second deep learning prediction model include convolutional neural networks, long short-term memory neural networks, or bidirectional gated recurrent neural networks.

7. The intelligent control method of metallurgical reactor based on real-time simulation and monitoring according to claim 1, characterized in that: Based on the preset physical field threshold range, according to the third image data before the metallurgical melt of the current heat is loaded into the metallurgical reactor, the fourth image data after the metallurgical melt is loaded into the metallurgical reactor, the melt physical property parameters, the current heat physical field data and the target physical field data, the reactor process parameter is regulated by the image parameter recognition model A, the clearance height prediction model B, the multi-physical field prediction model C and the process parameter optimization model D to obtain the second process parameter, including: Inputting the third image data into the image parameter recognition model A to obtain the current furnace geometric parameters; performing data processing through the pre-processing program according to the current furnace geometric parameters to obtain the current reactor geometric model; Inputting the fourth image data into the headroom height prediction model B to obtain the headroom height of the melt of the current furnace; Inputting the current reactor geometry model, the current furnace melt clearance height, the current furnace physical field data, the target physical field data and the melt physical property parameters into the process parameter optimization model D to obtain predicted process parameters; Inputting the current reactor geometry model, the current furnace melt clearance height, the melt physical property parameters and the predicted process parameters into the multi-physics field prediction model C to obtain predicted multi-physics field data; According to a preset physical field threshold range, the predicted multi-physical field data is verified to obtain a physical field verification result; When the physical field verification result is passed, the predicted process parameter is determined as the second process parameter.

8. An intelligent control device for a metallurgical reactor based on real-time simulation and monitoring, the intelligent control device for a metallurgical reactor based on real-time simulation and monitoring is used to implement the intelligent control method for a metallurgical reactor based on real-time simulation and monitoring as claimed in any one of claims 1 to 7, characterized in that: The device comprises: Training data acquisition module, used to monitor the metallurgical reactor during the smelting process through monitoring equipment max Heat data collection, obtaining first image data before the metallurgical melt is charged into the metallurgical reactor, first geometric parameters of the metallurgical reactor before the metallurgical melt is charged into the metallurgical reactor, second image data after the metallurgical melt is charged into the metallurgical reactor, second geometric parameters of the metallurgical reactor after the metallurgical melt is charged into the metallurgical reactor, melt physical property parameters, first process parameters and physical field monitoring data; A data set construction module is used to use the first image data and N of the first geometric parameters a The heat data constructs a data set a; according to the second image data, the second geometric parameters and N of the melt physical property parameters a The heat data constructs data set b; the N a The maximum value N of the smelting furnaces in the current age of the metallurgical reactor max one third of The first model training module is used to construct a model based on the data set a using the 3DGS algorithm to obtain an image parameter recognition model A; based on OpenCV, construct a model based on the data set b to obtain a clearance height prediction model B; A topology scheme building module, for calculating, based on the image parameter recognition model A and the headroom prediction model B, the melt headroom, a topology scheme of the reactor geometry, a reactor geometry model, and a structured grid of the reactor geometry model according to the first image data and the second image data; A multi-physics field simulation module, for performing simulation using CFD software based on the reactor geometry model, the structured grid and preset metallurgical industry specifications, according to the melt physical property parameters, the first process parameters and the physical field monitoring data, to obtain multi-physics field simulation data; The second model training module is used to construct N according to the multi-physics field simulation data, the reactor geometry model, the melt physical property parameters, the melt clearance height and the first process parameters. a The dataset c and N of the group data max A data set d of group data; using the data set c, training the first deep learning prediction model to obtain a multi-physics field prediction model C; The third model training module is used to input the data set d into the multi-physics field prediction model C for data prediction to obtain multi-physics field prediction data; construct a data set e according to the multi-physics field prediction data, the reactor geometry model, the melt physical property parameters, the melt clearance height and the first process parameter; use the data set e to train the second deep learning prediction model to obtain a process parameter optimization model D; The metallurgical reactor control module is used to obtain, through monitoring equipment, third image data of the current heat of metallurgical melt before it is loaded into the metallurgical reactor, fourth image data after it is loaded into the metallurgical reactor, physical field data of the current heat, and target physical field data; based on a preset physical field threshold range, according to the third image data of the current heat of metallurgical melt before it is loaded into the metallurgical reactor, fourth image data after it is loaded into the metallurgical reactor, physical property parameters of the melt, physical field data of the current heat, and target physical field data, the reactor process parameter is controlled through the image parameter recognition model A, the clearance height prediction model B, the multi-physical field prediction model C, and the process parameter optimization model D to obtain a second process parameter; and the metallurgical reactor is controlled according to the second process parameter.

9. An intelligent control device, characterized in that: The intelligent control device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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