Intelligent operation decision-making method, system and device for flash furnace and medium

Abstract

The invention discloses an intelligent operation decision-making method, system and device for a flash furnace and a medium, and the method comprises the steps: obtaining the smelting process data of the flash furnace, the manual observation information of the smelting process and the inspection and test data of a smelted product, and building a global database to carry out the unified management of the data; a fire observation opening area of the flash furnace, the interior of a flue gas pipeline and the furnace wall of a slag tap are monitored, and furnace condition sensing information is obtained; regulating and controlling loop setting of a reaction zone of the flash furnace according to the smelting process data, the manual observation information, the inspection and test data, the furnace condition sensing information and the first target index; and according to the smelting process data, the manual observation information and the second target index, electrode rising and falling of an electrode area of the flash furnace and the gear of the transformer are regulated and controlled. According to the embodiment of the invention, the working condition of the smelting process of the flash furnace can be monitored and analyzed, and the operation decision is intelligently executed according to the monitoring result. The method can be widely applied to the field of flash furnace smelting.

Description

Technical Field

[0001] This application relates to the field of flash furnace smelting technology, and in particular to a method, system, device and medium for intelligent operation decision-making of flash furnace. Background Technology

[0002] Flash furnaces, as advanced lead smelting equipment, have advantages such as high productivity and low pollution levels.

[0003] During the flash furnace smelting process, workers need to periodically visit the furnace to observe its condition and provide information such as the melt flowability in the reaction tower, the melt flowability in the electric furnace, the lead layer thickness, and the slag layer thickness in the electric furnace zone. Currently, the information identified by these workers can only be posted to WeChat groups and cannot be centrally managed with the flash furnace smelting control system and the inspection and testing system.

[0004] During the smelting process, flash furnaces generate flue dust composed of various oxides, including nitrogen oxides, in the rising flue. Since the flue dust often contains residual metals and other substances, experienced workers can judge the oxygen content and specific operating conditions inside the furnace by the color of the flue dust, thus optimizing circuit settings such as oxygen flow. For example, light gray or light white flue dust usually indicates sufficient oxygen supply and good combustion; while dark gray or black flue dust may indicate insufficient oxygen, requiring an immediate increase in oxygen supply. Due to the high temperature and brightness variations inside the flash furnace, the furnace environment is extremely complex. Workers mainly rely on visual judgment and experience for operation, which increases labor intensity and prevents real-time online monitoring. Furthermore, after prolonged operation, a large amount of lead, copper matte, and other mixtures accumulate around the slag discharge port, causing localized overheating of the furnace shell, which may lead to thinning of the outer shell or even rupture and leakage.

[0005] For flash furnaces in smelters, the settings of circuits such as oxygen flow rate have a significant impact on the final product quality: when oxygen deficiency occurs in the furnace, incomplete oxidation of metals such as lead scrap may occur, thus reducing the production recovery rate; when oxygen enrichment persists, the furnace temperature is too high, which may lead to increased energy consumption. The reaction mechanism of the smelting process is complex, making it difficult to establish a mathematical model, and the causal relationships in the smelting process are unclear. In addition, the composition of slag in smelters is complex (up to more than ten types). When the composition of the furnace charge changes or the proportion of the furnace charge is unstable, the flash furnace will experience problems in multiple aspects, such as metallurgical reactions, molten pool management, and flue gas systems, due to changes in the structure of the materials fed into the furnace, affecting the smooth operation of production.

[0006] Therefore, the problems existing in the current technology still need to be solved and optimized. Summary of the Invention

[0007] The main objective of this application is to propose a method, system, device, and medium for intelligent operation decision-making in a flash furnace, which aims to monitor and analyze the working status of the flash furnace smelting process and intelligently execute operation decisions based on the monitoring results.

[0008] To achieve the above objectives, one aspect of this application proposes a smart operation decision-making method for a flash furnace, the method comprising:

[0009] Acquire data on the smelting process of the flash furnace, information on manual observations of the smelting process, and test data of the smelted products; establish a global database to manage the smelting process data, the manual observation information, and the test data in a unified manner.

[0010] The inspection port area, the inside of the flue gas duct, and the furnace wall at the slag discharge port of the flash furnace are monitored to obtain furnace condition perception information.

[0011] The circuit settings of the reaction zone of the flash furnace are adjusted based on the smelting process data, the manual observation information, the test data, the furnace condition sensing information, and the first target index.

[0012] Based on the smelting process data, the manual observation information, and the second target indicator, the electrode raising and lowering and the transformer speed of the electrode zone of the flash furnace are controlled by a preset electrode control rule.

[0013] In some embodiments, the furnace condition sensing information includes visual indicators of the furnace mouth flame, furnace mouth temperature information, flue gas duct dust color, and slag discharge port furnace wall temperature information. The monitoring of the observation port area, the interior of the flue gas duct, and the slag discharge port furnace wall of the flash furnace to obtain the furnace condition sensing information includes:

[0014] The first sensor group acquires the furnace flame image and furnace temperature information of the viewing area, and determines the furnace flame visual index based on the furnace flame image.

[0015] The dust return image inside the flue gas duct is acquired by the second sensor group, and the color of the dust return in the flue gas duct is determined based on the dust return image;

[0016] The furnace wall temperature information at the slag discharge port is obtained through a third sensor group.

[0017] In some embodiments, the test data includes slag lead content and slag sulfur content, and the flash furnace intelligent operation decision-making method further includes:

[0018] An online forecasting model is established based on historical detection data of the lead content and sulfur content in the slag.

[0019] The predicted values ​​of lead content and sulfur content in the slag are based on the smelting process data and the online forecasting model.

[0020] The online forecasting model is updated based on the smelting process data.

[0021] In some embodiments, the first target index includes a target range for furnace top temperature, a target range for furnace top pressure, a target range for coke filter layer thickness, a target range for lead slag content, and a target range for throughput. The loop settings include setpoints for the furnace charge flow control system, the coke flow control system, the oxygen flow control system, the sulfide flow control system, the given frequency of the vertical shaft furnace dust collector induced draft fan, and the setpoint for the coke flow control system in the electric furnace zone. The adjustment of the loop settings for the reaction zone of the flash furnace based on the smelting process data, the manual observation information, the testing data, the furnace condition sensing information, and the first target index includes:

[0022] The calorific value of the furnace charge, the outlet pressure of the vertical shaft furnace, the nitrogen oxide content, the temperature and pressure at the top of the furnace, and the statistical values ​​of the operating rate are determined based on the smelting process data.

[0023] The observation information of the first furnace pre-work station is determined based on the aforementioned manual observation information;

[0024] The furnace flame color is determined based on the furnace condition sensing information.

[0025] The circuit settings of the reaction zone of the flash furnace are adjusted based on the calorific value of the furnace charge, the outlet pressure of the vertical furnace, the nitrogen oxide content, the furnace top temperature and pressure, the operating rate statistics, the observation information of the first furnace front post worker, the flame color at the furnace mouth, and the first target range.

[0026] In some embodiments, the second target index includes a target range for furnace bottom temperature, a target range for active power, a three-phase current constraint range, and a power factor constraint range. Based on the smelting process data, the manual observation information, and the second target index, the electrode raising and lowering and transformer tap positions of the flash furnace's electrode zone are controlled through preset electrode control rules, including:

[0027] The furnace bottom temperature, active power, three-phase electrode current value, and power factor are determined based on the smelting process data.

[0028] The observation information of the second furnace pre-work station worker is determined based on the aforementioned manual observation information;

[0029] Based on the furnace bottom temperature, the active power, the three-phase electrode current value, the power factor, the observation information of the second furnace front post worker, and the second target index, the electrode lifting and lowering setpoints and the transformer tap setpoints are determined through the preset electrode control rules.

[0030] The electrode lifting and lowering and the transformer speed of the flash furnace electrode zone are adjusted according to the electrode lifting setpoint and the transformer speed setpoint.

[0031] In some embodiments, the flash furnace intelligent operation decision-making method further includes:

[0032] The surface temperature field distribution information of the furnace shell of the furnace wall at the slag discharge port is determined based on the furnace wall temperature information at the slag discharge port.

[0033] Based on the temperature field distribution information on the furnace shell surface, determine whether there is an abnormal temperature condition on the furnace wall at the slag discharge port;

[0034] If there is an abnormal temperature on the furnace wall at the slag discharge port, a safety warning will be issued to the management personnel of the flash furnace.

[0035] In some embodiments, the flash furnace intelligent operation decision-making method further includes:

[0036] The virtual production environment of the flash furnace is constructed using metaverse technology;

[0037] The flash furnace is monitored and interacted with based on the virtual production environment.

[0038] To achieve the above objectives, another aspect of this application proposes a smart operation decision-making system for a flash furnace, the system comprising:

[0039] The furnace condition monitoring module is used to acquire smelting process data of the flash furnace, manual observation information of the smelting process, and test data of the smelted products, and to establish a global database for unified management of the smelting process data, the manual observation information, and the test data.

[0040] The furnace condition sensing module is used to monitor the inspection port area, the inside of the flue gas duct, and the furnace wall at the slag discharge port of the flash furnace, and to obtain furnace condition sensing information.

[0041] The reaction zone control module is used to control the loop settings of the reaction zone of the flash furnace based on the smelting process data, the manual observation information, the test data, the furnace condition sensing information, and the first target index.

[0042] The electrode zone control module is used to control the electrode raising and lowering and transformer speed of the electrode zone of the flash furnace according to the melting process data, the manual observation information and the second target index, through preset electrode control rules.

[0043] To achieve the above objectives, another aspect of this application provides a computer device, comprising:

[0044] At least one processor;

[0045] At least one memory for storing at least one program;

[0046] When the at least one program is executed by the at least one processor, the at least one processor performs the method described above.

[0047] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0048] The embodiments of this application include at least the following beneficial effects: This application provides a method, system, device, and medium for intelligent operation decision-making of a flash furnace. This solution achieves unified collection and management of flash furnace smelting process data, manual observation information of the smelting process, and inspection and testing data of smelted products by establishing a global database, which facilitates the provision of a basis for intelligent operation decision-making of the flash furnace; by observing the flame information at the furnace opening, the inside of the flue gas duct, and the furnace wall at the slag discharge port, it is easy to obtain information on the flame at the furnace opening, the color of the dust returning inside the flue gas duct, and the abnormal temperature of the furnace wall at the slag discharge port, which facilitates the provision of a basis for subsequent intelligent operation decision-making and facilitates the realization of safety early warning; by making intelligent operation decisions for the flash furnace based on the collected data and preset rules, it helps to improve the efficiency and quality of production decision-making. Attached Figure Description

[0049] Figure 1 This is a flowchart of a smart operation decision-making method for a flash furnace provided in an embodiment of this application;

[0050] Figure 2 This is a schematic diagram of an implementation scenario for monitoring the fire-watching area, the interior of the flue gas duct, and the furnace wall at the slag discharge port, as provided in the embodiments of this application.

[0051] Figure 3 This is a schematic diagram of the workflow for monitoring the fire vent area provided in an embodiment of this application;

[0052] Figure 4 This is a schematic diagram of an implementation scenario for monitoring the interior of the flue gas duct and the furnace wall at the slag discharge port, provided in an embodiment of this application.

[0053] Figure 5 This is a schematic diagram of the intelligent operation decision-making process of the reaction zone of the flash furnace provided in the embodiments of this application;

[0054] Figure 6 This is a schematic diagram of the intelligent operation decision-making process of the motor zone of the flash furnace provided in the embodiments of this application;

[0055] Figure 7This is a schematic diagram of the workflow for predicting lead and sulfur content in slag provided in the embodiments of this application;

[0056] Figure 8 This is a schematic diagram of the structure of a flash furnace intelligent operation decision system provided in an embodiment of this application;

[0057] Figure 9 This is a schematic diagram of the hardware structure of the computer device provided in the embodiments of this application. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0059] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0060] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0062] Before providing a detailed description of the embodiments of this application, the background technology involved in the embodiments of this application will be further explained first.

[0063] Production decision-making encompasses comprehensive indicator-based decision-making and operational control. Decisions are often closely related to industry knowledge, and currently, there is no unified decision-making technology; research can only be conducted in conjunction with specific industrial processes. For industrial processes such as petrochemicals, wastewater treatment, and distillation towers, where mathematical models can be established, model-based open-loop setting techniques are commonly used, such as the RTO and RTO+MPC decision-making techniques in the United States. However, production processes in industries like steel, non-ferrous metals, mineral processing, and power generation are often dynamic. Decision indicators are difficult to monitor online, testing cycles are long, and production processes are subject to fluctuations in raw material composition and process interference, resulting in complex operating conditions, unclear mechanisms, and unclear causal relationships. This makes it difficult to establish mathematical models and adopt model-based decision-making techniques. Currently, for specific scenarios, manual open-loop decision-making is used, combined with process experience, making it difficult to achieve closed-loop decision-making. Furthermore, when production stops for maintenance or production conditions change, the manual open-loop decision-making method based on process experience has poor adaptability. Therefore, to achieve optimized control of comprehensive production indicators (quality, output, cost, energy consumption, and material consumption, etc.), closed-loop optimization decision-making is required.

[0064] The metaverse brings together various technologies to create immersive augmented reality experiences, establishing a connection between the real and virtual worlds. The industrial metaverse involves mirroring real machines, factories, buildings, cities, grids, transportation systems, supply chains, and logistics processes in the virtual world to simulate physical systems and rapidly identify, analyze, and solve problems. More importantly, problems can be identified before they arise or become critical. The industrial metaverse integrates information and communication technologies related to industrial processes, creating rich digital twins that combine elements of the real world with meaningful digital data. While the use of technologies such as virtual and augmented reality, digital twins, and artificial intelligence (AI) has opened up new ways to aggregate and display information for industrial decision-making, collaboration, and keeping processes running at maximum efficiency, several key challenges remain to be addressed to realize the vision of the industrial metaverse. Research on the industrial metaverse in conjunction with industrial scenarios has attracted widespread interest from academia and engineering. Despite the enormous potential and broad application prospects of industrial metaverse technology, several factors have recently led to a decline in its popularity. One problem is insufficient practical application value: in practical applications, current industrial metaverse technologies are often used as gimmicks for visual monitoring. For complex production processes, how to combine digital twin technology and artificial intelligence technology to achieve real-time monitoring and optimization still requires further research and exploration. Secondly, the technology is not yet standardized: many manufacturers and equipment require interoperability and data exchange, but currently there are no unified standards and specifications, limiting its further promotion and application. Thirdly, the cost is high: the implementation of industrial metaverse requires significant investment, including hardware upgrades and software system upgrades. For some small and medium-sized enterprises, these costs may be prohibitive, further limiting its application. Fourthly, data security needs further enhancement: industrial metaverse technology involves a large amount of data exchange and sharing, which also brings data security issues. Failure to effectively guarantee data security may lead to serious consequences. While technical standards, cost, and data security issues can be resolved with improvements in hardware and software and the involvement of various organizations and companies, the lack of practical application value necessitates the integration of industrial control technology, optimization technology, and industrial metaverse technology to further explore the practical application value of industrial metaverse and create real economic benefits. Therefore, exploring a practical and feasible multi-technology integrated industrial metaverse implementation plan is highly instructive for the development of industrial metaverse.

[0065] Process computer control and management systems operating in industrial environments primarily employ computer control systems based on PLCs and DCSs. The main functions of DCS-based industrial process control systems are to achieve multi-loop control of industrial processes, logical and sequential control of equipment, and process monitoring. Optimizing computer systems enables industrial process operation optimization and control. High-tech companies, combining different process industries, have developed process control software and operation optimization software based on control software platforms. Computer control systems are a product of the Third Industrial Revolution, essentially achieving automation of operations and informatization of decision-making. Currently, my country has independently developed industrial servers and industrial control computers with independent intellectual property rights, representing a new generation of open industrial internet controllers, used to replace PLC / DCS control systems and edge control. With the development of new-generation information technologies such as industrial artificial intelligence and the industrial internet, the automation technologies of modeling, control, and optimization, new-generation information technologies, and the physical resources of control systems operating in complex industrial processes are closely integrated and coordinated. Research is being conducted on autonomous and controllable integrated industrial intelligent systems for optimization decision-making and control based on new-generation information technologies. This opens up new avenues for solving the challenges of integrated optimization and control of industrial processes and provides support for the development of intelligent platforms for optimization decision-making and control.

[0066] There is limited research on operational decision-making and optimization of flash smelting furnaces, and no related reports have been found to date.

[0067] Figure 1 This is an optional flowchart of a flash furnace intelligent operation decision-making method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0068] S101. Obtain the smelting process data of the flash furnace, the manual observation information of the smelting process, and the test data of the smelted products, and establish a global database to manage the smelting process data, manual observation information, and test data in a unified manner.

[0069] Specifically, during the smelting process in a flash furnace, workers need to periodically go to the furnace to observe the furnace conditions and provide information such as the flowability of the melt in the reaction tower, the flowability of the melt in the electric furnace, the lead layer thickness, and the slag layer thickness in the electric furnace zone. This information is crucial for the operation decisions of the flash furnace. Currently, the information identified by the workers can only be posted in relevant communication groups and cannot be managed in a unified manner with the flash furnace smelting control system and the inspection and testing system.

[0070] Therefore, in order to ensure that the various modules of the flash furnace work in a coordinated manner, a global database needs to be established. In this embodiment, the middleware technology of the Internet of Things system can be used to integrate manually identified furnace condition information with flash furnace smelting process data and inspection and testing data into a unified system, thereby establishing a global database for the flash furnace smelting process. This enables unified collection and management of flash furnace smelting process data, manual observation information of the smelting process, and inspection and testing data of smelted products, providing a basis for intelligent operation decisions of the flash furnace.

[0071] S102. Monitor the inspection port area, the inside of the flue gas duct, and the furnace wall at the slag discharge port of the flash furnace to obtain furnace condition perception information.

[0072] In some embodiments, the furnace condition sensing information includes visual indicators of the furnace mouth flame, furnace mouth temperature information, flue gas duct dust color, and slag discharge port furnace wall temperature information. This information is used to monitor the flash furnace's observation port area, the interior of the flue gas duct, and the slag discharge port furnace wall to acquire furnace condition sensing information, including:

[0073] S1021. Obtain the furnace flame image and furnace temperature information of the viewing area through the first sensor group, and determine the furnace flame visual index based on the furnace flame image.

[0074] S1022. Obtain the dust return image inside the flue gas duct through the second sensor group, and determine the dust return color of the flue gas duct based on the dust return image;

[0075] S1023. Obtain furnace wall temperature information at the slag discharge port through the third sensor group.

[0076] Specifically, for flash furnaces in smelters, the settings of circuits such as oxygen flow rate have a significant impact on the quality of the final product: when oxygen deficiency occurs in the furnace, insufficient oxidation of metals such as lead waste may occur, thereby reducing the production recovery rate; when oxygen enrichment occurs continuously, the furnace temperature is too high, which may lead to increased energy consumption.

[0077] During the smelting process, flash furnaces generate flue dust composed of various oxides, including nitrogen oxides, in the rising flue. Since the flue dust often contains residual metals and other substances, experienced workers can judge the oxygen content and specific operating conditions inside the furnace by the color of the flue dust, thus optimizing circuit settings such as oxygen flow. For example, light gray or light white flue dust usually indicates sufficient oxygen supply and good combustion; while dark gray or black flue dust may indicate insufficient oxygen, requiring an immediate increase in oxygen supply. Due to the high temperature and brightness variations inside the flash furnace, the furnace environment is extremely complex. Workers mainly rely on visual judgment and experience for operation, which increases labor intensity and prevents real-time online monitoring. Furthermore, after prolonged operation, a large amount of lead, copper matte, and other mixtures accumulate around the slag discharge port, causing localized overheating of the furnace shell, potentially leading to thinning of the outer shell or even rupture and leakage. Therefore, it is necessary to measure the furnace shell surface temperature online at the slag discharge port to prevent these phenomena.

[0078] In this embodiment, three sensor groups can be used to detect the fire-viewing area, flue, and furnace wall respectively. For example, the hardware setup related to vision and temperature can be referred to Figure 2 The system is divided into three parts. First, in the observation port area of ​​the reaction tower, four sets of high-temperature resistant visible light cameras equipped with circulating water cooling systems and four online infrared thermometers (i.e., the first sensor group) will be installed to measure the temperature changes inside the furnace online. Second, an endoscopic camera system (i.e., the second sensor group) will be installed inside the rising flue to facilitate online observation and monitoring of information such as the color of dust return images of gas and dust inside the flue. Finally, two sets of visible light-infrared cameras (i.e., the third sensor group) will be installed around the outer walls on both sides of the slag discharge port to detect the surface temperature field of the furnace wall in real time, helping to detect material accumulation conditions in advance and reduce the occurrence of liquid leakage.

[0079] For the fire-viewing area, the furnace temperature and the structure and function of the vision-based fire color recognition system are as follows: Figure 3 As shown, the system consists of four parts: a furnace opening area image and temperature acquisition module, a furnace internal condition perception and recognition module, a data storage and query module, an operating condition recognition visualization interface, and an oxygen flow intelligent control module. The furnace opening area image and temperature acquisition module stores multiple video feeds from the site and preprocesses them for data tracing and querying. The furnace internal condition perception and recognition module analyzes and quantifies key visual indicators, such as flame color and brightness. Simultaneously, it uses advanced AI intelligent recognition algorithms to classify the collected video data of various operating conditions, thereby comprehensively judging different operating conditions. The data storage and query module stores and queries temperature, flame images, extracted visual indicators, and related production variables. The oxygen flow intelligent control module adjusts the oxygen flow rate based on the visual indicators and other relevant variables output by the perception and recognition module.

[0080] To address the lack of real-time online monitoring in the dust return flue area and slag discharge area, visual monitoring hardware (various types of cameras) will be installed in the corresponding areas, and data transmission will be conducted via a PoE switch, such as... Figure 4 As shown, it comprises three parts: image acquisition, data transmission, and sensing and recognition. The image acquisition section, used in flue gas duct areas where high temperatures and significant dust interference are prevalent, employs an advanced industrial-grade endoscopic vision camera system. This system is heat-resistant and features automatic dust backflushing, enabling simultaneous online capture and measurement of smoke, dust, and temperature within the duct. A visible light-infrared camera is installed 5-10 meters outside the furnace wall on both sides of the slag discharge port to capture the temperature field distribution on the furnace wall surface from a wide angle, facilitating timely detection of leaks and other issues. In the data transmission section, a photoelectric conversion PoE switch is installed at each detection point (between the two cameras). The camera is powered via PoE, and video data streams from each point are collected via network cable. The data is then transmitted via fiber optic cable to the photoelectric conversion PoE switch in the central control room, achieving long-distance lossless transmission of video data. In the video monitoring section, a server and related monitoring displays are placed in the main control room. The images from the endoscopic pipes and the infrared images from the furnace walls on both sides are transmitted to the main control room, allowing operators to observe and detect the color of the dust return and the temperature at the slag discharge port in a timely manner, and take appropriate action.

[0081] S103. Adjust the circuit settings of the reaction zone of the flash furnace based on smelting process data, manual observation information, inspection and testing data, furnace condition perception information and the first target index.

[0082] In some embodiments, the first target indicators include target ranges for furnace top temperature, furnace top pressure, coke filter layer thickness, lead slag content, and throughput. The loop settings include setpoints for the burden flow control system, coke flow control system, oxygen flow control system, sulfide flow control system, vertical shaft furnace dust collector induced draft fan frequency, and electric furnace zone coke flow control system. The loop settings of the flash furnace reaction zone are adjusted based on smelting process data, manual observation information, testing data, furnace condition sensing information, and the first target indicators, including:

[0083] S1031. Determine the calorific value of the furnace charge, the outlet pressure of the vertical furnace, the nitrogen oxide content, the furnace top temperature and pressure, and the statistical value of the operating rate based on the smelting process data.

[0084] S1032. Determine the observation information of the first furnace pre-work station based on manual observation information;

[0085] S1033. Determine the flame color at the furnace opening based on furnace condition sensing information;

[0086] S1034. The circuit settings of the reaction zone of the flash furnace are adjusted based on the calorific value of the furnace charge, the outlet pressure of the vertical furnace, the nitrogen oxide content, the furnace top temperature and pressure, the operating rate statistics, the observation information of the first furnace front post, the flame color at the furnace mouth, and the first target interval.

[0087] Specifically, by setting the control system values ​​for the furnace charge flow rate, coke flow rate, oxygen flow rate, sulfide flow rate, coke flow rate, and the frequency of the dust collector and induced draft fan in the flash furnace reaction zone, the furnace top temperature, furnace top pressure, coke filter layer thickness, lead slag content, and throughput in the reaction zone are controlled within the target range to ensure the normal progress of the oxidation-reduction reaction in the reaction zone.

[0088] Reference Figure 5 To address the varying process requirements of different production indicators in the flash furnace reaction zone, the following flow rates are implemented: Charge flow rate ensures production throughput meets targets and prevents excessively high furnace top temperatures; coke flow rate controls the coke layer thickness within the target range to guarantee the reduction reaction and reduce lead slag content; oxygen flow rate adjusts the oxygen content and reaction temperature in the reaction zone to ensure a suitable environment for the oxidation reaction; sulfide flow rate increases the furnace top and reaction temperatures under stable production and normal load conditions in subsequent processes; the frequency of the induced draft fan in the vertical furnace dust collector ensures the vertical furnace in the reaction zone is under negative pressure to improve dust removal efficiency; and the coke flow rate in the electric arc furnace zone ensures the continued reduction reaction in the electric arc furnace zone. This system automatically adjusts the setpoints of each loop in real time based on production data and indicators. It also incorporates a human-machine interaction mechanism to achieve self-optimization and self-learning of the decision-making process, enabling it to adapt to different operating conditions.

[0089] S104. Based on smelting process data, manual observation information, and the second target indicator, the electrode raising and lowering and transformer speed of the flash furnace electrode zone are controlled by preset electrode control rules.

[0090] In some embodiments, the second target indicators include a target range for furnace bottom temperature, a target range for active power, a three-phase current constraint range, and a power factor constraint range. Based on smelting process data, manual observation information, and the second target indicators, the electrode raising and lowering and transformer tap positions of the flash furnace's electrode zone are controlled through preset electrode control rules, including:

[0091] S1041. Determine the furnace bottom temperature, active power, three-phase electrode current value, and power factor based on the smelting process data;

[0092] S1042. Determine the observation information of the second furnace pre-work station based on manual observation information;

[0093] S1043. Based on the furnace bottom temperature, active power, three-phase electrode current value, power factor, observation information of the second furnace front post worker, and the second target index, determine the electrode lifting and lowering setpoint and the transformer tap setpoint through the preset electrode control rules.

[0094] S1044. Adjust the electrode lifting and transformer speed of the electrode zone of the flash furnace according to the electrode lifting setpoint and the transformer speed setpoint.

[0095] Specifically, by deciding the raising and lowering of each electrode in the three-phase electrode of the flash furnace's electric heating zone and the transformer's tap, the active power and the furnace bottom temperature of the electric heating zone are controlled within the target range, provided that the power factor and current value are within the constraints, so as to ensure the fluidity of the lead layer and slag layer in the furnace.

[0096] Reference Figure 6 To address the different process requirements of the three-phase electrodes in the electric heating zone of the flash furnace, electrode #1 is primarily located above the slag layer to ensure smooth slag flow from the reaction zone to the furnace zone and prevent slag buildup in the reaction zone. Electrode #2 is the main regulating electrode, inserted into the copper matte layer to form the electrode heating zone, heating the lead layer and ensuring its fluidity for smooth discharge. Two key considerations are ensuring sufficient power and maintaining the sensor temperature below the lead layer: lower temperatures require deeper insertion and slightly increased power, while higher temperatures require slightly lower power. Electrode #3 is also located above the slag layer to heat it, ensuring its fluidity and smooth slag discharge.

[0097] The goal of the flash furnace electric arc furnace area is to control the furnace bottom temperature within a certain range. This is achieved by controlling the furnace bottom temperature based on the desired power output of the electric arc furnace area. The active power of the flash furnace electric arc furnace area is tracked against this desired power output. Constraints include the active power target range, the three-phase current constraint range, and the power factor constraint range. Combined with electrode control rules and decision-making algorithms summarized from field experience, active power is controlled over a large range through transformer tap settings and within a small range through electrode pressure adjustment.

[0098] In some embodiments, the test data includes slag lead content and slag sulfur content, and the flash furnace intelligent operation decision-making method further includes:

[0099] S201. Establish an online forecasting model based on historical monitoring data of lead and sulfur content in slag.

[0100] S202. Predicted values ​​of lead content and sulfur content in slag based on smelting process data and online forecasting models;

[0101] S203. Update the online forecast model based on the smelting process data.

[0102] Specifically, the slag composition is analyzed manually on-site. Laboratory personnel take samples at the slag discharge port every two hours, and the composition is determined through processes such as cake making, drying, and analysis to obtain the percentage of various components in the slag, with particular attention paid to lead and sulfur content. Operators adjust the industrial production process based on the lead and sulfur content. For example, if the lead content is too high, it indicates potential over-oxygenation and a thin coke filter layer in the flash furnace. Operators will consider specific operating conditions, such as temperature and coke layer thickness, to determine whether to reduce oxygen or add coke. If the sulfur content is too high, it indicates oxygen deficiency in the flash furnace, and operators will decide whether to add oxygen based on the specific operating conditions. However, the lead and sulfur testing cycle is only two hours. Currently, real-time detection of lead and sulfur content is not available on-site, making it difficult for operators to adjust the industrial control process in real time. Real-time and accurate forecasting of lead and sulfur content is crucial for intelligent control of the industrial site and significantly improves product quality.

[0103] Reference Figure 7 The technical route of the intelligent prediction system for lead and sulfur content in slag is as follows: Figure 7 As shown, the system's functions mainly consist of two parts: online prediction of slag lead and sulfur content, and adaptive correction of the prediction model. The online prediction of slag lead and sulfur content requires real-time acquisition and processing of field data. Through analysis of industrial field control processes, industrial big data analysis, system identification, and deep learning technology, an online prediction model for slag lead and sulfur content is established, providing real-time predictions and a reference for operators to adjust oxygen-to-material ratios, coke filter layer thickness, etc. Since changes in production process conditions and drift in process parameters may lead to a decrease in the accuracy of the prediction model, the intelligent prediction system for slag lead and sulfur needs an adaptive correction function. This system can adaptively train the model structure and parameters based on the latest industrial process data and slag lead and sulfur label data, resulting in a high-precision prediction model adapted to the latest operating conditions. When the original online prediction model no longer meets the required accuracy, the system can self-correct, ensuring real-time high-precision predictions and facilitating intelligent control of the industrial field.

[0104] In some embodiments, the flash furnace intelligent operation decision-making method further includes:

[0105] S301. Determine the surface temperature field distribution information of the furnace shell of the furnace wall at the slag discharge port based on the furnace wall temperature information at the slag discharge port.

[0106] S302. Determine whether there is an abnormal temperature on the furnace wall at the slag discharge port based on the temperature field distribution information on the furnace shell surface.

[0107] S303. If there is an abnormal temperature on the furnace wall at the slag discharge port, issue a safety warning to the flash furnace management personnel.

[0108] Specifically, after prolonged operation, a large amount of a mixture of lead, copper matte, and other substances accumulates around the slag discharge port of the flash furnace, causing localized overheating of the furnace shell. This can lead to thinning of the outer shell or even puncture and leakage. In this embodiment, when an abnormal surface temperature of the furnace shell is detected, a warning message needs to be issued to the operator for immediate handling to prevent the above phenomena from occurring.

[0109] In some embodiments, the flash furnace intelligent operation decision-making method further includes:

[0110] S401. Construct a virtual production environment for the flash reactor using metaverse technology;

[0111] S402. Monitor and interact with the flash furnace based on the virtual production environment.

[0112] Specifically, in the existing flash furnace smelting process, monitoring methods mainly rely on traditional video surveillance and manual inspections, which suffer from problems such as incomplete monitoring, untimely response, and low data processing efficiency. Specific problems include: limited monitoring coverage: current monitoring systems primarily rely on human observation and inspections to monitor furnace conditions, resulting in a limited monitoring range and failing to achieve comprehensive coverage and real-time monitoring of the entire production process. Low data processing efficiency: while communication methods such as WeChat groups enable some data sharing, they lack systematic data processing and analysis capabilities, leading to low data utilization and an inability to provide timely and accurate support for production decisions. Lack of immersive interactive experience: traditional monitoring methods cannot provide an intuitive and immersive interactive experience, making it difficult for operators to comprehensively perceive and understand the production status from multiple dimensions and angles, affecting the efficiency and accuracy of decision-making.

[0113] The metaverse-driven intelligent monitoring system aims to achieve a comprehensive upgrade of production monitoring by introducing metaverse technology. By constructing a virtual-physical integrated production monitoring environment, it improves the real-time performance, comprehensiveness, and accuracy of monitoring, reduces the labor intensity of manual monitoring, and enhances the efficiency and quality of production decision-making.

[0114] By utilizing metaverse technology to construct a virtual flash furnace production environment, comprehensive and multi-faceted monitoring of the production process is achieved. An immersive human-machine interface is developed, enabling operators to perceive the production status as if they were actually there, improving the intuitiveness and accuracy of monitoring. Existing IoT systems are integrated to collect various data from the production site in real time (such as temperature, pressure, and flow rate). Advanced data processing algorithms are employed to clean, analyze, and mine the collected data, extracting valuable information. Based on the processed data, real-time monitoring and early warning of production indicators are achieved. When production indicators deviate from the target range, an alarm mechanism is automatically triggered, reminding operators to take timely measures. Combined with intelligent analysis algorithms, production decision support is provided to operators, including furnace condition assessment and parameter adjustment. The monitoring model and control strategies are continuously optimized to improve the intelligence and adaptability of the monitoring system. The metaverse monitoring platform is seamlessly integrated with existing management, decision-making, control, communication, and power distribution systems to achieve information sharing and collaboration. Through cross-system data interaction and collaborative work, the overall intelligence level and operational efficiency of the production process are improved.

[0115] Please see Figure 8 This application also provides an intelligent operation decision-making system for a flash furnace, which can implement the above-mentioned method. The system includes:

[0116] The furnace condition monitoring module is used to acquire smelting process data of the flash furnace, manual observation information of the smelting process, and test data of the smelted products, and to establish a global database for unified management of smelting process data, manual observation information, and test data.

[0117] The furnace condition sensing module is used to monitor the inspection port area, the inside of the flue gas duct, and the furnace wall at the slag discharge port of the flash furnace to obtain furnace condition sensing information.

[0118] The reaction zone control module is used to control the loop settings of the reaction zone of the flash furnace based on smelting process data, manual observation information, inspection and testing data, furnace condition sensing information, and the first target index.

[0119] The electrode zone control module is used to control the electrode raising and lowering and transformer speed of the flash furnace electrode zone according to the melting process data, manual observation information and the second target index, through preset electrode control rules.

[0120] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0121] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0122] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0123] Please see Figure 9 , Figure 9 A computer apparatus illustrating another embodiment includes:

[0124] At least one processor;

[0125] At least one memory for storing at least one program;

[0126] When at least one program is executed by at least one processor, such that at least one processor implements the method described above.

[0127] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0128] The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 using the methods described in the embodiments of this application.

[0129] The 903 input / output interface is used to implement information input and output.

[0130] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0131] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0132] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0133] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0134] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0135] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0136] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0137] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0138] This application provides a method, system, device, and medium for intelligent operation decision-making in a flash furnace. It establishes a global database to achieve unified collection and management of data from the flash furnace smelting process, manual observation information during the smelting process, and inspection and testing data of the smelted products, facilitating intelligent operation decision-making for the flash furnace. By monitoring the inspection area, the interior of the flue gas duct, and the furnace wall at the slag discharge port, it facilitates the acquisition of flame information at the furnace opening, the color of dust returning from the flue gas duct, and abnormal temperatures on the furnace wall at the slag discharge port, providing a basis for subsequent intelligent operation decisions and enabling safety warnings. By using the collected data and preset rules to make intelligent operation decisions for the flash furnace, it helps improve the efficiency and quality of production decision-making.

[0139] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0140] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0141] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0142] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0143] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0144] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0145] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0146] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0148] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0149] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A smart operation decision-making method for a flash furnace, characterized in that, include: Acquire data on the smelting process of the flash furnace, information on manual observations of the smelting process, and test data of the smelted products; establish a global database to manage the smelting process data, the manual observation information, and the test data in a unified manner. The inspection port area, the inside of the flue gas duct, and the furnace wall at the slag discharge port of the flash furnace are monitored to obtain furnace condition perception information. The circuit settings of the reaction zone of the flash furnace are adjusted based on the smelting process data, the manual observation information, the test data, the furnace condition sensing information, and the first target index. Based on the smelting process data, the manual observation information, and the second target indicator, the electrode raising and lowering and the transformer speed of the electrode zone of the flash furnace are controlled by a preset electrode control rule.

2. The intelligent operation decision-making method for a flash furnace according to claim 1, characterized in that, The furnace condition sensing information includes visual indicators of the furnace mouth flame, furnace mouth temperature information, flue gas duct dust color, and slag discharge port furnace wall temperature information. The monitoring of the flash furnace's observation port area, the interior of the flue gas duct, and the slag discharge port furnace wall to obtain furnace condition sensing information includes: The first sensor group acquires the furnace flame image and furnace temperature information of the viewing area, and determines the furnace flame visual index based on the furnace flame image. The dust return image inside the flue gas duct is acquired by the second sensor group, and the color of the dust return in the flue gas duct is determined based on the dust return image; The furnace wall temperature information at the slag discharge port is obtained through a third sensor group.

3. The intelligent operation decision-making method for a flash furnace according to claim 1, characterized in that, The test data includes slag lead content and slag sulfur content, and the intelligent operation decision-making method for the flash furnace also includes: An online forecasting model is established based on historical detection data of the lead content and sulfur content in the slag. The predicted values ​​of lead content and sulfur content in the slag are based on the smelting process data and the online forecasting model. The online forecasting model is updated based on the smelting process data.

4. The intelligent operation decision-making method for a flash furnace according to claim 1, characterized in that, The first target indicators include the target ranges for furnace top temperature, furnace top pressure, coke filter layer thickness, lead slag content, and throughput. The loop settings include the setpoints for the furnace charge flow control system, coke flow control system, oxygen flow control system, sulfide flow control system, vertical shaft furnace dust collector induced draft fan frequency, and electric furnace area coke flow control system. The adjustment of the loop settings for the flash furnace reaction zone based on the smelting process data, manual observation information, laboratory test data, furnace condition sensing information, and the first target indicators includes: The calorific value of the furnace charge, the outlet pressure of the vertical shaft furnace, the nitrogen oxide content, the temperature and pressure at the top of the furnace, and the statistical values ​​of the operating rate are determined based on the smelting process data. The observation information of the first furnace pre-work station is determined based on the aforementioned manual observation information; The furnace flame color is determined based on the furnace condition sensing information. The circuit settings of the reaction zone of the flash furnace are adjusted based on the calorific value of the furnace charge, the outlet pressure of the vertical furnace, the nitrogen oxide content, the furnace top temperature and pressure, the operating rate statistics, the observation information of the first furnace front post worker, the flame color at the furnace mouth, and the first target range.

5. The intelligent operation decision-making method for a flash furnace according to claim 1, characterized in that, The second target indicators include the target range for furnace bottom temperature, the target range for active power, the three-phase current constraint range, and the power factor constraint range. Based on the smelting process data, the manual observation information, and the second target indicators, the electrode raising and lowering and transformer tap positions of the flash furnace's electrode zone are controlled through preset electrode control rules, including: The furnace bottom temperature, active power, three-phase electrode current value, and power factor are determined based on the smelting process data. The observation information of the second furnace pre-work station worker is determined based on the aforementioned manual observation information; Based on the furnace bottom temperature, the active power, the three-phase electrode current value, the power factor, the observation information of the second furnace front post worker, and the second target index, the electrode lifting and lowering setpoints and the transformer tap setpoints are determined through the preset electrode control rules. The electrode lifting and lowering and the transformer speed of the flash furnace electrode zone are adjusted according to the electrode lifting setpoint and the transformer speed setpoint.

6. The intelligent operation decision-making method for a flash furnace according to claim 2, characterized in that, The intelligent operation decision-making method for the flash furnace also includes: The surface temperature field distribution information of the furnace shell of the furnace wall at the slag discharge port is determined based on the furnace wall temperature information at the slag discharge port. Based on the temperature field distribution information on the furnace shell surface, determine whether there is an abnormal temperature condition on the furnace wall at the slag discharge port; If there is an abnormal temperature on the furnace wall at the slag discharge port, a safety warning will be issued to the management personnel of the flash furnace.

7. A method for intelligent operation decision-making of a flash furnace according to any one of claims 1-6, characterized in that, The intelligent operation decision-making method for the flash furnace also includes: The virtual production environment of the flash furnace is constructed using metaverse technology; The flash furnace is monitored and interacted with based on the virtual production environment.

8. A smart operation decision-making system for a flash furnace, characterized in that, include: The furnace condition monitoring module is used to acquire smelting process data of the flash furnace, manual observation information of the smelting process, and test data of the smelted products, and to establish a global database for unified management of the smelting process data, the manual observation information, and the test data. The furnace condition sensing module is used to monitor the inspection port area, the inside of the flue gas duct, and the furnace wall at the slag discharge port of the flash furnace, and to obtain furnace condition sensing information. The reaction zone control module is used to control the loop settings of the reaction zone of the flash furnace based on the smelting process data, the manual observation information, the test data, the furnace condition sensing information, and the first target index. The electrode zone control module is used to control the electrode raising and lowering and transformer speed of the electrode zone of the flash furnace according to the melting process data, the manual observation information and the second target index, through preset electrode control rules.

9. A computer device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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