Iron smelting material proportion monitoring and adjusting system based on AI

Abstract

The invention relates to the technical field of iron smelting material proportioning, in particular to an AI-based iron smelting material proportioning monitoring and adjusting system. Comprising a central control unit used for setting a plurality of monitoring points; the material unit is used for collecting real-time in-furnace material parameters; the monitoring unit comprises a plurality of monitoring sub-modules, the monitoring sub-modules are arranged at all monitoring points, and the monitoring unit is used for collecting operation parameters of the direct-current submerged arc furnace; an AI intelligent agent and a plurality of iron smelting periods are established based on a central control unit, the AI intelligent agent generates an optimal material proportioning strategy according to iron smelting material parameters in a single iron smelting period and sets corresponding feeding parameters according to the material proportioning strategy, uneven mixing of materials is avoided, and meanwhile the real-time feeding amount is monitored, so that the feeding efficiency is improved. And the feeding deviation can be early warned and corrected in time, so that the iron smelting quality is prevented from being influenced by the material ratio.

Description

Technical Field

[0001] The present application relates to the technical field of iron smelting material ratio, and in particular to an AI-based iron smelting material ratio monitoring and adjustment system. Background Art

[0002] The proportion of iron smelting materials is a key link in the iron smelting process, which directly affects the quality and performance of iron. However, at the current stage, the proportion of iron smelting materials is mainly calculated manually, which is prone to errors in proportion calculation, resulting in an imbalance in the material ratio. At the same time, due to fluctuations in the composition of raw materials, the composition of raw materials such as iron ore and coke is unstable, resulting in inaccurate proportions, which further affects the quality of iron smelting.

[0003] At the same time, there are also problems in the iron smelting process such as uneven material mixing affecting the smelting effect, improper use of additives affecting the quality of iron, equipment failure leading to inaccurate ratios or production interruptions, which in turn affect the quality and efficiency of iron smelting. Summary of the Invention

[0004] The purpose of this application is: to solve the above technical problems, this application provides an AI-based iron smelting material ratio monitoring and adjustment system, which aims to improve the ratio accuracy of iron smelting materials and ensure the quality and efficiency of iron smelting.

[0005] In some embodiments of the present application, an AI intelligent body and multiple ironmaking cycles are established based on the central control unit. The AI intelligent body generates an optimal material ratio strategy based on the ironmaking material parameters within a single ironmaking cycle, and sets corresponding feeding parameters according to the material ratio strategy to avoid uneven material mixing. At the same time, by monitoring the real-time feeding amount, timely warning and correction of feeding deviations are carried out to avoid affecting the ironmaking quality due to material ratio.

[0006] In some embodiments of the present application, multiple disturbance indicators are established based on the equipment parameters of the direct current ore furnace. By real-time monitoring of each disturbance indicator, the material ratio and feeding parameters are adjusted in time to avoid the impact of equipment operation fluctuations on the iron smelting quality. At the same time, early warnings are issued for potential operating risks of the equipment in a timely manner to ensure the stability of the iron smelting process, avoid problems such as inaccurate ratios or production interruptions caused by equipment failures, and improve iron smelting efficiency.

[0007] In some embodiments of the present application, an AI-based iron smelting material ratio monitoring and adjustment system is provided, comprising: Central control unit, used to set multiple monitoring points; Material unit, used to collect real-time parameters of materials entering the furnace; A monitoring unit, comprising a plurality of monitoring submodules, wherein the monitoring submodules are arranged at various monitoring points, and the monitoring unit is used to collect operating parameters of the DC ore-fired furnace; The central control unit includes: The first processing module is used to establish an AI agent; The first processing module is also used to establish multiple ironmaking cycles, and the AI intelligent agent is used to set the material sub-strategy of each ironmaking cycle.

[0008] In some embodiments of the present application, the central control unit further includes: The second processing module is used to generate a feeding deviation value according to the real-time parameters of the furnace material, and determine whether to generate a correction instruction according to the feeding deviation value; The third processing module is used to obtain the monitoring data packet of the monitoring unit, and the AI intelligent agent determines whether to generate an adjustment instruction based on the monitoring data packet.

[0009] In some embodiments of the present application, the first processing module is further configured to: Establish a material characteristic index series A, A=(a1,a2…a i …a n ), where a i is the i-th material characteristic index; n is the number of material characteristic indexes; Generate quantitative strategies for each material characteristic indicator, and generate material analysis sub-models based on all quantitative strategies; Establish multiple training data packages based on historical ironmaking parameters, and generate a proportioning sub-model based on the iterative results of all training data packages; Establish an AI agent based on the material analysis sub-model and the proportioning sub-model.

[0010] In some embodiments of the present application, the first processing module is further configured to: Obtain expected material parameters in the current ironmaking cycle; The logistics analysis sub-model generates a material analysis table for the current ironmaking cycle based on expected material parameters; The proportioning sub-model generates the material proportioning strategy for the current ironmaking cycle based on the material analysis table; Set multiple time intervals within the current iron smelting cycle; Establish a time interval sequence T, T=(t1, t2…t i …t m ), where t i is the i-th time interval; m is the number of time intervals; Set the delivery sub-strategy for each time interval based on the material ratio strategy; Set the end time node of each time interval as the feedback time node.

[0011] In some embodiments of the present application, the second processing module is further configured to: Obtain the parameters of the incoming materials collected by the material unit at the current feedback time node; Set the time interval corresponding to the current feedback time node as the target time interval; Obtain feedback data packets and delivery sub-strategies for the target time interval; Generate the feeding deviation value f at the current feedback time node based on the delivery sub-strategy and feedback data packet; Preset material deviation value threshold F1; If f>F1, the current feedback time node generates a correction instruction.

[0012] In some embodiments of the present application, generating a feeding deviation value f at a current feedback time node includes: f=e1*Q1*[ η i *(p i -p' i) 2]+e2*Q2*U; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; is the number of material categories within the target time interval; η i is the impact factor of the i-th material category within the target time interval; p i is the actual delivery quantity of the i-th material category within the target time interval; p' i is the expected delivery quantity of the i-th material category within the target time interval; U is the historical deviation value.

[0013] In some embodiments of the present application, the third processing module is further configured to: Set multiple disturbance indicators based on the historical operating parameters of the DC submerged arc furnace; Establish a disturbance index series B, B=(b1, b2…b i …b r ), where b i is the i-th disturbance index; r is the number of disturbance indexes; Set the first-level operating value of each disturbance indicator; Establish a disturbance sub-model based on all disturbance indicators; The disturbance sub-model generates the disturbance evaluation value of each preset feedback time node in the current ironmaking cycle; Determine whether to generate an adjustment instruction based on the disturbance evaluation value.

[0014] In some embodiments of the present application, determining whether to generate an adjustment instruction based on a disturbance evaluation value includes: Obtaining the monitoring data packet of the monitoring unit at the current feedback time node; Obtain the delivery sub-strategy for the time interval corresponding to the current feedback time node, and generate the secondary operating values of each disturbance indicator at the current feedback time node; Generate the disturbance evaluation value g of the current feedback time node according to the monitoring data packet; g=e3*Q3* β i *(j i -j' 1i) 2 ]+e4*Q4* β i *(j i -j' 2i) 2 ]; Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; r is the number of disturbance indicators; βi is the influencing factor of the i-th disturbance evaluation indicator; j i is the reference value of the i-th disturbance evaluation index generated based on the monitoring data packet of the current feedback time node; j' 1i is the first-level operating value of the i-th disturbance index; j' 2i is the secondary operating value of the i-th disturbance indicator at the current feedback time node; Preset disturbance evaluation value threshold G1; If g>G1, the current feedback time node generates an adjustment instruction.

[0015] In some embodiments of the present application, the central control unit further includes: An early warning module, which is used to obtain the feeding deviation value and disturbance evaluation value of all feedback time nodes in the current ironmaking cycle; Generate a risk assessment value c for the current ironmaking cycle; c= (e5*f i +e6*g i) ; Among them, e5 is the preset fifth weight coefficient; e6 is the preset sixth weight coefficient; m is the number of feedback time nodes in the current ironmaking cycle; f i is the feeding deviation value of the i-th feedback time node in the current ironmaking cycle; g i is the disturbance evaluation value of the i-th feedback time node in the current ironmaking cycle; Preset risk assessment value threshold C1; If c>C1, the early warning module generates an early warning instruction.

[0016] In some embodiments of the present application, the central control unit further includes: The fourth processing module is used to obtain all iron smelting data in the current iron smelting cycle; Generate enhanced data packages based on all iron smelting data; The fourth processing module is further configured to generate update instructions for the AI agent based on the enhanced data packet.

[0017] Compared with the prior art, the AI-based iron smelting material ratio monitoring and adjustment system of the present application embodiment has the following beneficial effects: Based on the central control unit, an AI intelligent body and multiple ironmaking cycles are established. The AI intelligent body generates the optimal material ratio strategy according to the ironmaking material parameters within a single ironmaking cycle, and sets the corresponding feeding parameters according to the material ratio strategy to avoid uneven material mixing. At the same time, by monitoring the real-time feeding amount, it can timely issue early warnings and corrections for feeding deviations to avoid affecting the ironmaking quality due to material ratio.

[0018] Based on the equipment parameters of the DC ore-fired furnace, multiple disturbance indicators are established. By real-time monitoring of each disturbance indicator, the material ratio and feeding parameters are adjusted in a timely manner to avoid the impact of equipment operation fluctuations on ironmaking quality. At the same time, early warnings are issued for potential operating risks of the equipment in a timely manner to ensure the stability of the ironmaking process, avoid problems such as inaccurate ratios or production interruptions caused by equipment failures, and improve ironmaking efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a structural diagram of an AI-based iron smelting material ratio monitoring and adjustment system in the preferred embodiment of the present application. DETAILED DESCRIPTION

[0020] The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0021] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0022] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. Throughout this application, unless otherwise specified, "plurality" means two or more.

[0023] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0024] like Figure 1 As shown, an AI-based iron smelting material ratio monitoring and adjustment system according to a preferred embodiment of the present application includes: Central control unit, used to set multiple monitoring points; Material unit, used to collect real-time parameters of materials entering the furnace; The monitoring unit includes a plurality of monitoring submodules, which are arranged at various monitoring points. The monitoring unit is used to collect the operating parameters of the DC ore-fired furnace; The central control unit includes: The first processing module is used to establish an AI agent; The first processing module is also used to establish multiple ironmaking cycles, and the AI intelligent agent is used to set the material sub-strategy of each ironmaking cycle.

[0025] Specifically, the central control unit also includes: The second processing module is used to generate a feeding deviation value according to the real-time parameters of the furnace material, and determine whether to generate a correction instruction according to the feeding deviation value; The third processing module is used to obtain the monitoring data packet of the monitoring unit, and the AI intelligent agent determines whether to generate an adjustment instruction based on the monitoring data packet. Specifically, iron smelting materials include but are not limited to iron ore (magnetite Fe3O4, hematite Fe2O3, limonite Fe2O3·nH2O, siderite FeCO3, etc.), alloying elements, flux (limestone, dolomite), reducing agents (coke and carbon monoxide), air, etc.

[0026] Specifically, historical ironmaking parameters are collected to generate training data sets. These data sets include the corresponding ironmaking qualities of various materials under different ratio strategies. Machine learning is then used to process these training data sets to establish an input-output mapping. When new raw material parameters are input, the optimal material ratio strategy is generated. For example, inputting parameters such as iron ore type, crushed stone diameter, and impurity content will output the optimal ratio of flux, reducing agent, and alloying elements.

[0027] Specifically, the first processing module is further configured to: Establish a material characteristic index series A, A=(a1,a2…ai …a n ), where a i is the i-th material characteristic index; n is the number of material characteristic indexes; Generate quantitative strategies for each material characteristic indicator, and generate material analysis sub-models based on all quantitative strategies; Establish multiple training data packages based on historical ironmaking parameters, and generate a proportioning sub-model based on the iterative results of all training data packages; Establish an AI agent based on the material analysis sub-model and the proportioning sub-model.

[0028] Specifically, material characteristic indicators include but are not limited to iron ore type, impurity content, impurity type, fixed carbon content of coke, CaO content in limestone and other parameters.

[0029] Specifically, by setting a quantitative strategy for each material characteristic indicator, the real-time material parameters can be described quickly and accurately, which facilitates the analysis and processing of the AI intelligent body and improves the analysis efficiency of the iron smelting material ratio.

[0030] It can be understood that in the above embodiment, an AI intelligent body and multiple ironmaking cycles are established based on the central control unit. The AI intelligent body generates the optimal material ratio strategy according to the ironmaking material parameters within a single ironmaking cycle to improve the accuracy of the ironmaking material ratio.

[0031] In a preferred embodiment of the present application, the first processing module is further configured to: Obtain expected material parameters in the current ironmaking cycle; The logistics analysis sub-model generates a material analysis table for the current ironmaking cycle based on expected material parameters; The proportioning sub-model generates the material proportioning strategy for the current ironmaking cycle based on the material analysis table; Set multiple time intervals within the current iron smelting cycle; Establish a time interval sequence T, T=(t1, t2…t i …t m ), where t i is the i-th time interval; m is the number of time intervals; Set the delivery sub-strategy for each time interval based on the material ratio strategy; Set the end time node of each time interval as the feedback time node.

[0032] Specifically, a single ironmaking cycle is evenly divided into multiple time intervals, and the feeding parameters in each time interval are optimized according to the material ratio strategy set by the AI intelligent body, thereby generating a feeding sub-strategy for each time interval to avoid uneven material mixing.

[0033] Specifically, the second processing module is further configured to: Obtain the parameters of the incoming materials collected by the material unit at the current feedback time node; Set the time interval corresponding to the current feedback time node as the target time interval; Obtain feedback data packets and delivery sub-strategies for the target time interval; Generate the feeding deviation value f at the current feedback time node based on the delivery sub-strategy and feedback data packet; Preset material deviation value threshold F1; If f>F1, the current feedback time node generates a correction instruction.

[0034] Specifically, the greater the feeding deviation, the greater the deviation between the actual amount of material currently entering the furnace and the expected amount corresponding to the material ratio strategy, and the greater the impact on the ironmaking quality.

[0035] Specifically, the material deviation value threshold F1 can be set according to historical data.

[0036] Specifically, the feeding deviation value f at the current feedback time node is generated, including: f=e1*Q1*[ η i *(p i -p' i) 2]+e2*Q2*U; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; is the number of material categories within the target time interval; η i is the impact factor of the i-th material category within the target time interval; p i is the actual delivery quantity of the i-th material category within the target time interval; p' i is the expected delivery quantity of the i-th material category within the target time interval; U is the historical deviation value.

[0037] Specifically, all parameters in the model are normalized by presetting a first fixed coefficient and a second fixed coefficient, so that each parameter in the model is within the same value range.

[0038] Specifically, by traversing the material ratio strategy, all material categories in the current ironmaking cycle are generated, and by real-time monitoring of the parameters of the materials entering the furnace, the actual amount of each material list entering the furnace is generated.

[0039] Specifically, the historical deviation value is generated based on the cumulative value of the feeding deviation values of each past feedback time node in the current ironmaking cycle. The larger the cumulative value, the larger the corresponding historical deviation value.

[0040] It can be understood that in the above embodiment, the corresponding feeding parameters are set according to the material ratio strategy to avoid uneven material mixing. At the same time, by monitoring the real-time feeding amount, the feeding deviation is warned and corrected in time to avoid the impact of material ratio on iron smelting quality.

[0041] In a preferred embodiment of the present application, the third processing module is further configured to: Set multiple disturbance indicators based on the historical operating parameters of the DC submerged arc furnace; Establish a disturbance index series B, B=(b1, b2…b i …b r ), where b i is the i-th disturbance index; r is the number of disturbance indexes; Set the first-level operating value of each disturbance indicator; Establish a disturbance sub-model based on all disturbance indicators; The disturbance sub-model generates the disturbance evaluation value of each preset feedback time node in the current ironmaking cycle; Determine whether to generate an adjustment instruction based on the disturbance evaluation value.

[0042] Specifically, the disturbance indicators include, but are not limited to, boiler operating temperature, slag emission, content of various substances in the slag and other parameters.

[0043] Specifically, a plurality of monitoring points are set according to different disturbance indices, and corresponding data acquisition equipment is set according to the type of disturbance indices to be collected at each monitoring point.

[0044] Specifically, the monitoring submodules are preferably various sensors.

[0045] Specifically, the first-level evaluation value of each disturbance index means that the current disturbance index is in the best state, that is, the ironmaking quality is in the best state.

[0046] Specifically, the larger the disturbance evaluation value is, the greater the deviation between the current boiler's ironmaking state and the expected state is, and the greater the impact on its ironmaking quality is.

[0047] Specifically, when determining whether to generate an adjustment instruction based on the disturbance evaluation value, the following steps are included: Obtaining the monitoring data packet of the monitoring unit at the current feedback time node; Obtain the delivery sub-strategy for the time interval corresponding to the current feedback time node, and generate the secondary operating values of each disturbance indicator at the current feedback time node; Generate the disturbance evaluation value g of the current feedback time node according to the monitoring data packet; g=e3*Q3* β i *(ji -j' 1i) 2 ]+e4*Q4* β i *(j i -j' 2i) 2 ]; Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; r is the number of disturbance indicators; βi is the influencing factor of the i-th disturbance evaluation indicator; j i is the reference value of the i-th disturbance evaluation index generated based on the monitoring data packet of the current feedback time node; j' 1i is the first-level operating value of the i-th disturbance index; j' 2i is the secondary operating value of the i-th disturbance indicator at the current feedback time node; Preset disturbance evaluation value threshold G1; If g>G1, the current feedback time node generates an adjustment instruction.

[0048] Specifically, when the disturbance evaluation value exceeds the preset disturbance evaluation value threshold, the disturbance sub-model obtains the real-time deviation of each disturbance indicator based on the disturbance instruction, and adjusts the real-time material ratio and feeding parameters to avoid the impact of equipment operation fluctuations on iron smelting quality, improve the overall iron smelting quality, and ensure the safe operation of the equipment.

[0049] Specifically, all parameters in the model are normalized by presetting the third fixed coefficient and the fourth fixed coefficient, so that each parameter in the model is within the same value range.

[0050] In a preferred embodiment of the present application, the central control unit further includes: The early warning module is used to obtain the feeding deviation value and disturbance evaluation value of all feedback time nodes in the current ironmaking cycle; Generate a risk assessment value c for the current ironmaking cycle; c= (e5*f i +e6*g i) ; Among them, e5 is the preset fifth weight coefficient; e6 is the preset sixth weight coefficient; m is the number of feedback time nodes in the current ironmaking cycle; f i is the feeding deviation value of the i-th feedback time node in the current ironmaking cycle; g i is the disturbance evaluation value of the i-th feedback time node in the current ironmaking cycle; Preset risk assessment value threshold C1; If c>C1, the early warning module generates an early warning instruction.

[0051] Specifically, the value ranges of the feeding deviation value and the disturbance evaluation value are the same.

[0052] Specifically, the greater the risk assessment value, the greater the possibility that the current DC submerged arc furnace has operational risks. If the real-time risk assessment value is greater than the preset risk assessment value threshold, it means that the current DC submerged arc furnace may have operational failures and needs to be repaired in time to avoid equipment failures that lead to inaccurate proportions or production interruptions, thereby improving ironmaking efficiency.

[0053] In a preferred embodiment of the present application, the central control unit further includes: The fourth processing module is used to obtain all iron smelting data in the current iron smelting cycle; Generate enhanced data packages based on all iron smelting data; The fourth processing module is further used to generate update instructions for the AI agent based on the enhanced data packet.

[0054] Specifically, all data within a single ironmaking cycle are recorded to generate an enhanced data package, which is used to iteratively train the AI agent. By periodically iterating the AI agent, its accuracy in optimizing material ratios is enhanced, thereby improving ironmaking quality and efficiency.

[0055] According to the first concept of this application, an AI intelligent body and multiple ironmaking cycles are established based on the central control unit. The AI intelligent body generates the optimal material ratio strategy according to the ironmaking material parameters within a single ironmaking cycle, and sets the corresponding feeding parameters according to the material ratio strategy to avoid uneven material mixing. At the same time, by monitoring the real-time feeding amount, the feeding deviation is warned and corrected in time to avoid the impact of material ratio on ironmaking quality.

[0056] According to the second concept of the present application, multiple disturbance indicators are established according to the equipment parameters of the DC blast furnace. By real-time monitoring of each disturbance indicator, the material ratio and feeding parameters are adjusted in time to avoid the impact of equipment operation fluctuations on the iron smelting quality. At the same time, early warnings are issued for potential operating risks of the equipment in a timely manner to ensure the stability of the iron smelting process, avoid problems such as inaccurate ratios or production interruptions caused by equipment failures, and improve iron smelting efficiency.

[0057] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and replacements can be made without departing from the technical principles of the present application. These improvements and replacements should also be regarded as the scope of protection of the present application.

Claims

1. An AI-based iron smelting material ratio monitoring and adjustment system, characterized in that: include: Central control unit, used to set multiple monitoring points; Material unit, used to collect real-time parameters of materials entering the furnace; A monitoring unit, comprising a plurality of monitoring submodules, wherein the monitoring submodules are arranged at various monitoring points, and the monitoring unit is used to collect operating parameters of the DC ore-fired furnace; The central control unit includes: The first processing module is used to establish an AI agent; The first processing module is also used to establish multiple ironmaking cycles, and the AI intelligent agent is used to set the material sub-strategy of each ironmaking cycle.

2. The AI-based iron smelting material ratio monitoring and adjustment system according to claim 1 is characterized in that: The central control unit also includes: The second processing module is used to generate a feeding deviation value according to the real-time parameters of the furnace material, and determine whether to generate a correction instruction according to the feeding deviation value; The third processing module is used to obtain the monitoring data packet of the monitoring unit, and the AI intelligent agent determines whether to generate an adjustment instruction based on the monitoring data packet.

3. The AI-based iron smelting material ratio monitoring and adjustment system according to claim 2, characterized in that: The first processing module is further configured to: Establish a material characteristic index series A, A=(a1,a2…a i …a n ), where a i is the i-th material characteristic index; n is the number of material characteristic indexes; Generate quantitative strategies for each material characteristic indicator, and generate material analysis sub-models based on all quantitative strategies; Establish multiple training data packages based on historical ironmaking parameters, and generate a proportioning sub-model based on the iterative results of all training data packages; Establish an AI agent based on the material analysis sub-model and the proportioning sub-model.

4. The AI-based iron smelting material ratio monitoring and adjustment system according to claim 3 is characterized in that: The first processing module is further configured to: Obtain expected material parameters in the current ironmaking cycle; The logistics analysis sub-model generates a material analysis table for the current ironmaking cycle based on expected material parameters; The proportioning sub-model generates the material proportioning strategy for the current ironmaking cycle based on the material analysis table; Set multiple time intervals within the current iron smelting cycle; Establish a time interval sequence T, T=(t1, t2…t i …t m ), where t i is the i-th time interval; m is the number of time intervals; Set the delivery sub-strategy for each time interval based on the material ratio strategy; Set the end time node of each time interval as the feedback time node.

5. The AI-based iron smelting material ratio monitoring and adjustment system according to claim 4 is characterized in that: The second processing module is further configured to: Obtain the parameters of the incoming materials collected by the material unit at the current feedback time node; Set the time interval corresponding to the current feedback time node as the target time interval; Obtain feedback data packets and delivery sub-strategies for the target time interval; Generate the feeding deviation value f at the current feedback time node based on the delivery sub-strategy and feedback data packet; Preset material deviation value threshold F1; If f>F1, the current feedback time node generates a correction instruction.

6. The AI-based iron smelting material ratio monitoring and adjustment system according to claim 5 is characterized in that: Generate the feeding deviation value f at the current feedback time node, including: f=e1*Q1*[ η i *(p i -p' i) 2]+e2*Q2*U; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; is the number of material categories within the target time interval; η i is the impact factor of the i-th material category within the target time interval; p i is the actual delivery quantity of the i-th material category within the target time interval; p' i is the expected delivery quantity of the i-th material category within the target time interval; U is the historical deviation value.

7. The AI-based iron smelting material ratio monitoring and adjustment system according to claim 5, characterized in that: The third processing module is further configured to: Set multiple disturbance indicators based on the historical operating parameters of the DC submerged arc furnace; Establish a disturbance index series B, B=(b1, b2…b i …b r ), where b i is the i-th disturbance index; r is the number of disturbance indexes; Set the first-level operating value of each disturbance indicator; Establish a disturbance sub-model based on all disturbance indicators; The disturbance sub-model generates the disturbance evaluation value of each preset feedback time node in the current ironmaking cycle; Determine whether to generate an adjustment instruction based on the disturbance evaluation value.

8. The AI-based iron smelting material ratio monitoring and adjustment system according to claim 7, characterized in that: When judging whether to generate an adjustment instruction based on the disturbance evaluation value, it includes: Obtaining the monitoring data packet of the monitoring unit at the current feedback time node; Obtain the delivery sub-strategy for the time interval corresponding to the current feedback time node, and generate the secondary operating values of each disturbance indicator at the current feedback time node; Generate the disturbance evaluation value g of the current feedback time node according to the monitoring data packet; g=e3*Q3* β i *(j i -j' 1i) 2 ]+e4*Q4* β i *(j i -j' 2i) 2 ]; Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; r is the number of disturbance indicators; βi is the influencing factor of the i-th disturbance evaluation indicator; j i is the reference value of the i-th disturbance evaluation index generated based on the monitoring data packet of the current feedback time node; j' 1i is the first-level operating value of the i-th disturbance index; j' 2i is the secondary operating value of the i-th disturbance indicator at the current feedback time node; Preset disturbance evaluation value threshold G1; If g>G1, the current feedback time node generates an adjustment instruction.

9. The AI-based iron smelting material ratio monitoring and adjustment system according to claim 8, characterized in that: The central control unit also includes: An early warning module, which is used to obtain the feeding deviation value and disturbance evaluation value of all feedback time nodes in the current ironmaking cycle; Generate a risk assessment value c for the current ironmaking cycle; c= (e5*f i +e6*g i) ; Among them, e5 is the preset fifth weight coefficient; e6 is the preset sixth weight coefficient; m is the number of feedback time nodes in the current ironmaking cycle; f i is the feeding deviation value of the i-th feedback time node in the current ironmaking cycle; g i is the disturbance evaluation value of the i-th feedback time node in the current ironmaking cycle; Preset risk assessment value threshold C1; If c>C1, the early warning module generates an early warning instruction.

10. The AI-based iron smelting material ratio monitoring and adjustment system according to claim 9, characterized in that: The central control unit also includes: The fourth processing module is used to obtain all iron smelting data in the current iron smelting cycle; Generate enhanced data packages based on all iron smelting data; The fourth processing module is further configured to generate update instructions for the AI agent based on the enhanced data packet.

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