A method and system for staggered scheduling of hot blast stoves based on uncertain stochastic programming

Abstract

This invention discloses a method and system for staggered scheduling of hot blast stoves based on uncertain stochastic programming. The method includes constructing business rules based on the distribution of hot blast stoves in the gas pipeline network; considering the furnace changeover time and interval of each blast furnace in the business rules, establishing an uncertain stochastic mathematical model: the model objective is to calculate the cumulative sum of the waiting times for a furnace changeover of a certain blast furnace, minimizing the cumulative sum while satisfying constraints; the decision variable is the waiting time for a furnace changeover of a certain blast furnace; the uncertain stochastic model is transformed into a deterministic equivalent form and linearized; the model is solved to obtain the furnace changeover time and furnace changeover operation sequence combination given in the current business rules for each hot blast stove with a blast furnace. Employee experience is quantified into different uncertain variables and their corresponding uncertain distributions. This effectively addresses the uncertainties caused by equipment aging and unstable furnace conditions, avoiding gas venting due to large fluctuations in pipeline pressure. It improves the stability and efficiency of hot blast stove scheduling.

Description

Technical Field

[0001] This application relates to the field of hot blast stove state switching technology, and more specifically, to a hot blast stove scheduling and peak-shaving method and system based on uncertain stochastic programming. Background Technology

[0002] Hot blast stoves are crucial components in blast furnace ironmaking processes. Their primary function is to burn blast furnace gas to heat air, creating hot blast that is then introduced into the blast furnace for ironmaking. Staggered hot blast stove operation is a highly efficient technique. Its core objective is to ensure a continuous and stable supply of high-temperature hot blast to the blast furnace while minimizing gas pipeline pressure fluctuations caused by hot blast stove switching operations. This, in turn, reduces waste and environmental pollution resulting from gas venting due to these pressure fluctuations.

[0003] Different steel companies choose different methods to implement staggered hot blast stove replacement functionality. A widely used method is for different blast furnace dispatch rooms to coordinate replacement times via telephone or instant messaging software. In this scenario, staggered hot blast stove replacement relies entirely on manual operation and communication, depending on the operator's experience and understanding of the blast furnace conditions. This staggered replacement method is detrimental to the stable operation of the enterprise because if an experienced employee leaves, staggered replacement may be difficult to achieve for a period of time, leading to large fluctuations in furnace conditions and pipeline pressure, which greatly impacts production and blast furnace gas reuse.

[0004] Some steel companies summarize their furnace changing business rules, hot blast stove operation manuals, and historical data (such as the shortest and longest furnace firing times, and the required flue gas temperature during furnace changing) into complex furnace changing process logic. This logic is then programmed to automate the furnace changing process, while retaining staff to handle unforeseen emergencies in the actual production environment. However, with aging equipment and increasingly unstable blast furnace conditions, the previously used business rules and operation manuals may become ineffective in certain aspects. For example, a particular hot blast stove in a blast furnace may age faster, requiring a longer firing time to bring the hot blast to a usable temperature. In this case, relying solely on fixed business rules and furnace changing operation manuals cannot guarantee the effectiveness of staggered furnace changing. One solution is to incorporate the practical experience of the furnace changing staff. However, this raises the question of how to integrate staff experience into the staggered furnace changing rules or functions. Summary of the Invention

[0005] The purpose of this application is to provide a hot blast stove scheduling staggered peak method and system based on uncertain stochastic programming, which solves the problem that the previous furnace replacement rules are no longer applicable due to changes in operators, and that the previously used furnace replacement scheduling business rules and operation manuals may cause large fluctuations in pipeline pressure and gas venting due to uncertainties such as equipment aging, unstable furnace conditions, etc., thereby improving the stability and efficiency of hot blast stove scheduling.

[0006] The embodiments of this application are implemented as follows:

[0007] A hot blast stove scheduling and peak-shifting method based on uncertain stochastic programming, characterized by the following steps:

[0008] S1: Construct business rules based on the distribution of blast furnace hot blast stoves in the gas pipeline network;

[0009] S2: Considering the furnace changing time and interval for each blast furnace, establish an uncertain stochastic mathematical model:

[0010] The decision variable is: the waiting time for a specific furnace changeover in a given blast furnace;

[0011] The model objective is to calculate the cumulative sum of the waiting time for a certain furnace changeover in a blast furnace, and to minimize the cumulative sum while satisfying the constraints.

[0012] The constraints are as follows: the absolute value of the difference in waiting time between different blast furnaces must be greater than the staggered blast furnace replacement time of different blast furnaces; the absolute value of the difference in waiting time between different blast furnace replacements in the same blast furnace must be greater than the minimum interval between two blast furnace replacements in the same blast furnace; the sum of the waiting time for a certain blast furnace replacement plus the firing time of the hot blast stove that needs to be fired during the replacement must be within the range of the minimum and maximum firing time of the hot blast stove of that blast furnace.

[0013] S3: Transform the uncertain stochastic model into a deterministic equivalent form and linearize it. Solve the model to obtain the furnace changing time and furnace changing operation sequence combination of all hot blast stoves in the current business rules.

[0014] In the above technical solution, the step S1 of constructing business rules includes:

[0015] To obtain the number of blast furnaces, blast furnaces located in the same regional pipeline network need to undergo staggered furnace replacement, while blast furnaces located in different regional pipeline networks do not need to undergo staggered furnace replacement. Staggered furnace replacement means that if two blast furnaces need to be replaced at a certain time, one of them can only complete the paired furnace replacement before the other begins the paired furnace replacement.

[0016] Obtain the number of hot blast stoves corresponding to each blast furnace and assign them numbers; each furnace change is a paired furnace change. First, the hot blast stove with the longest firing time is switched from firing to air supply. Then, the hot blast stove with the longest air supply time is switched from air supply to firing. There are two paired furnace changes within the minimum furnace change cycle of all hot blast stoves involved in each blast furnace.

[0017] Set the minimum and maximum firing time for the hot blast stove.

[0018] In the above technical solution, there are at least 2-4 blast furnaces in step S1, and each blast furnace includes at least 2-4 hot blast stoves.

[0019] In the above technical solution, the uncertain stochastic mathematical model in step S2 is as follows:

[0020] Objective function: To reduce the adjustment amount while satisfying the constraints:

[0021] The constraints are:

[0022] Different blast furnaces need to be switched at different times: ;

[0023] The same blast furnace cannot be seamlessly connected: ;

[0024] upper and lower limits of furnace firing time constraints: ;

[0025] in, i Blast furnace number; j Furnace replacement number; No. i Blast Furnace No. j The duration of the hot blast stove firing time during the second furnace change is required for the transfer operation. : Initial waiting time for the j-th furnace replacement of the i-th blast furnace; : No. i Maximum firing time for a blast furnace; : No. i Minimum firing time for a blast furnace; Uncertain variable: The minimum interval between two furnace changes in the same blast furnace. Random variable: Furnace switching time, used for peak shaving between different blast furnaces; Decision variable: No. i Blast Furnace No. j Waiting time for the next furnace change; The time slack variable for avoiding seamless transitions during the j-th furnace change; : The slack variable for the staggered peak time of the j-th furnace change.

[0026] In the above technical solution, step S3 adopts the expected value model. Based on probability theory and uncertainty theory, it transforms the uncertain stochastic model into a deterministic equivalent form and linearizes the absolute value constraints. The uncertain distribution of the uncertain variable "the minimum interval time between two furnace changes in the same blast furnace" is used... This indicates that the probability distribution of the random variable "staggered furnace switching time at different blast furnaces" is represented by... express.

[0027] To address the uncertainties caused by equipment aging and unstable furnace conditions during the staggered scheduling and furnace replacement of hot blast stoves, if relevant measurement points and historical data exist for such uncertainties, probability distributions can be used to approximate the frequency of these uncertainties. For example, the furnace firing time can be referenced from historical furnace firing times.

[0028] In the above technical solution, step S3 uses a linear programming solver to solve the problem.

[0029] In the above technical solution, the furnace changing sequence combination obtained in step S3 is such that if a certain hot blast stove is "fired and switched to air supply", then another hot blast stove is "air supply and switched to fire", thus forming a pair of furnace changing with the certain hot blast stove.

[0030] In the above technical solution, in step S3, a scheduling Gantt chart is established based on the combination of the changing time and changing sequence of each hot blast stove. Each hot blast stove, production unit, and power generation unit in the gas pipeline network performs hot blast stove switching operations according to the schedule.

[0031] A hot blast furnace scheduling system, characterized in that it comprises:

[0032] Business rule construction unit: used to extract business rules based on the distribution of blast furnace hot blast stoves in the gas pipeline network, the business rules including:

[0033] To obtain the number of blast furnaces, blast furnaces located in the same regional pipeline network need to undergo staggered furnace replacement, while blast furnaces located in different regional pipeline networks do not need to undergo staggered furnace replacement. Staggered furnace replacement means that if two blast furnaces need to be replaced at a certain time, one of them can only complete the paired furnace replacement before the other begins the paired furnace replacement.

[0034] Obtain the number of hot blast stoves corresponding to each blast furnace and assign them numbers; each furnace change is a paired furnace change. First, the hot blast stove with the longest firing time is switched from firing to air supply. Then, the hot blast stove with the longest air supply time is switched from air supply to firing. There are two paired furnace changes within the minimum furnace change cycle of all hot blast stoves involved in each blast furnace.

[0035] Set the minimum and maximum firing time for the hot blast stove;

[0036] The stochastic model unit is used to establish an uncertain stochastic mathematical model based on the furnace changeover time and interval of each blast furnace. The objective of the stochastic mathematical model is to calculate the sum of the waiting times for a certain furnace changeover of a certain blast furnace, and to minimize the sum while satisfying the constraints. The decision variable is the waiting time for a certain furnace changeover of a certain blast furnace. The constraints are: the absolute value of the difference between the waiting times for different furnace changeovers must be greater than the staggered furnace changeover time of different blast furnaces; the absolute value of the difference between the waiting times for different furnace changeovers in the same blast furnace must be greater than the minimum interval between two furnace changeovers in the same blast furnace; the sum of the waiting time for a certain furnace changeover of a certain blast furnace and the firing time of the hot blast stove that needs to be fired during the furnace changeover must be within the range of the minimum and maximum firing time of the hot blast stove of that blast furnace.

[0037] The model solving and scheduling calculation unit is used to transform uncertain stochastic models into deterministic equivalent forms and linearize them, solve the models, and obtain the furnace changing time and furnace changing operation sequence combination of all hot blast stoves in the current business rules.

[0038] A computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed, it implements the above-described hot blast stove scheduling and peak-shifting method based on uncertain stochastic programming.

[0039] In summary, this invention introduces mathematical modeling techniques to reduce reliance on manual operation and communication. It applies uncertain stochastic programming to address the uncertainties arising from equipment aging and unstable furnace conditions. Quantifying employee experience and incorporating it into the mathematical model improves its practicality and accuracy. This effectively addresses the uncertainties caused by equipment aging and unstable furnace conditions, preventing gas venting due to large fluctuations in pipeline pressure. It also improves the stability and efficiency of hot blast stove scheduling. Specific beneficial effects are as follows.

[0040] Capability to handle uncertainty: Traditional methods rely on operator experience and fixed programming rules, making it difficult to adapt to the uncertainties brought about by equipment aging and unstable furnace conditions. This invention introduces uncertainty theory and quantifies uncertainty through mathematical modeling techniques, enabling the system to adapt to equipment aging and furnace condition changes. Furthermore, it does not rely on individual experience and improves scheduling stability through an adaptive mathematical model.

[0041] Reduced reliance on manual labor: Traditional methods heavily depend on operator experience and offline communication, making them highly dependent on individual expertise. This invention: Through mathematical models and optimization algorithms, it reduces reliance on manual operation and communication, thereby increasing the level of automation.

[0042] Improving pipeline pressure stability: Traditional methods: Due to the lack of effective staggered furnace replacement strategies, pipeline pressure may fluctuate significantly. This invention: By optimizing scheduling, it effectively avoids a sharp increase in gas pipeline pressure caused by simultaneous furnace replacement, thus reducing gas venting.

[0043] Environmental Benefits: Traditional methods: Gas emissions lead to resource waste and environmental pollution. This invention: By reducing gas emissions, resource utilization efficiency is improved, and environmental pollution is reduced.

[0044] Economic Benefits: Traditional methods: Due to gas venting and equipment instability, production efficiency may decrease and maintenance costs may increase. This invention: By improving production stability and reducing resource waste, production costs are reduced, thus increasing economic benefits.

[0045] Flexibility and Adaptability: Traditional methods are poorly adaptable to unexpected situations such as changes in production plans and equipment maintenance. This invention: The mathematical model can be flexibly adjusted to adapt to changes in production plans and equipment maintenance needs.

[0046] Model scalability: Traditional methods have limited scalability and struggle to adapt to new business rules or equipment changes. The mathematical model of this invention is easily extensible, allowing it to incorporate more business rules and consider more uncertainties.

[0047] Decision Support: Traditional decision-making methods lack data support and rely on personal experience, making it difficult to predict and address potential production risks. This invention provides data- and model-based decision support, improving the scientific rigor and accuracy of decision-making. Through uncertain stochastic programming, risks in the production process can be better identified and avoided.

[0048] Digitalization and Intelligentization: Traditional methods and technologies are relatively outdated and cannot meet the demands of modern intelligent steel industry. This invention employs advanced mathematical modeling and optimization techniques, aligning with the development trend of digitalization and intelligentization in the steel industry. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart of a hot blast stove scheduling and peak-shifting method based on uncertain random programming, as described in an embodiment of this application.

[0051] Figure 2 This is a cross-section of the hot blast stove scheduling model of a blast furnace in Embodiment 2 of this application.

[0052] Figure 3 This is a Gantt chart of the hot blast stove schedule for all blast furnaces in Example 2 of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0054] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0055] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0056] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this application is in use. They 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, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this application. In addition, the terms "first," "second," and "third," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0057] Furthermore, terms such as "horizontal," "vertical," and "sag" do not imply that components must be absolutely horizontal or suspended, but rather that they can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal relative to "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0058] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "set up," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0059] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature being directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0060] The features and performance of this application will be further described in detail below with reference to the embodiments.

[0061] Example 1

[0062] This invention designs a mathematical model method for implementing staggered furnace switching in hot blast stoves, taking into account employee experience. For example... Figure 1 As shown, each step will be explained in detail.

[0063] Step 1: Extract Business Rules

[0064] (1) Blast furnace gas is produced by blast furnace ironmaking and is supplied to hot blast stoves, production units, power generation units and other departments through pipelines.

[0065] (2) Based on the distribution of the gas pipeline network, it was found that four large blast furnaces are involved in staggered furnace replacement. Let them be numbered A, B, C, and D. Among them, A and B belong to the same area of ​​the pipeline network, and C and D belong to the same area of ​​the pipeline network. Therefore, A and B need to replace furnaces during staggered periods, C and D need to replace furnaces during staggered periods, while A and C do not need to replace furnaces during staggered periods.

[0066] (3) Each blast furnace has four hot blast stoves, which are numbered 1, 2, 3, and 4. Each stove change is done in pairs. First, the hot blast stove with the longest firing time is switched from firing to blasting, and then the hot blast stove with the longest blasting time is switched from blasting to firing. Each blast furnace has two paired stove changes within the minimum stove change cycle involving all hot blast stoves.

[0067] (4) Taking A and B as an example, staggered furnace replacement means that if both A and B need to replace furnaces at a certain time, only one of them can complete the paired furnace replacement before the other starts the paired furnace replacement.

[0068] (5) Two paired furnace changes cannot be completed seamlessly in the same blast furnace. In order to ensure a stable supply of hot air required for blast furnace ironmaking, a buffer time needs to be reserved for furnace changes and furnace firing.

[0069] (6) Due to process and standard equipment usage considerations, there are minimum and maximum firing time limits for hot blast stoves.

[0070] Step 2: Establish an uncertain stochastic mathematical model

[0071] Symbol / Variable Description:

[0072] i The blast furnace number is used as a known input parameter in this model.

[0073] j The furnace changeover number is used as a known input parameter in this model;

[0074] The firing time of the hot blast stove to be fired during the j-th furnace change of the i-th blast furnace is set for each hot blast stove and used as a known input parameter in this model.

[0075] The initial waiting time for the j-th furnace changeover of the i-th blast furnace; the estimated time for the current furnace changeover is obtained based on the given furnace firing time and the current real-time status; the waiting time for the current furnace changeover is obtained by subtracting the current time from the estimated time for the current furnace changeover.

[0076] : Maximum firing time of the i-th blast furnace; used as a known input parameter in this model;

[0077] : Minimum firing time of the i-th blast furnace; used as a known input parameter in this model;

[0078] Uncertain variable: The minimum interval between two furnace changes in the same blast furnace.

[0079] Random variable, furnace switching time, used for staggered peak hours for different blast furnaces;

[0080] Decision variables: Waiting time for the j-th furnace replacement of the i-th blast furnace;

[0081] The waiting time for the j-th furnace replacement of the i-th blast furnace; ranging from 0 to 30.

[0082] The slack variable for the staggered peak time of the j-th furnace changeover is between 0 and 25.

[0083] The time relaxation variable for avoiding seamless transition during the j-th furnace change is between 0 and 60.

[0084] Model:

[0085] Objective function: To reduce the adjustment amount while satisfying the constraints;

[0086] (1) ;

[0087] In the above formula, since the two variables of the last two terms can be input, the first term is taken as the minimum: that is, the absolute value of the difference between the waiting time of the j-th furnace change and the initial waiting time of the j-th furnace change of the i-th furnace is taken, and the minimum value is taken by iterating through all the hot blast stoves of the blast furnace.

[0088] constraint:

[0089] Different blast furnaces need to be switched at different times.

[0090] ; (2)；

[0091] The same blast furnace cannot be seamlessly connected.

[0092] (3)；

[0093] upper and lower limits of furnace firing time

[0094] (4)；

[0095] Step 3: Transform into a deterministic equivalent form

[0096] A common approach to handling uncertain parameters in mathematical models is to use the expected value model, which can transform the uncertain stochastic model in step two into the following model:

[0097] ;

[0098] Uncertain distribution of uncertain variables The probability distribution of a random variable is represented by... Based on probability theory and uncertainty theory, the above model can be further transformed into the following deterministic equivalent form:

[0099] ;

[0100] Step 4: Model Linearization

[0101] The mathematical model described above is relatively simple and small in scale, and can be solved using a solver. However, due to the presence of absolute value constraints in the model, it needs to be linearized. The linearized result is as follows:

[0102] ;

[0103] in, and All are non-negative integers.

[0104] Step 5: Solving the Model

[0105] Once the linearized model is written into SCIP using a modeling language, the mathematical model can be solved using the SCIP solver.

[0106] Of course, since production plans may change due to human factors, the mathematical model in this invention does not take into account human factors such as planned equipment maintenance.

[0107] Example 2

[0108] Through testing and practical verification, this invention can automatically complete the peak-shaving function of hot blast stove scheduling in most cases, reducing reliance on employee experience and the time cost of offline communication, and helping to stabilize pipeline pressure and reduce blast furnace gas emissions.

[0109] exist Figure 2 and 3 In this embodiment, based on the distribution of the gas pipeline network, four large blast furnaces, A, B, C, and D, are involved in staggered furnace replacement. A and B belong to the same pipeline area, as do C and D. Therefore, A and B require staggered furnace replacement, C and D require staggered furnace replacement, while A and C do not require staggered furnace replacement.

[0110] Each blast furnace has four hot blast stoves, numbered 1, 2, 3, and 4. Each stove changeover is done in pairs. First, the hot blast stove with the longest firing time is switched from firing to blasting, and then the hot blast stove with the longest blasting time is switched from blasting to firing. Each blast furnace has two paired stove changes within the minimum stove changeover cycle involving all hot blast stoves.

[0111] Figure 2 The table lists the start times of the schedules for the four blast furnaces in Blast Furnace A, as well as the furnace switching operations from the current state to the next state. For example, in Blast Furnace A, the No. 2 hot blast stove has the longest firing time. It was switched from firing to blasting at 08:19:00 on November 29, 2024, and the switch ended at 08:36:00 on November 29, 2024, with a switching time of 17 minutes.

[0112] In the second column, the No. 3 hot blast stove, which had the longest air supply time, was switched from air supply to firing. Then, the No. 4 hot blast stove, which had the longest firing time, was switched from firing to air supply. And so on.

[0113] Figure 3The middle section is a Gantt chart showing the status switching schedule for all hot blast stoves of four large blast furnaces (A, B, C, and D) from 16:48 on November 21, 2024 to 00:00 on November 22, 2024. During each switching process, the burning and feeding states overlap at least partially, satisfying the business rule that there are two paired stove switchings within the minimum stove switching cycle for all hot blast stoves involved in each blast furnace.

[0114] The embodiments described above are some, but not all, of the embodiments of this application. The detailed description of the embodiments of this application is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

Claims

1. A hot blast stove scheduling and peak-shaving method based on uncertain stochastic programming, characterized in that, Includes the following steps: S1: Construct business rules based on the distribution of hot blast stoves corresponding to blast furnaces in the gas pipeline network; S2: Considering the furnace changing time and changing interval for each blast furnace in the business rules, establish an uncertain stochastic mathematical model: The decision variables are: the waiting time for a certain blast furnace to change furnace; the time slack variable for the peak shifting of a certain blast furnace to change furnace; and the time slack variable for avoiding seamless connection of a certain blast furnace to change furnace. The model objective is to, while satisfying the constraints, take the absolute value of the difference between the waiting time of a certain blast furnace changeover and the initial waiting time of a certain blast furnace changeover, and then iterate through all the hot blast stoves corresponding to the blast furnaces to find the minimum value. The constraints are as follows: the absolute value of the difference in waiting time between different blast furnaces must be equal to the sum of the staggered blast furnace replacement time of different blast furnaces and the slack variable of the staggered time of a certain blast furnace replacement; the absolute value of the difference in waiting time between different blast furnace replacements in the same blast furnace must be equal to the sum of the uncertain variable of the minimum interval between two blast furnace replacements in the same blast furnace and the time slack variable of avoiding seamless connection of a certain blast furnace replacement; the sum of the waiting time of a certain blast furnace replacement plus the firing time of the hot blast stove that needs to be fired during the replacement must be within the range of the minimum and maximum firing time of the hot blast stove corresponding to that blast furnace. S3: Transform the uncertain random model into a deterministic equivalent form and linearize the absolute value constraints to obtain the uncertain distribution of the uncertain variable "minimum interval time between two furnace changes in the same blast furnace" and the probability distribution of the random variable "staggered furnace change time in different blast furnaces"; solve the model to obtain the furnace change time of each hot blast stove of all blast furnaces in the current business rules and the combination of furnace change operation order. The furnace changing sequence is such that if a certain hot blast stove is switched from "burning" to "blowing", then another hot blast stove is switched from "blowing" to "burning", thus forming a pair of furnace changing pairs with that particular hot blast stove.

2. The hot blast stove scheduling and peak-shifting method based on uncertain stochastic programming according to claim 1, characterized in that, The business rules constructed include: To obtain the number of blast furnaces, blast furnaces located in the same regional pipeline network need to undergo staggered furnace replacement, while blast furnaces located in different regional pipeline networks do not need to undergo staggered furnace replacement. Staggered furnace replacement means that if two blast furnaces need to be replaced at a certain time, one of them can only complete the paired furnace replacement before the other begins the paired furnace replacement. Obtain the number of hot blast stoves corresponding to each blast furnace and assign them numbers; each furnace change is a paired furnace change. First, the hot blast stove with the longest firing time is switched from firing to air supply. Then, the hot blast stove with the longest air supply time is switched from air supply to firing. There are two paired furnace changes within the minimum furnace change cycle of all hot blast stoves involved in each blast furnace. Set the minimum and maximum firing time for the hot blast stove.

3. The hot blast stove scheduling and peak-shifting method based on uncertain stochastic programming according to claim 1, characterized in that, In step S1, there are at least 2-4 blast furnaces, and each blast furnace includes at least 2-4 hot blast stoves.

4. The hot blast stove scheduling and peak-shifting method based on uncertain stochastic programming according to claim 1, characterized in that, The uncertain stochastic mathematical model for step S2 is as follows: objective function To reduce the adjustment amount while satisfying the constraints: The constraints are: Different blast furnaces need to be switched at different times: ; The same blast furnace cannot be seamlessly connected: ; upper and lower limits of furnace firing time constraints: ; in, i Blast furnace number; j Furnace replacement number; No. i Blast Furnace No. j The duration of the hot blast stove firing time during the second furnace change is required for the transfer operation. : No. i Blast Furnace No. j Initial waiting time for the next furnace change; : No. i Maximum firing time for a blast furnace; : No. i Minimum firing time for a blast furnace; Uncertain variable: The minimum interval between two furnace changes in the same blast furnace. Random variable: Furnace switching time, used for peak shaving between different blast furnaces; Decision variable: No. i Blast Furnace No. j Waiting time for the next furnace change; The time slack variable for avoiding seamless transitions during the j-th furnace change; : The slack variable for the staggered peak time of the j-th furnace change.

5. The hot blast stove scheduling and peak-shifting method based on uncertain stochastic programming according to claim 1, characterized in that, Step S3 adopts the expected value model, which, based on probability theory and uncertainty theory, transforms the uncertain stochastic model into a deterministic equivalent form and linearizes the absolute value constraint.

6. The hot blast stove scheduling and peak-shifting method based on uncertain stochastic programming according to claim 1, characterized in that, Step S3 uses a linear programming solver to solve the problem.

7. The hot blast stove scheduling and peak-shifting method based on uncertain stochastic programming according to claim 1, characterized in that, In step S3, a scheduling Gantt chart is established based on the combination of the changing time and changing sequence of each hot blast stove. Each hot blast stove, production unit, and power generation unit in the gas pipeline network performs hot blast stove switching operations according to this schedule.

8. A hot blast stove scheduling system, characterized in that, include: Business rule construction unit: used to extract business rules based on the distribution of hot blast stoves corresponding to blast furnaces in the gas pipeline network, the business rules including: To obtain the number of blast furnaces, blast furnaces located in the same regional pipeline network need to undergo staggered furnace replacement, while blast furnaces located in different regional pipeline networks do not need to undergo staggered furnace replacement. Staggered furnace replacement means that if two blast furnaces need to be replaced at a certain time, one of them can only complete the paired furnace replacement before the other begins the paired furnace replacement. Obtain the number of hot blast stoves corresponding to each blast furnace and assign them numbers; each furnace change is a paired furnace change. First, the hot blast stove with the longest firing time is switched from firing to air supply. Then, the hot blast stove with the longest air supply time is switched from air supply to firing. There are two paired furnace changes within the minimum furnace change cycle of all hot blast stoves involved in each blast furnace. Set the minimum and maximum firing time for the hot blast stove; The stochastic model unit is used to establish an uncertain stochastic mathematical model based on the furnace changeover time and interval of each blast furnace. The objective of the stochastic mathematical model is to calculate the sum of the waiting times for a furnace changeover of a certain blast furnace, and to minimize the sum while satisfying the constraints. The decision variable is the waiting time for a furnace changeover of a certain blast furnace. The constraints are: the absolute value of the difference between the waiting times for different furnace changeovers must be greater than the staggered furnace changeover time of different blast furnaces; the absolute value of the difference between the waiting times for different furnace changeovers in the same blast furnace must be greater than the minimum interval between two furnace changeovers in the same blast furnace; the sum of the waiting time for a furnace changeover of a certain blast furnace and the firing time of the hot blast stove that needs to be fired during the furnace changeover must be within the range of the minimum and maximum firing time of the hot blast stove corresponding to that blast furnace. The model solving and scheduling calculation unit is used to transform the uncertain stochastic model into a deterministic equivalent form and linearize it, solve the model, and obtain the furnace changing time and furnace changing operation sequence combination given in the current business rules for each hot blast stove with a blast furnace. The furnace changing sequence is such that if a certain hot blast stove is switched from "burning" to "blowing", then another hot blast stove is switched from "blowing" to "burning", thus forming a pair of furnace changing pairs with that particular hot blast stove.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the hot blast stove scheduling and peak-shifting method based on uncertain stochastic programming as described in any one of claims 1-7.

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