Hot blast stove scheduling peak shifting method and system based on uncertain stochastic programming

Abstract

The invention discloses a hot blast stove scheduling peak shifting method and system based on uncertain stochastic programming. The method comprises the steps that a business rule is constructed based on the distribution condition of blast furnace hot blast stoves in a gas pipe network; the furnace changing time and the furnace changing interval of each blast furnace in the business rules are considered, and an uncertain random mathematical model is established: the model target is that the cumulative sum of certain furnace changing waiting time of a certain blast furnace is solved, and the minimum value of the cumulative sum is taken while constraint conditions are met; the decision variable is a certain furnace change waiting time of a certain blast furnace; and converting the uncertain random model into a deterministic equivalence form, linearizing, and solving the model to obtain the furnace changing time and furnace changing operation sequence combination of each hot blast furnace of the blast furnace in the current business rule. The employee experience is quantified into different uncertain variables and corresponding uncertain distributions thereof. The uncertainty caused by equipment aging and unstable furnace conditions is effectively dealt with, and gas diffusion caused by large pressure fluctuation of a pipe network is avoided. Scheduling stability and efficiency of the hot blast stove are improved.

Description

Technical Field

[0001] The present application relates to the technical field of hot blast furnace state switching-related furnace replacement, and in particular to a hot blast furnace scheduling peak shifting method and system based on uncertain random programming. Background Art

[0002] Hot blast furnace is an important component in the blast furnace ironmaking process. Its main function is to burn blast furnace gas to heat the air to form hot blast, and then send the hot blast into the blast furnace to participate in ironmaking. Peak-shifting hot blast furnace replacement is an efficient hot blast furnace operation technology. Its core purpose is to reduce the pressure fluctuation of the gas pipeline network caused by the hot blast furnace replacement operation while ensuring the continuous and stable supply of high-temperature hot blast to the blast furnace, thereby reducing the waste and environmental pollution caused by the gas release caused by the pressure fluctuation of the pipeline network.

[0003] In the process of realizing the staggered furnace replacement function of blast furnace hot blast furnaces, different steel companies will choose different methods. The most widely used method is that different blast furnace dispatching rooms coordinate the furnace replacement time through telephone or instant messaging software. In this scenario, the staggered furnace replacement of hot blast furnaces depends entirely on manual operation and communication, and on the operator's experience and understanding of the blast furnace conditions. This staggered furnace replacement method is not conducive to the stable operation of the enterprise, because if an experienced employee leaves, it may make it difficult to achieve staggered furnace replacement for a period of time, which in turn leads to large changes in furnace conditions and pipeline pressure, which has a great impact on production and blast furnace gas reuse.

[0004] Some steel companies will summarize the furnace change business rules, hot blast furnace operation manuals and historical data (such as the shortest and longest furnace burning time, how many degrees the flue temperature needs to reach when changing furnaces, etc.) to form a complex furnace change process logic, and implement such automatic furnace change logic through programming, while leaving furnace change operation staff to deal with uncertain emergencies in the actual production environment. However, with the aging of equipment and the increasingly unstable blast furnace conditions brought about by it, the business rules and operation manuals used previously may fail in some aspects. For example, a hot blast furnace in a blast furnace ages faster, and it takes a longer burning time to burn the hot air to a usable temperature. At this time, relying solely on fixed business rules and furnace change operation manuals cannot guarantee the effectiveness of staggered furnace change; one solution is to take into account the practical experience of furnace change staff. But the question that follows is how to incorporate employee experience into staggered furnace change rules or functions. Summary of the invention

[0005] The purpose of this application is to provide a hot blast furnace scheduling staggered method and system based on uncertain random planning, to solve the problem that the previous furnace changing rules are not applicable due to changes in operators in hot blast furnace scheduling, and the previously used furnace changing scheduling business rules and operation manuals may cause large fluctuations in pipeline pressure and gas release due to equipment aging, unstable furnace conditions and other uncertainties, so as to improve the stability and efficiency of hot blast furnace scheduling.

[0006] The embodiment of the present application is implemented as follows: A hot blast stove scheduling peak shifting method based on uncertain stochastic programming, characterized by comprising the following steps: S1: Build business rules based on the distribution of blast furnace hot blast stoves in the gas pipeline network; S2: Considering the furnace change time and interval of each blast furnace, an uncertain random mathematical model is established: The decision variables are: the waiting time for a certain blast furnace change; The model objective is to find the cumulative sum of the waiting time for a certain blast furnace change and take the minimum value of the cumulative sum while satisfying the constraints; The constraints are: the absolute value of the difference in waiting time for different blast furnaces must be greater than the staggered blast furnace changing time of different blast furnaces; the absolute value of the difference in waiting time for different blast furnace changes in the same blast furnace must be greater than the minimum interval between two blast furnace changes in the same blast furnace; the sum of the waiting time for a certain blast furnace change plus the burning time of the hot blast furnace that needs to be burned and transferred during the blast furnace change must be within the minimum burning time and maximum burning time of the hot blast furnace of the blast furnace; S3: Convert the uncertain random model into a deterministic equivalent form and linearize it, solve the model, and obtain the hot blast stove replacement time and replacement operation sequence combination of all blast furnaces in the current business rules.

[0007] In the above technical solution, building business rules in step S1 includes: Get the number of blast furnaces. Blast furnaces located in the same regional pipeline network need to be replaced at off-peak times, while blast furnaces located in different regional pipeline networks do not need to be replaced at off-peak times. Off-peak replacement means that if two blast furnaces need to be replaced at a certain moment, the other blast furnace can only start to be replaced after one of them completes the paired replacement. Get the number of hot blast stoves corresponding to each blast furnace and number them; each furnace change is done in pairs. First, set the hot blast stove with the longest furnace burning time to switch from furnace burning to air supply, and then set the hot blast stove with the longest air supply time to switch from air supply to furnace burning. 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 burning time of the hot air furnace.

[0008] 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.

[0009] In the above technical solution, the uncertain random mathematical model in step S2 is as follows: Objective function: , while satisfying the constraints and reducing the adjustment amount: The constraints are: Different blast furnaces need to be replaced at different peaks: ; The same blast furnace cannot be seamlessly connected: ; The upper and lower bounds of furnace burning time are as follows: ; in, i : Blast furnace number; j : Number of furnace changes; No. i Blast furnace No. j The first time the furnace is changed, the burning and transfer operation should be carried out. The burning time of the hot blast furnace; : The initial waiting time for the jth furnace change of the i-th blast furnace; : No. i Maximum firing time of a blast furnace; : No. i Minimum firing time of a blast furnace; : Uncertain variable, the minimum interval between two blast furnace changes in the same blast furnace; : Random variable, furnace change time, used for peak shifting of different blast furnaces; Decision variable: No. i Blast furnace No. j Waiting time for furnace change; : The time slack variable for avoiding seamless connection during the j-th furnace change; : The slack variable of the peak-shifting time of the jth furnace change.

[0010] In the above technical solution, step S3 adopts the expected value model, based on probability theory and uncertainty theory, to transform the uncertain random model into a deterministic equivalent form and linearize the absolute value constraint. The uncertain distribution of the uncertain variable "the minimum interval time between two blast furnace changes in the same blast furnace" is The probability distribution of the random variable "different blast furnace peak replacement time" is expressed as express.

[0011] In view of the uncertainty caused by equipment aging, unstable furnace conditions, etc. in the process of hot blast furnace scheduling and staggered furnace replacement, if there are relevant measurement points and historical data for the uncertain factors, probability distribution can be used to approximate the frequency of the uncertain factors. For example, the furnace burning time can refer to the historical furnace burning time.

[0012] In the above technical solution, step S3 is solved using a linear programming solver.

[0013] In the above technical solution, the furnace changing sequence combination obtained in step S3 is that if a certain hot blast furnace is "converted from furnace burning to air supply", then another hot blast furnace is "converted from air supply to furnace burning", forming a pair of furnace changing with the certain hot blast furnace.

[0014] In the above technical solution, in step S3, a scheduling Gantt chart is established according to the combination of the furnace replacement time and furnace replacement sequence of each hot blast furnace, and each hot blast furnace, production unit, and power generation unit in the gas pipeline network performs hot blast furnace replacement operation according to the schedule.

[0015] A hot blast stove scheduling system, characterized by comprising: Business rule building unit: used to extract business rules based on the distribution of blast furnace hot blast stoves in the gas pipeline network, the business rules include: Get the number of blast furnaces. Blast furnaces located in the same regional pipeline network need to be replaced at off-peak times, while blast furnaces located in different regional pipeline networks do not need to be replaced at off-peak times. Off-peak replacement means that if two blast furnaces need to be replaced at a certain moment, the other blast furnace can only start to be replaced after one of them completes the paired replacement. Get the number of hot blast stoves corresponding to each blast furnace and number them; each furnace change is done in pairs. First, set the hot blast stove with the longest furnace burning time to switch from furnace burning to air supply, and then set the hot blast stove with the longest air supply time to switch from air supply to furnace burning. 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 burning time of the hot air furnace; The random model unit is used to establish an uncertain random mathematical model according to the furnace changing time and furnace changing interval of each blast furnace: the target of the random mathematical model is to calculate the cumulative sum of the waiting time for a furnace changing of a certain blast furnace, and take the minimum value of the cumulative sum while satisfying the constraint conditions; the decision variable is: the waiting time for a furnace changing of a certain blast furnace; the constraint conditions are: the absolute value of the difference in the waiting time for furnace changing of different blast furnaces must be greater than the peak-shifting furnace changing time of different blast furnaces; the absolute value of the difference in the waiting time for different furnace changings in the same blast furnace must be greater than the minimum interval time between two furnace changings of the same blast furnace; the sum of the waiting time for a furnace changing of a certain blast furnace plus the furnace burning time of the hot blast furnace that needs to be burned and transferred during the furnace changing must be within the minimum furnace burning time and the maximum furnace burning time of the hot blast furnace of the blast furnace; The model solving and scheduling calculation unit is used to transform the uncertain random model into a deterministic equivalent form and linearize it, solve the model, and obtain the hot blast stove replacement time and the replacement operation sequence combination of all blast furnaces in the current business rules.

[0016] A computer-readable storage medium stores a computer program, wherein when the computer program is executed, the above-mentioned hot blast stove scheduling peak shifting method based on uncertain random programming is implemented.

[0017] In summary, the present invention introduces mathematical modeling technology to reduce dependence on manual operation and communication. Uncertain stochastic programming is applied to deal with the uncertainty caused by equipment aging and unstable furnace conditions. Employee experience is quantified and incorporated into the mathematical model to improve the practicality and accuracy of the model. It effectively deals with the uncertainty caused by equipment aging and unstable furnace conditions, and avoids gas release caused by large fluctuations in pipeline pressure. It improves the stability and efficiency of hot blast furnace scheduling. The specific beneficial effects are as follows.

[0018] Ability to cope with uncertainty: Traditional methods rely on the operator's experience and fixed programming rules, and are difficult to adapt to the uncertainty caused by equipment aging and unstable furnace conditions. This invention introduces uncertainty theory and quantifies uncertainty through mathematical modeling technology, so that the system can adapt to equipment aging and furnace condition changes. At the same time, it does not rely on personal experience and improves the stability of scheduling through adaptive mathematical models.

[0019] Reduced manual dependence: Traditional methods are highly dependent on the operator's experience and offline communication, and are highly dependent on personal experience. The present invention: Through mathematical models and optimization algorithms, it reduces dependence on manual operation and communication and improves the level of automation.

[0020] Improve the stability of pipeline network pressure: Traditional methods: Due to the lack of effective peak-shifting furnace replacement strategies, pipeline network pressure fluctuations may occur. The present invention: By optimizing the schedule, it effectively avoids the sharp increase in gas pipeline network pressure caused by simultaneous furnace replacement and reduces gas emission.

[0021] Environmental benefits: Traditional methods: gas emission leads to waste of resources and environmental pollution. The present invention: by reducing gas emission, it improves resource utilization efficiency and reduces environmental pollution.

[0022] Economic benefits: Traditional methods: Due to gas emission and equipment instability, production efficiency may be reduced and maintenance costs may increase. The present invention: By improving production stability and reducing resource waste, production costs are reduced and economic benefits are improved.

[0023] Flexibility and adaptability: Traditional methods have poor adaptability to unexpected situations such as changes in production plans and equipment maintenance. The present invention: The mathematical model can be flexibly adjusted to meet the needs of changes in production plans and equipment maintenance.

[0024] Model scalability: Traditional methods: limited scalability, difficult to adapt to new business rules or equipment changes. The present invention: The mathematical model is easy to expand, can incorporate more business rules and consider more uncertain factors.

[0025] Decision support: Traditional methods: The decision-making process lacks data support and relies on personal experience. It is difficult to predict and deal with potential production risks. The present invention: provides decision support based on data and models to improve the scientificity and accuracy of decision-making. Through uncertain stochastic programming, risks in the production process can be better identified and avoided.

[0026] Technological advancement: Traditional methods: The technology is relatively backward and it is difficult to meet the needs of modern steel industry intelligence. The present invention: It adopts advanced mathematical modeling and optimization technology, which is in line with the development trend of digitalization and intelligence of the steel industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 This is a flow chart of a hot blast furnace peak-shifting scheduling method based on uncertain random programming in an embodiment of the present application.

[0029] Figure 2 This is a cross section of a hot blast stove scheduling model for a blast furnace in Example 2 of the present application.

[0030] Figure 3 This is a Gantt chart for scheduling all blast furnace hot blast stoves in Example 2 of the present application. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0032] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0033] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0034] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the application is usually placed when in use. They are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0035] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0036] In the description of this application, it should also be noted that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0037] In the present application, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0038] The features and performance of the present application are further described in detail below in conjunction with the embodiments.

[0039] Example 1 The present invention designs a mathematical model method for realizing hot blast furnace peak shifting that takes employee experience into consideration. Figure 1 As shown, each step will be described in detail.

[0040] Step 1: Extract business rules (1) Blast furnace gas is produced during ironmaking in blast furnaces and supplied to hot blast furnaces, production units, power generation units and other departments through pipelines.

[0041] (2) According to the distribution of the gas pipeline network, it is found that there are four large blast furnaces that require staggered furnace replacement. Assume that they are numbered A, B, C, and D. A and B belong to the pipeline network in the same area, and C and D belong to the pipeline network in the same area. Therefore, A and B need staggered furnace replacement, C and D need staggered furnace replacement, and A and C do not need staggered furnace replacement.

[0042] (3) Each large blast furnace has four hot blast furnaces, which are numbered 1, 2, 3, and 4. Each furnace change is done in pairs. First, the hot blast furnace with the longest burning time switches from burning to blowing air, and then the hot blast furnace with the longest blowing air time switches from blowing air to burning. There are two paired furnace changes in the minimum furnace change cycle involving all hot blast furnaces in each blast furnace.

[0043] (4) Taking A and B as an example, staggered furnace replacement means that if both A and B need to change furnaces at a certain time, the other can only start the paired furnace replacement after one of them completes the paired furnace replacement.

[0044] (5) It is not possible to seamlessly complete two paired furnace changes in the same blast furnace at one time. In order to ensure a stable supply of hot air required for blast furnace ironmaking, a buffer time needs to be left for furnace change and furnace firing.

[0045] (6) Considering the process and the standard use of equipment, there are minimum and maximum burning time limits for hot blast furnaces.

[0046] Step 2: Establish an uncertain random mathematical model Symbol / variable description: i : Blast furnace number, used as a known input parameter in this model; j : The number of furnace changes, used as a known input parameter in this model; The burning time of the hot blast furnace to be burned and transferred during the jth furnace change of the i-th blast furnace; set for each hot blast furnace and used as a known input parameter in this model; The initial waiting time for the jth furnace change of the i-th blast furnace; the estimated time for the current furnace change is obtained according to the given furnace burning time and the current real-time status, and the waiting time for the current furnace change of the current blast furnace is obtained by subtracting the current time from the estimated time for the current furnace change; : The maximum firing time of the ith blast furnace; used as a known input parameter in this model; : The minimum firing time of the ith blast furnace; used as a known input parameter in this model; : Uncertain variable, the minimum interval between two blast furnace changes in the same blast furnace; : Random variable, furnace change time, used for peak shifting of different blast furnaces; Decision variables: Waiting time for the jth furnace change of the i-th blast furnace; The waiting time for the jth furnace change of the i-th blast furnace; the range is between 0 and 30; : The j-th furnace change peak time relaxation variable, ranging from 0 to 25; : The time relaxation variable for avoiding seamless connection during the jth furnace change, ranging from 0 to 60; Model: Objective function: Reduce the amount of adjustment while satisfying the constraints; (1) ; In the above formula, since the two variables of the last two terms can be input, the first term is the smallest: that is, the waiting time of the jth furnace change of the i-th blast furnace is subtracted from the initial waiting time of the jth furnace change of the i-th blast furnace and the absolute value is taken, and the minimum value is taken after traversing all the hot blast furnaces of the blast furnaces; constraint: The replacement of different blast furnaces requires staggered peak hours.

[0047] (2)； The same blast furnace cannot be connected seamlessly. (3)； Upper and lower bounds of furnace burning time (4)； Step 3: Convert to deterministic equivalent form A common method for dealing with uncertain parameters in mathematical models is to use the expected value model, which can transform the uncertain random model in step 2 into the following model: ; The uncertain distribution of uncertain variables is The probability distribution of a random variable is expressed as It means that based on probability theory and uncertainty theory, the above model is further converted into the following deterministic equivalent form: ; Step 4: Model Linearization The above mathematical model is relatively simple and small in scale, and can be solved using a solver. Due to the absolute value constraints in the model, the above model needs to be linearized. The linearization results are as follows: ; in, and All are non-negative integers.

[0048] Step 5: Model solution Write the linearized model into SCIP using the modeling language, and then use the SCIP solver to solve the mathematical modeling.

[0049] Of course, since the production plan may be changed due to human factors, the mathematical model in the present invention does not take human factors such as equipment maintenance planning into consideration. Example 2 After testing and actual verification, the present invention can automatically complete the peak-shifting function of hot blast furnace scheduling in most cases, reduce the dependence on employee experience and the man-hour cost of offline communication, and help to achieve the stability of pipeline network pressure and the reduction of blast furnace gas emission.

[0050] exist Figure 2 and 3 In the embodiment, according to the distribution of the gas pipeline network, there are four large blast furnaces A, B, C, and D that require staggered furnace replacement, among which A and B belong to the pipeline network in the same area, and C and D belong to the pipeline network in the same area. Therefore, A and B need staggered furnace replacement, C and D need staggered furnace replacement; while A and C do not need staggered furnace replacement.

[0051] Each blast furnace has four hot blast stoves, numbered 1, 2, 3, and 4. Each furnace change is done in pairs. First, the hot blast stove with the longest burning time switches from burning to supplying air, and then the hot blast stove with the longest supplying air time switches from supplying air to burning. There are two paired furnace changes in the minimum furnace change cycle involving all hot blast stoves in each blast furnace.

[0052] Figure 2 The schedule start time of the four blast furnaces of Blast Furnace A and the furnace change operation from the current state to the next state are listed. For example, in Blast Furnace A, the No. 2 hot blast furnace has the longest burning time. It switches from burning to air supply at 08:19:00 on November 29, 2024, and the switch ends at 08:36:00 on November 29, 2024. The switching time lasts for 17 minutes.

[0053] In the second column, the No. 3 hot blast furnace with the longest air supply time switched from air supply to furnace burning. Then the No. 4 hot blast furnace with the longest furnace burning time switched from furnace burning to air supply. And so on.

[0054] Figure 3 The figure shows the Gantt chart of the state switching schedule of all hot blast furnaces of the four large blast furnaces A, B, C, and D from 16:48 on November 21, 2024 to 00:00 on November 22. During each switching process, the states of burning and feeding overlap at least partially, which meets the business rule that there are two paired furnace changes within the minimum furnace change cycle of all hot blast furnaces involved in each blast furnace.

[0055] The embodiments described above are part of the embodiments of the present application, rather than all of the embodiments. The detailed description of the embodiments of the present application is not intended to limit the scope of the present application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

Claims

1. A hot blast stove scheduling peak shifting method based on uncertain stochastic programming, characterized in that: The steps include: S1: Build business rules based on the distribution of blast furnace hot blast stoves in the gas pipeline network; S2: Considering the furnace change time and interval of each blast furnace in the business rules, an uncertain random mathematical model is established: The decision variables are: the waiting time for a certain blast furnace change; the slack variable for the peak time of a certain blast furnace change; the slack variable for the time to avoid seamless connection of a certain blast furnace change; The model objective is: while satisfying the constraints, take the absolute value of the difference between the waiting time of a furnace change of a certain blast furnace and the initial waiting time of a furnace change of a certain blast furnace, and traverse all the hot blast furnaces to get the minimum value; The constraints are: the absolute value of the difference in waiting time for different blast furnaces must be equal to the sum of the staggered furnace changing time of different blast furnaces and the staggered time relaxation variable of a certain blast furnace; the absolute value of the difference in waiting time for different furnace changes in the same blast furnace must be equal to the sum of the minimum interval time between two furnace changes in the same blast furnace and the time relaxation variable to avoid seamless connection of a certain blast furnace change; the sum of the waiting time for a certain blast furnace change plus the burning time of the hot blast furnace that needs to be burned and transferred during the furnace change must be within the minimum burning time and maximum burning time of the hot blast furnace of the blast furnace; S3: Convert the uncertain random model into a deterministic equivalent form and linearize it, solve the model, and obtain the hot blast stove replacement time and replacement operation sequence combination of all blast furnaces in the current business rules.

2. The hot blast stove scheduling peak shifting method based on uncertain stochastic programming according to claim 1 is characterized in that: The business rules constructed include: Get the number of blast furnaces. Blast furnaces located in the same regional pipeline network need to be replaced at off-peak times, while blast furnaces located in different regional pipeline networks do not need to be replaced at off-peak times. Off-peak replacement means that if two blast furnaces need to be replaced at a certain moment, the other blast furnace can only start to be replaced after one of them completes the paired replacement. Get the number of hot blast stoves corresponding to each blast furnace and number them; each furnace change is done in pairs. First, set the hot blast stove with the longest furnace burning time to switch from furnace burning to air supply, and then set the hot blast stove with the longest air supply time to switch from air supply to furnace burning. 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 burning time of the hot air furnace.

3. The hot blast stove scheduling peak shifting method based on uncertain stochastic programming according to claim 1 is 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 peak shifting method based on uncertain stochastic programming according to claim 1 is characterized in that: The uncertain random mathematical model in step S2 is as follows: Objective Function , while satisfying the constraints and reducing the adjustment amount: The constraints are: Different blast furnaces need to be replaced at different peaks: ; The same blast furnace cannot be seamlessly connected: ; The upper and lower bounds of furnace burning time are as follows: ; in, i : Blast furnace number; j : Number of furnace changes; No. i Blast furnace No. j The first time the furnace is changed, the burning and transfer operation should be carried out. The burning time of the hot blast furnace; : The initial waiting time for the jth furnace change of the i-th blast furnace; : No. i Maximum firing time of a blast furnace; : No. i Minimum firing time of a blast furnace; : Uncertain variable, the minimum interval between two furnace changes in the same blast furnace; : Random variable, furnace change time, used for peak shifting of different blast furnaces; Decision variable: No. i Blast furnace No. j Waiting time for furnace change; : The time slack variable for avoiding seamless connection during the j-th furnace change; : The slack variable of the peak-shifting time of the jth furnace change.

5. The hot blast stove scheduling peak shifting method based on uncertain stochastic programming according to claim 1 is characterized in that: Step S3 adopts the expected value model, based on probability theory and uncertainty theory, to transform the uncertain random model into a deterministic equivalent form and linearize the absolute value constraint. The uncertain distribution of the uncertain variable "the minimum interval time between two blast furnace changes in the same blast furnace" is expressed as The probability distribution of the random variable "different blast furnace peak replacement time" is expressed as express.

6. The hot blast stove scheduling peak shifting method based on uncertain stochastic programming according to claim 1 is characterized in that: Step S3 is solved using a linear programming solver.

7. The hot blast stove scheduling peak shifting method based on uncertain stochastic programming according to claim 1 is characterized in that: The furnace changing sequence combination obtained in step S3 is that if a certain hot blast furnace is "burning furnace to supply air", then another hot blast furnace is "supply air to burn furnace", forming a pair of furnace changing with the certain hot blast furnace.

8. The hot blast stove scheduling peak shifting method based on uncertain stochastic programming according to claim 1 is characterized in that: In step S3, a scheduling Gantt chart is established according to the combination of the furnace replacement time and furnace replacement sequence of each hot blast furnace, and each hot blast furnace, production unit, and power generation unit in the gas pipeline network performs hot blast furnace replacement operation according to the schedule.

9. A hot blast stove scheduling system, characterized in that: include: Business rule building unit: used to extract business rules based on the distribution of blast furnace hot blast stoves in the gas pipeline network, the business rules include: Get the number of blast furnaces. Blast furnaces located in the same regional pipeline network need to be replaced at off-peak times, while blast furnaces located in different regional pipeline networks do not need to be replaced at off-peak times. Off-peak replacement means that if two blast furnaces need to be replaced at a certain moment, the other blast furnace can only start to be replaced after one of them completes the paired replacement. Get the number of hot blast stoves corresponding to each blast furnace and number them; each furnace change is done in pairs. First, set the hot blast stove with the longest furnace burning time to switch from furnace burning to air supply, and then set the hot blast stove with the longest air supply time to switch from air supply to furnace burning. 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 burning time of the hot air furnace; The random model unit is used to establish an uncertain random mathematical model according to the furnace changing time and furnace changing interval of each blast furnace: the target of the random mathematical model is to calculate the cumulative sum of the waiting time for a furnace changing of a certain blast furnace, and take the minimum value of the cumulative sum while satisfying the constraint conditions; the decision variable is: the waiting time for a furnace changing of a certain blast furnace; the constraint conditions are: the absolute value of the difference in the waiting time for furnace changing of different blast furnaces must be greater than the peak-shifting furnace changing time of different blast furnaces; the absolute value of the difference in the waiting time for different furnace changings in the same blast furnace must be greater than the minimum interval time between two furnace changings of the same blast furnace; the sum of the waiting time for a furnace changing of a certain blast furnace plus the furnace burning time of the hot blast furnace that needs to be burned and transferred during the furnace changing must be within the minimum furnace burning time and the maximum furnace burning time of the hot blast furnace of the blast furnace; The model solving and scheduling calculation unit is used to transform the uncertain random model into a deterministic equivalent form and linearize it, solve the model, and obtain the furnace changing time and furnace changing operation sequence combination for each hot blast furnace in the current business rules.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the hot blast stove scheduling peak shifting method based on uncertain stochastic programming as described in any one of claims 1 to 8 is implemented.

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