Nickel-iron furnace and AOD furnace heat scheduling method and device

Abstract

The invention relates to the technical field of steel production management, and provides a heat scheduling method for a ferronickel furnace and an AOD furnace. The method comprises the following steps: acquiring first working operation data corresponding to a plurality of ferronickel furnaces, second working operation data corresponding to a plurality of AOD furnaces, preset path matching table data between the ferronickel furnaces and the AOD furnaces, and scheduling path change data; and constructing a target path optimization function according to a plurality of second work operation data, the preset path matching table data and the scheduling path change data. And according to the target path optimization function and the plurality of first work operation data, through a preset optimization matching algorithm, obtaining target scheduling path results of the plurality of ferronickel furnaces and the plurality of AOD furnaces. In this way, the limitation that only a single target (such as the shortest path or the shortest time) is considered in the prior art is avoided. And furthermore, an optimal solution is searched from a global perspective by using a preset optimization matching algorithm, so that the heat scheduling efficiency is improved, and the waste of transportation resources is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steel production management, in particular to a nickel-iron furnace and AOD furnace batch scheduling method and device. BACKGROUND

[0002] In the stainless steel smelting process, high nickel molten iron produced by a nickel-iron furnace needs to be transported to an argon oxygen decarburization (AOD) furnace for refining. This process covers multiple nickel-iron furnaces, multiple AOD furnaces, a limited number of transport vehicles, and a complex network of internal paths, thus forming a complex dynamic scheduling problem.

[0003] In the prior art, the traditional nickel-iron furnace and AOD furnace batch scheduling method relies too much on the manual experience of the scheduler, and the batch scheduling of the nickel-iron furnace and the AOD furnace is defined according to the manual experience of the scheduler, so as to realize the transportation of the high nickel molten iron produced by the nickel-iron furnace to the argon oxygen decarburization furnace.

[0004] However, using the prior art, since only a single target such as the shortest path or the shortest time is considered, or only the batch scheduling problem of the nickel-iron furnace and the AOD furnace in a static environment is processed, there is a problem of low batch scheduling efficiency and waste of transportation resources. SUMMARY

[0005] The embodiments of the present application provide a nickel-iron furnace and AOD furnace batch scheduling method and device, which can analyze the second working operation data of multiple AOD furnaces, the preset path matching table data, and the scheduling path change data, construct a target path optimization function, and avoid the limitation of only considering a single target (such as the shortest path or the shortest time) in the prior art. Further, the optimal solution is explored from a global perspective by using a preset optimization matching algorithm, and the target scheduling path result is obtained, thereby improving the batch scheduling efficiency, reducing the waste of transportation resources, improving the production efficiency and economic benefits.

[0006] To achieve the above purpose, in a first aspect, the embodiments of the present application provide a nickel-iron furnace and AOD furnace batch scheduling method, comprising: obtaining first working operation data corresponding to multiple nickel-iron furnaces, second working operation data corresponding to multiple AOD furnaces, preset path matching table data between the multiple nickel-iron furnaces and the multiple AOD furnaces, and scheduling path change data, wherein the preset path matching table is used to determine the path preference value between the multiple nickel-iron furnaces and the multiple AOD furnaces, and each nickel-iron furnace and each AOD furnace correspond one by one; constructing a target path optimization function according to the multiple second working operation data, the preset path matching table data, and the scheduling path change data; According to the target path optimization function and the plurality of first work operation data, a plurality of nickel-iron furnaces and a plurality of AOD furnaces are obtained by a preset optimization matching algorithm.

[0007] In one embodiment, the target path optimization function is constructed according to the plurality of second work operation data, the preset path matching table data, and the scheduling path change data, comprising: Based on the preset path matching table data, the AOD furnace corresponding to each nickel-iron furnace is determined; For each nickel-iron furnace and the corresponding AOD furnace, a single penalty function of each nickel-iron furnace and the corresponding AOD furnace is constructed according to the second work operation data; According to the preset path matching table data, the path preference penalty total value of the plurality of nickel-iron furnaces and the plurality of AOD furnaces is determined; According to the plurality of single penalty functions, the path preference penalty total value, and the scheduling path change data, the target path optimization function is constructed by weighted summation.

[0008] In one embodiment, the single penalty function of each nickel-iron furnace and the corresponding AOD furnace is constructed according to the second work operation data, comprising: According to the second work operation data, a time penalty function, a weight penalty function, and a nickel point penalty function are constructed; The time penalty function, the weight penalty function, and the nickel point penalty function are weighted and summed to obtain the single penalty function.

[0009] In one embodiment, the second work operation data includes planned iron replacement time, heavy package transportation time, heavy package waiting time, and reserved time length coefficient, and the time penalty function is constructed according to the second work operation data, comprising: According to the planned iron replacement time, the heavy package transportation time, and the heavy package waiting time, the latest plugging time is determined; According to the latest plugging time, the heavy package transportation time, and the reserved time length coefficient, the scheduling plugging time is determined; According to the latest plugging time and the scheduling plugging time, the time penalty function is constructed; Wherein, the time penalty function can be defined by the following expression:

[0010] Wherein, The scheduling plugging time is represented by T, The latest plugging time is represented by T, And a configurable penalty coefficient at the t-th time point.

[0011] In one embodiment, the second work operation data further comprises a standard nickel equivalent, a remaining water nickel point, a remaining water weight, a required nickel point, a minimum remaining water weight, and a maximum remaining water weight, and a weight penalty function is constructed according to the second work operation data, comprising: a makeup water amount is determined according to the standard nickel equivalent, the remaining water nickel point, the remaining water weight, and the required nickel point; a minimum hot metal weight is determined according to the makeup water amount and the minimum remaining water weight; a maximum hot metal weight is determined according to the makeup water amount and the maximum remaining water weight; the weight penalty function is constructed according to the minimum hot metal weight and the maximum hot metal weight; wherein the weight penalty function can be defined by the following expression:

[0012] wherein, represents the minimum hot metal weight, represents the maximum hot metal weight, represents a minimum weight penalty value, represents an overweight linear penalty coefficient.

[0013] In one embodiment, the second work operation data further comprises a total hot metal weight and an average nickel point, and a nickel point penalty function is constructed according to the second work operation data, comprising: the nickel point penalty function is constructed according to the total hot metal weight, the average nickel point, and the required nickel point; wherein the nickel point penalty function can be defined by the following expression:

[0014] wherein, represents a nickel point penalty coefficient, represents the total hot metal weight, represents the average nickel point, represents the required nickel point.

[0015] In one embodiment, the time penalty function, the weight penalty function, and the nickel point penalty function are weighted and summed to obtain the single-time penalty function, comprising: the single-time penalty function is determined according to the formula wherein, represents a weight coefficient of the time penalty function, represents a weight coefficient of the weight penalty function, ​This represents the weighting coefficient of the nickel point penalty function.

[0016] In one embodiment, the target path optimization function may be defined by the following expression:

[0017] in, This represents the coefficient of the total single-time penalty for multiple nickel-iron furnaces and multiple AOD furnaces. This represents the total value coefficient of the path preference penalty. This represents the data coefficient for the scheduling path change. Indicates the first i The AOD furnace matches the first j The single-time penalty value for each batch of nickel-iron furnace iron production. Indicates the first i The AOD furnace matches the first j The path preference penalty value for each nickel-iron furnace operation. This indicates the scheduling path change data.

[0018] In one embodiment, the method further includes: generating a scheduling control instruction based on the target scheduling path result, so that an operator can control the path scheduling between multiple nickel-iron furnaces and multiple AOD furnaces according to the scheduling control instruction.

[0019] Secondly, embodiments of the present invention provide a furnace scheduling device for nickel-iron furnaces and AOD furnaces, comprising: The data acquisition module is used to acquire first working operation data corresponding to multiple nickel-iron furnaces, second working operation data corresponding to multiple AOD furnaces, preset path matching table data between multiple nickel-iron furnaces and multiple AOD furnaces, and scheduling path change data. The preset path matching table is used to determine the path preference value between the multiple nickel-iron furnaces and the multiple AOD furnaces, with each nickel-iron furnace corresponding to each AOD furnace. The target path optimization function construction module is used to construct the target path optimization function based on multiple second work operation data, the preset path matching table data, and the scheduling path change data. The target scheduling path result acquisition module is used to obtain the target scheduling path results of multiple nickel-iron furnaces and multiple AOD furnaces based on the target path optimization function and multiple first working operation data, through a preset optimization matching algorithm.

[0020] The above technical solution has the following technical effects: Thus, the furnace scheduling method for nickel-iron furnaces and AOD furnaces provided in this embodiment acquires first operating data corresponding to multiple nickel-iron furnaces, second operating data corresponding to multiple AOD furnaces, a preset path matching table between multiple nickel-iron furnaces and multiple AOD furnaces, and scheduling path change data. The preset path matching table is used to determine the path preference values ​​between multiple nickel-iron furnaces and multiple AOD furnaces, with each nickel-iron furnace corresponding to each AOD furnace. Based on multiple second operating data, the preset path matching table data, and the scheduling path change data, a target path optimization function is constructed. Based on the target path optimization function and multiple first operating data, a preset optimization matching algorithm is used to obtain the target scheduling path results between multiple nickel-iron furnaces and multiple AOD furnaces. In this way, by analyzing the second operating data of multiple AOD furnaces, the preset path matching table data, and the scheduling path change data to construct the target path optimization function, the limitation of prior art that only considers a single objective (such as the shortest path or the shortest time) is avoided. Furthermore, by utilizing a pre-defined optimization matching algorithm to explore the optimal solution from a global perspective and obtain the target scheduling path result, the efficiency of furnace scheduling is improved, the waste of transportation resources is reduced, and production efficiency and economic benefits are increased. Attached Figure Description

[0021] Figure 1 This is a schematic flowchart of a furnace scheduling method for nickel-iron furnaces and AOD furnaces provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of a furnace scheduling device for nickel-iron furnace and AOD furnace provided in an embodiment of the present invention. Detailed Implementation

[0022] To further illustrate the various embodiments, the present invention provides accompanying drawings. These drawings are part of the disclosure of the present invention, primarily used to illustrate the embodiments and to explain the operating principles of the embodiments in conjunction with the relevant descriptions in the specification. With reference to these drawings, those skilled in the art should be able to understand other possible implementations and the advantages of the present invention. Components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0023] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0024] Example 1: Figure 1 This is a schematic flowchart illustrating a furnace scheduling method for nickel-iron furnaces and AOD furnaces according to an embodiment of the present invention. This embodiment specifically includes the following steps: S10: Obtain the first working operation data corresponding to multiple nickel-iron furnaces, the second working operation data corresponding to multiple AOD furnaces, the preset path matching table data between multiple nickel-iron furnaces and multiple AOD furnaces, and the scheduling path change data.

[0025] The first operational data is used to constrain the optimization range of the target scheduling path result. The first operational data includes the amount of iron stored in the nickel-iron furnace under different operating conditions. These different operating conditions may be, for example, the iron production operating condition, the iron tapping operating condition, and the slag exposure operating condition.

[0026] For a nickel-iron furnace in its iron-producing operation, according to the formula... Determine the iron reserves, of which, This represents the amount of iron stored at time t. This indicates the maximum iron storage capacity configuration parameter. Indicates the rate of iron production.

[0027] For the nickel-iron furnace in the tapping operation state, according to the formula Determine the iron reserves, of which, Indicates the iron tapping speed.

[0028] For nickel-iron furnaces in the slag-bearing operating state .

[0029] The aforementioned preset path matching table is used to determine the path preference values ​​between multiple ferronickel furnaces and multiple AOD furnaces, with each ferronickel furnace corresponding to a specific AOD furnace. Specifically, the preset path matching table can determine whether each ferronickel furnace has a matching, reachable AOD furnace, check for furnaces with no casting capacity and in a safe inventory warning state, and identify furnaces that have already issued refills of special iron batches. If any of these exist, the corresponding AOD furnace must be matched to that furnace.

[0030] For example, the preset path matching table can be a two-dimensional matrix. The values ​​in the matrix represent the path preference from the nickel-iron furnace to the corresponding AOD furnace. The value is -1, which means the path is unreachable; 0 means the preferred path; 1 means the reachable path; 2 means the not recommended path; and 3 means the strongly not recommended path. However, it is not limited to this. The present invention is not specifically limited. Those skilled in the art can set it according to the actual situation.

[0031] The aforementioned scheduling path change data refers to determining the number of times the path changes between two adjacent scheduling operations for multiple nickel-iron furnaces and multiple AOD furnaces.

[0032] It should be noted that the data interface module can obtain the first working operation data corresponding to multiple nickel-iron furnaces, the second working operation data corresponding to multiple AOD furnaces, and the preset path matching table data in real time from the manufacturing execution system, process control system, and laboratory information management system. In order to ensure the accuracy of the subsequent acquisition of target scheduling path results, the acquired data needs to be cleaned, format converted, and outlier handled.

[0033] Specifically, the system acquires the first operating data corresponding to multiple nickel-iron furnaces, namely the iron storage amount of the nickel-iron furnaces under different operating states, the second operating data corresponding to multiple AOD furnaces, the preset path matching table data between multiple nickel-iron furnaces and multiple AOD furnaces, the preset path matching table is used to determine the path preference value between multiple nickel-iron furnaces and multiple AOD furnaces, thereby determining the AOD furnace corresponding to each nickel-iron furnace, and scheduling path change data.

[0034] S11: Construct the target path optimization function based on multiple second-work operation data, preset path matching table data, and scheduling path change data.

[0035] Specifically, after obtaining the second working operation data, preset path matching table data, and scheduling path change data corresponding to multiple AOD furnaces, a target path optimization function is constructed based on the multiple second working operation data, preset path matching table data, and scheduling path change data.

[0036] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S11 may be: S111: Based on the preset path matching table data, determine the AOD furnace corresponding to each nickel-iron furnace.

[0037] S112: For each nickel-iron furnace and its corresponding AOD furnace, construct a single-cycle penalty function for each nickel-iron furnace and its corresponding AOD furnace based on the second working operation data.

[0038] Specifically, based on the preset path matching table data, it is possible to determine the corresponding AOD furnace for each nickel-iron furnace. Based on this, after determining the corresponding AOD furnace for each nickel-iron furnace, a single-time penalty function is constructed for each nickel-iron furnace and its corresponding AOD furnace according to the second working operation data.

[0039] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S112 may be: S1121: Based on the second working data, construct the time penalty function, weight penalty function, and nickel point penalty function.

[0040] Optionally, based on the above embodiments, the second operational data includes: planned iron exchange time, heavy package transportation time, heavy package waiting time, and reserved time coefficient. Therefore, in some embodiments of the present invention, one implementation of constructing a time penalty function based on the second operational data can be: S20: Determine the latest time to close the port based on the planned iron exchange time, the transportation time of the heavy package, and the waiting time of the heavy package.

[0041] Specifically, after obtaining the planned iron ore delivery time, the heavy package transportation time, and the heavy package waiting time, the latest blocking time is determined based on these factors.

[0042] Optional, according to the formula Determine the latest time for closing the road, among which, Indicates the planned iron ore delivery time. Indicates the transportation time for heavy packages. This indicates the waiting time for repackaging.

[0043] S21: Determine the timing of the blockade based on the latest blockade time, the transport time of the heavy package, and the reserved time coefficient.

[0044] Specifically, after obtaining the latest blocking time, the blocking time is determined based on the latest blocking time, the transport time of the heavy package, and the reserved time coefficient.

[0045] Optional, according to the formula Determine the timing of the bottleneck, among which, This represents the reserved time coefficient.

[0046] S22: Construct a time penalty function based on the latest blocking time and the scheduled blocking time.

[0047] The time penalty function can be defined by the following expression:

[0048] in, Indicates the time for scheduling the blockage. Indicates the latest time to close the passage. and This represents the configurable penalty coefficient at time t, with an example of the penalty coefficient. For example, a penalty coefficient of 100. For example, it can be 200, but is not limited to this. This invention is not specifically limited, and those skilled in the art can set it according to the actual situation.

[0049] Optionally, based on the above embodiments, the second working operation data further includes: standard nickel equivalent, residual water nickel point, residual water weight, required nickel point, minimum residual water weight, and maximum residual water weight. Based on this, in some embodiments of the present invention, one implementation of constructing the weight penalty function according to the second working operation data can be: S23: Determine the amount of water to be replenished based on the standard nickel equivalent, the nickel point of the remaining water, the weight of the remaining water, and the required nickel point.

[0050] Specifically, after obtaining the standard nickel equivalent, residual water nickel point, residual water weight, and required nickel point, the amount of water to be replenished is determined based on these parameters.

[0051] Optional, according to the formula Determine the amount of water to replenish, among which, This indicates a requirement for nickel points. Indicates standard nickel equivalent. Indicates the residual nickel point. This indicates the weight of the remaining water.

[0052] S24: Determine the minimum required iron weight based on the amount of water replenished and the minimum remaining water weight.

[0053] Specifically, after obtaining the water replenishment amount, the minimum required iron weight is determined based on the water replenishment amount and the minimum remaining water weight.

[0054] Optional, according to the formula Determine the minimum required iron weight, where, This indicates the minimum remaining water weight.

[0055] S25: Determine the maximum required iron weight based on the amount of water replenished and the maximum weight of the remaining water.

[0056] Specifically, after obtaining the water replenishment amount, the maximum required iron weight is determined based on the water replenishment amount and the maximum remaining water weight.

[0057] Optional, according to the formula Determine the maximum required iron weight, where, This indicates the maximum remaining water weight.

[0058] S26: Construct a weight penalty function based on the minimum and maximum required iron weights.

[0059] Specifically, after obtaining the minimum and maximum required iron weights, a weight penalty function is constructed based on these weights.

[0060] The weight penalty function can be defined by the following expression:

[0061] in, Indicates the minimum required iron weight. Indicates the maximum required iron weight. This is the minimum weight penalty value, which could be, for example, 100. The overweight linear penalty coefficient can be, for example, 100, but is not limited thereto. This invention does not impose specific limitations, and those skilled in the art can set it according to the actual situation.

[0062] Optionally, based on the above embodiments, the second working operation data further includes: total molten iron weight and average nickel point. Therefore, in some embodiments of the present invention, one implementation of constructing the nickel point penalty function based on the second working operation data can be: S27: Construct a nickel point penalty function based on the total weight of molten iron, the average nickel point, and the required nickel point.

[0063] Specifically, after obtaining the total molten iron weight, average nickel point, and required nickel point, a nickel point penalty function is constructed based on the total molten iron weight, average nickel point, and required nickel point.

[0064] The nickel point penalty function can be defined by the following expression:

[0065] in, This represents the nickel point penalty coefficient. This indicates the total weight of molten iron. Indicates the average nickel point. This indicates a requirement for nickel points.

[0066] S1122: Perform a weighted summation of the time penalty function, weight penalty function, and nickel point penalty function to obtain the single penalty function.

[0067] Specifically, after obtaining the time penalty function, weight penalty function, and nickel point penalty function, the time penalty function, weight penalty function, and nickel point penalty function are weighted and summed to obtain the single penalty function for each nickel-iron furnace and the corresponding AOD furnace.

[0068] Optionally, based on the above embodiments, in some embodiments of the present invention, according to the formula... Determine the single-time penalty function.

[0069] in, This represents the weighting coefficients of the time penalty function. This represents the weight coefficients of the weight penalty function. This represents the weighting coefficients of the nickel-point penalty function. The values ​​of the weight coefficients of the time penalty function, the weight penalty function, and the nickel point penalty function are not specifically limited in this invention, and those skilled in the art can set them according to the actual situation.

[0070] S113: Determine the total path preference penalty value for multiple nickel-iron furnaces and multiple AOD furnaces based on the preset path matching table data.

[0071] Specifically, based on the obtained preset path matching table data, the total path preference penalty value of multiple nickel-iron furnaces and multiple AOD furnaces is obtained.

[0072] For example, following the above embodiments, a preset path matching table is a two-dimensional matrix. The values ​​in the matrix represent the path preference degree from the nickel-iron furnace to the corresponding AOD furnace. The value is -1 indicating that the path is unreachable, 0 indicating a preferred path, 1 indicating a reachable path, 2 indicating a discouraged path, and 3 indicating a strongly discouraged path. The multiple values ​​in the two-dimensional matrix are summed to obtain the total path preference penalty value for multiple nickel-iron furnaces and multiple AOD furnaces.

[0073] S114: Construct the target path optimization function by weighted summation of multiple single-penalty functions, the total value of path preference penalties, and scheduling path change data.

[0074] Specifically, after obtaining multiple single-time penalty functions, the total value of path preference penalty, and scheduling path change data, a weighted sum is performed on the multiple single-time penalty functions, the total value of path preference penalty, and the scheduling path change data to construct the target path optimization function.

[0075] Optionally, based on the above embodiments, in some embodiments of the present invention, the target path optimization function may be defined by the following expression:

[0076] in, This represents the coefficient of the total single-time penalty for multiple nickel-iron furnaces and multiple AOD furnaces. This represents the total coefficient of path preference penalty. This represents the data coefficient for scheduling path changes. Indicates the first i The AOD furnace matches the first j The single-time penalty value for each batch of nickel-iron furnace iron production. Indicates the first i The AOD furnace matches the first j The path preference penalty value for each nickel-iron furnace operation. This indicates data related to changes in the scheduling path.

[0077] S12: Based on the target path optimization function and multiple first working operation data, obtain the target scheduling path results of multiple nickel-iron furnaces and multiple AOD furnaces through a preset optimization matching algorithm.

[0078] Specifically, after constructing the target path optimization function, a preset optimization matching algorithm is used to find the target scheduling path results for multiple nickel-iron furnaces and multiple AOD furnaces based on the target path optimization function and multiple first working operation data.

[0079] Optionally, based on the above embodiments, the preset optimization matching algorithm can be a genetic algorithm. Therefore, in some embodiments of the present invention, one implementation of S12 can be: Using the AOD furnace as the gene location and the gene value as the encoding of the nickel-iron furnace matched with the AOD furnace, multiple chromosomes are constructed. For the initial population, heuristic rules are used to obtain the chromosomes. Constraints are set based on multiple first working operation data, nickel-iron furnaces with no casting capacity and reaching the safe inventory warning state, and nickel-iron furnaces that have issued re-contracts. The optimization range of the target scheduling path results is set. Furthermore, based on the target path optimization function, a preset search method is used for iterative optimization to obtain the target scheduling path results of multiple nickel-iron furnaces and multiple AOD furnaces. The preset search method can be, for example, tournament selection, two-point crossover, or exchange mutation.

[0080] Optionally, based on the above embodiments, the preset optimization matching algorithm can also be an algorithm based on a constraint programming solver using a satisfiability method. Therefore, in some embodiments of the present invention, another implementation of S12 can be: Based on a predefined 0-1 decision variable representing the matching relationship between each nickel-iron furnace and each AOD furnace, multiple constraints are set, including: First constraint: matching constraint between each nickel-iron furnace and each AOD furnace; Second constraint: nickel-iron furnaces with special iron grades (i.e., those without casting capacity and reaching the safety stock warning state) and nickel-iron furnaces that have issued re-bundles must have a corresponding AOD furnace matching constraint; Third constraint: constraints on weight and time range. Based on this, using a predefined search method based on the target path optimization function, iterative optimization is performed to obtain the target scheduling path results for multiple nickel-iron furnaces and multiple AOD furnaces.

[0081] Thus, the furnace scheduling method for nickel-iron furnaces and AOD furnaces provided in this embodiment acquires first operating data corresponding to multiple nickel-iron furnaces, second operating data corresponding to multiple AOD furnaces, a preset path matching table between multiple nickel-iron furnaces and multiple AOD furnaces, and scheduling path change data. The preset path matching table is used to determine the path preference values ​​between multiple nickel-iron furnaces and multiple AOD furnaces, with each nickel-iron furnace corresponding to each AOD furnace. Based on multiple second operating data, the preset path matching table data, and the scheduling path change data, a target path optimization function is constructed. Based on the target path optimization function and multiple first operating data, a preset optimization matching algorithm is used to obtain the target scheduling path results between multiple nickel-iron furnaces and multiple AOD furnaces. In this way, by analyzing the second operating data of multiple AOD furnaces, the preset path matching table data, and the scheduling path change data to construct the target path optimization function, the limitation of prior art that only considers a single objective (such as the shortest path or the shortest time) is avoided. Furthermore, by utilizing a pre-defined optimization matching algorithm to explore the optimal solution from a global perspective and obtain the target scheduling path result, the efficiency of furnace scheduling is improved, the waste of transportation resources is reduced, and production efficiency and economic benefits are increased.

[0082] Optionally, based on the above embodiments, some embodiments of the present invention further include: Based on the target scheduling path results, a scheduling control instruction is generated so that the operator can control the path scheduling between multiple nickel-iron furnaces and multiple AOD furnaces according to the scheduling control instruction.

[0083] Specifically, after obtaining the target scheduling path result, a scheduling control instruction is generated based on the target scheduling path result and sent to the control terminal. The operator can obtain the path scheduling information between multiple nickel-iron furnaces and multiple AOD furnaces based on the scheduling control instruction, thereby realizing the path scheduling between multiple nickel-iron furnaces and multiple AOD furnaces.

[0084] Example 2: Figure 2 This is a schematic diagram of a furnace scheduling device for nickel-iron furnaces and AOD furnaces according to an embodiment of the present invention, as shown below. Figure 2 As shown, it includes: a data acquisition module 10, a target path optimization function construction module 11, and a target scheduling path result acquisition module 12.

[0085] The data acquisition module 10 is used to acquire first working operation data corresponding to multiple nickel-iron furnaces, second working operation data corresponding to multiple AOD furnaces, preset path matching table data between multiple nickel-iron furnaces and multiple AOD furnaces, and scheduling path change data. The preset path matching table is used to determine the path preference value between the multiple nickel-iron furnaces and multiple AOD furnaces, with each nickel-iron furnace corresponding to each AOD furnace.

[0086] The target path optimization function construction module 11 is used to construct the target path optimization function based on multiple second work operation data, preset path matching table data, and scheduling path change data.

[0087] The target scheduling path result acquisition module 12 is used to acquire the target scheduling path results of multiple nickel-iron furnaces and multiple AOD furnaces based on the target path optimization function and multiple first working operation data, through a preset optimization matching algorithm.

[0088] In this embodiment, the data acquisition module acquires first operating data corresponding to multiple nickel-iron furnaces, second operating data corresponding to multiple AOD furnaces, a preset path matching table between the multiple nickel-iron furnaces and the multiple AOD furnaces, and scheduling path change data. The preset path matching table is used to determine the path preference values ​​between the multiple nickel-iron furnaces and the multiple AOD furnaces, with each nickel-iron furnace corresponding to each AOD furnace. The target path optimization function construction module constructs a target path optimization function based on the multiple second operating data, the preset path matching table data, and the scheduling path change data. The target scheduling path result acquisition module obtains the target scheduling path results between the multiple nickel-iron furnaces and the multiple AOD furnaces based on the target path optimization function and the multiple first operating data, using a preset optimization matching algorithm. Thus, by analyzing the second operating data of the multiple AOD furnaces, the preset path matching table data, and the scheduling path change data to construct the target path optimization function, the limitations of prior art that only considers a single objective (such as the shortest path or the shortest time) are avoided. Furthermore, by utilizing a pre-defined optimization matching algorithm to explore the optimal solution from a global perspective and obtain the target scheduling path result, the efficiency of furnace scheduling is improved, the waste of transportation resources is reduced, and production efficiency and economic benefits are increased.

[0089] Although the invention has been specifically shown and described in conjunction with preferred embodiments, those skilled in the art should understand that various changes in form and detail may be made to the invention without departing from the spirit and scope of the invention as defined in the appended claims, all of which shall be within the scope of protection of the invention.

Claims

1. A method for scheduling the furnace cycles of a nickel-iron furnace and an AOD furnace, characterized in that, The method includes: The system acquires first operating data corresponding to multiple nickel-iron furnaces, second operating data corresponding to multiple AOD furnaces, preset path matching table data between multiple nickel-iron furnaces and multiple AOD furnaces, and scheduling path change data. The preset path matching table is used to determine the path preference value between the multiple nickel-iron furnaces and the multiple AOD furnaces, with each nickel-iron furnace corresponding to each AOD furnace. Based on multiple second-work operation data, the preset path matching table data, and scheduling path change data, a target path optimization function is constructed. Based on the target path optimization function and multiple first-stage operational data, the target scheduling path results for multiple nickel-iron furnaces and multiple AOD furnaces are obtained through a preset optimization matching algorithm.

2. The method according to claim 1, characterized in that, The step of constructing a target path optimization function based on multiple second-work operation data, the preset path matching table data, and scheduling path change data includes: Based on the preset path matching table data, the AOD furnace corresponding to each nickel-iron furnace is determined; For each of the nickel-iron furnaces and the corresponding AOD furnaces, a single-time penalty function is constructed for each of the nickel-iron furnaces and the corresponding AOD furnaces based on the second operating data; Based on the preset path matching table data, determine the total path preference penalty value for multiple nickel-iron furnaces and multiple AOD furnaces; The target path optimization function is constructed by weighted summation of multiple single-penalty functions, the total value of the path preference penalty, and the scheduling path change data.

3. The method according to claim 2, characterized in that, The step of constructing a single-cycle penalty function for each nickel-iron furnace and its corresponding AOD furnace based on the second operational data includes: Based on the second working data, construct the time penalty function, the weight penalty function, and the nickel point penalty function; The time penalty function, weight penalty function, and nickel point penalty function are weighted and summed to obtain the single penalty function.

4. The method according to claim 3, characterized in that, The second operational data includes: planned iron ore exchange time, heavy package transportation time, heavy package waiting time, and reserved time coefficient. Based on the second operational data, a time penalty function is constructed, including: The latest blocking time is determined based on the planned iron exchange time, the heavy package transportation time, and the heavy package waiting time. The scheduling time for blocking the road is determined based on the latest blocking time, the duration of heavy package transportation, and the reserved time coefficient. The time penalty function is constructed based on the latest blocking time and the scheduled blocking time. The time penalty function can be defined by the following expression: in, This indicates the scheduling of the bottleneck time. This indicates the latest time the gate was closed. and This represents the configurable penalty coefficient at time t.

5. The method according to claim 4, characterized in that, The second operational data also includes: standard nickel equivalent, residual water nickel point, residual water weight, required nickel point, minimum residual water weight, and maximum residual water weight. Based on the second operational data, a weight penalty function is constructed, including: The amount of water to be replenished is determined based on the standard nickel equivalent, the nickel point of the remaining water, the weight of the remaining water, and the required nickel point. The minimum required iron weight is determined based on the water replenishment amount and the minimum remaining water weight. The maximum required iron weight is determined based on the water replenishment volume and the maximum remaining water weight. The weight penalty function is constructed based on the minimum required iron weight and the maximum required iron weight; The weight penalty function can be defined by the following expression: in, This indicates the minimum required iron weight. This indicates the maximum required iron weight. This represents the minimum weight penalty value. This represents the linear penalty coefficient for overweight.

6. The method according to claim 5, characterized in that, The second operational data also includes: total molten iron weight and average nickel point. Based on the second operational data, a nickel point penalty function is constructed, including: Based on the total weight of molten iron, the average nickel point, and the required nickel point, the nickel point penalty function is constructed. The nickel point penalty function can be defined by the following expression: in, This represents the nickel point penalty coefficient. This indicates the total weight of the molten iron. This indicates the average nickel point. This indicates the required nickel point.

7. The method according to claim 6, characterized in that, The step of weighted summing of the time penalty function, weight penalty function, and nickel point penalty function to obtain the single-time penalty function includes: According to the formula Determine the single-step penalty function; in, This represents the weighting coefficient of the time penalty function. This represents the weight coefficient of the weight penalty function. This represents the weighting coefficient of the nickel point penalty function.

8. The method according to claim 7, characterized in that, The target path optimization function can be defined by the following expression: in, This represents the coefficient of the total single-time penalty for multiple nickel-iron furnaces and multiple AOD furnaces. This represents the total value coefficient of the path preference penalty. This represents the data coefficient for the scheduling path change. Indicates the first i The AOD furnace matches the first j The single-time penalty value for each batch of nickel-iron furnace iron production. Indicates the first i The AOD furnace matches the first j The path preference penalty value for each nickel-iron furnace operation. This indicates the scheduling path change data.

9. The method according to claim 8, characterized in that, The method further includes: Based on the target scheduling path result, a scheduling control instruction is generated so that the operator can control the path scheduling between multiple nickel-iron furnaces and multiple AOD furnaces according to the scheduling control instruction.

10. A furnace scheduling device for nickel-iron furnaces and AOD furnaces, characterized in that, The device includes: The data acquisition module is used to acquire first working operation data corresponding to multiple nickel-iron furnaces, second working operation data corresponding to multiple AOD furnaces, preset path matching table data between multiple nickel-iron furnaces and multiple AOD furnaces, and scheduling path change data. The preset path matching table is used to determine the path preference value between the multiple nickel-iron furnaces and the multiple AOD furnaces, with each nickel-iron furnace corresponding to each AOD furnace. The target path optimization function construction module is used to construct the target path optimization function based on multiple second work operation data, the preset path matching table data, and the scheduling path change data. The target scheduling path result acquisition module is used to obtain the target scheduling path results of multiple nickel-iron furnaces and multiple AOD furnaces based on the target path optimization function and multiple first working operation data, through a preset optimization matching algorithm.

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