A method and system for optimal allocation of sintered blended ore based on pruning backtracking

Abstract

This invention discloses an optimal allocation method and system for sintered blended ore based on pruning and backtracking. The method includes the following steps: defining the main element content and total proportion of each ore type; modeling the ore blending problem based on actual parameters; searching for the optimal solution using a backtracking algorithm and outputting all ore blending schemes that meet the requirements; pruning and optimizing the ore blending schemes; and performing secondary programming on the obtained ore blending schemes to obtain the proportioning schemes. The effectiveness and practicality of the algorithm have been verified through application in a real production environment. Optimal ore blending and proportioning schemes have been repeatedly provided in actual ore blending processes. These schemes fully meet the ore blending target requirements, ensure stable production, effectively improve ore blending quality and efficiency, reduce production costs, and create greater economic benefits for enterprises.

Description

Technical Field

[0001] This invention relates to the field of metallurgical sintering technology, and in particular to an optimal allocation method and system for sintered blended ore based on pruning backtracking. Background Technology

[0002] In steel sintering, to reduce costs, it is necessary to use a mixture of iron ore from multiple sources for sintering to achieve stable production output, quality, and cost. Because the quantity of ore to be blended exceeds the number of batching silos, steel companies face difficulties in batching mixed ore. For example, the mixed ore in Liuzhou Steel's secondary material yard consists of 16 types of ore, while there are only 8 batching silos. Therefore, it needs to be blended multiple times to ensure that the iron and silicon content of the blended ore is the same each time. Due to the limited capacity of the feeding equipment, only two minerals can be changed each time the formula is changed. The entire process is divided into 5 batching stages, requiring that the ore types in these 5 batching stages cover all 16 types, and that the total ratio of the 5 batching stages matches the target ratio set by the group. In the past, when there were only 10 types of ore, a relatively ideal formula could be obtained by manual combination. However, now that there are 16 types of ore, the number of possible combinations reaches 12,870, exceeding human calculation capabilities.

[0003] To address this issue, many scholars have proposed their research solutions: fuzzy optimization algorithms are a commonly used method in the optimization of sintering raw material formulations. Tang Qingli, in his "Comparative Analysis of Sintering Batching Optimization Methods and Algorithms," points out that for large-scale complex problems, genetic algorithms, particle swarm optimization, and ant colony optimization can flexibly and effectively solve the problems; neural networks can predict production results based on big data; and expert systems built upon neural networks can effectively guide production practice. Kang Zhipeng, in his "Research on the Mixing and Batching Technology of Equal SiO2 and Equal TFe in Shougang Jingtang," transforms on-site process constraints into computer language, establishes a mathematical model, and introduces intelligent algorithms to solve the model. This upgrades the "equal SiO2" packing of the mixed ore to "equal SiO2 and equal TFe" packing, establishing an intelligent optimization batching system for the mixed ore, improving the mixed ore indicators, and significantly improving the quality and stability of the sinter.

[0004] In summary, the optimization of sintering raw material formulation is a complex multi-objective optimization problem. Various methods, such as fuzzy optimization algorithms, genetic algorithms, neural networks, and simulated annealing algorithms, can partially address this issue. However, these methods are not closely integrated with on-site conditions to solve the comprehensive optimization problem of multiple ore blending processes. Therefore, it is necessary to invent an optimized ore blending algorithm that automatically finds the best mixed ore blending scheme to achieve stable production and improve production efficiency and quality. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] Given that the quantity of ore to be blended in existing sintered blended ores far exceeds the quantity of batching bins, which exceeds the computational capacity of manual batching and the need for multiple batching optimizations, this invention is proposed.

[0007] Therefore, the purpose of this invention is to provide an optimal allocation method and system for sintered blended ore based on pruning backtracking. The aim is to find the best blending scheme for the mixed ore by using the pruning backtracking optimization method in combination with on-site conditions, thereby solving the difficulties of manual ore blending, achieving stable production, and improving efficiency.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] In a first aspect, the present invention provides an optimal allocation method for sintered blended ore based on pruning backtracking, the specific steps of which include defining the content of major elements and the total proportion of each type of ore according to the type of ore.

[0010] The ore blending problem is modeled based on actual parameters;

[0011] The optimal solution is searched using a backtracking algorithm, and all ore blending schemes that meet the requirements are output.

[0012] The ore blending scheme was pruned and optimized.

[0013] The obtained ore blending scheme is then subjected to secondary planning to obtain the ore blending ratio scheme.

[0014] As a preferred embodiment of the optimal allocation method for sintered blended ore based on pruning backtracking described in this invention, the main element content includes the iron content, silicon content, and aluminum content of each ore. The target iron content, target silicon content, and target aluminum content of all ores are calculated using the following formulas: Where n represents the number of ore types, and n is greater than 10; M represents the content of each major element in each ore; and k represents the total proportion of each ore.

[0015] As a preferred embodiment of the optimal allocation method for sintered blended ore based on pruning backtracking described in this invention, the actual parameters include 16 ore types and 8 batching bins. Only 2 ore batching bins can be changed each time, and the entire process is divided into 5 batching operations. The ore types in the 5 batching operations cover all types of ore, so the set S = {1, 2, 3, ..., 16} is defined. |Ti|=8, |T1∩T2|=6, |T2∩T3|=6, |T3∩T4|=6, |T4∩T5|=6, |T1∪T2∪T3∪T4∪T5|=16, where Ti represents the set of elements taken in the i-th time. Find all combinations of {T1, T2, T3, T4, T5}; and the total content of each major element in each ore blend is consistent with the target iron content, target silicon content, and target aluminum content.

[0016] As a preferred embodiment of the optimal allocation method for sintered blended ore based on pruning backtracking described in this invention, the following steps are included: The optimal solution search via backtracking algorithm involves selecting two types of ore from the candidate ore batches each time, first sorting them according to iron content, selecting the two ores with the highest and second highest iron content, and adding them to the current batch; based on the capacity limitations of the feeding equipment, only two types of ore are changed each time the formula is changed; if the number of ore types in the current formula is equal to 8, then the iron content is calculated and compared to update the optimal solution; if the number of ore types in the current formula is less than 8, then two types of ore are selected from the remaining candidate ore batches, and the search continues downwards to output the proposed solution.

[0017] As a preferred embodiment of the optimal allocation method for sintered blended ore based on pruning backtracking as described in this invention, the pruning optimization includes: during the search process, if it is found that a certain ore blending scheme cannot meet the capacity limit of the feeding equipment, then pruning is performed directly, and the subtree of that state is no longer searched; using the convex hull criterion to determine the relationship between the eight selected ores and the objective function, if the target component falls outside the convex hull, then pruning is performed directly, and the subtree of that state is no longer searched; if a certain material yard equipment fails for a short period of time during the production process, when any seven of the eight ores selected in a certain batch cannot reach the target content, pruning is performed directly, and the subtree of that state is no longer searched.

[0018] As a preferred embodiment of the optimal allocation method for sintered blended ore based on pruning backtracking described in this invention, the objective function is:

[0019]

[0020] Among them, M1, M2, and M3 represent the iron, silicon, and aluminum contents of the eight ores in five blending operations, respectively. Each matrix is ​​5x8. In this blending operation, the iron content is 57.95%, the silicon content is 5.16%, and the aluminum content is 2.17%, values ​​determined by the overall blending ratio. The constraints include the following two items: First, x i，j Every element in the set is greater than 0.03 and less than 0.5, and this condition holds true for any value of i from 1 to 5. Where j indicates that only 8 batching bins can be used for each batching; secondly, for n = 0, 1, ..., 15, representing 16 types of ore, we have: Where N2 represents the combination of five blends of each ore, (r, c)∈N2 n This represents a tuple in a certain combination of minerals N2.

[0021] As a preferred embodiment of the optimal allocation method for sintered blended ore based on pruning backtracking described in this invention, the quadratic programming includes using the sum of squares of the differences between the total proportion of iron, silicon, and aluminum and the target iron content, silicon content, and aluminum content as the objective function, and solving this function yields the final proportioning scheme for the fifth time.

[0022] Secondly, embodiments of the present invention provide an optimal allocation system for sintered blended ore based on pruning backtracking, which implements the steps of the above method using any computer programming language, and compiles and runs the program on any operating system and any hardware architecture.

[0023] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-described method.

[0024] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the above-described method.

[0025] The beneficial effects of this invention are as follows: This invention provides an efficient search algorithm that can find the optimal solution or near-optimal solution in a short time, providing strong support for decision-making and optimization in related fields; the method of this invention fully considers practical problems such as a large number of candidate minerals and strict formula restrictions, and has high feasibility and practicality. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0027] Figure 1 This is a flowchart of an optimal allocation method for sintered homogenized ore based on pruning backtracking according to the present invention.

[0028] Figure 2 This is a diagram showing the ore blending scheme results of the optimal allocation method and system for sintered blended ore based on pruning backtracking according to the present invention.

[0029] Figure 3This diagram shows the results of a quadratic programming scheme for the optimal allocation method and system of sintered blended ore based on pruning backtracking, as described in this invention. Detailed Implementation

[0030] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0031] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0032] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0033] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth.

[0034] Example 1

[0035] Reference Figure 1 The first embodiment of the present invention provides a method and system for optimal allocation of sintered blended ore based on pruning backtracking, which includes the following steps.

[0036] S1: Define the content of major elements and the total proportion according to the type of ore.

[0037] The main element content includes the iron, silicon, and aluminum content of each ore. The target iron, silicon, and aluminum content for all ores are calculated separately using the following formulas:

[0038]

[0039] Where n represents the number of ore types, and n is greater than 10; M represents the content of each major element in each ore; and k represents the total proportion of each ore.

[0040] S2: Model the ore blending problem based on actual parameters.

[0041] The actual parameters include 16 types of ore and 8 batching bins. Only 2 types of ore batching bins can be changed at a time, and the entire process is divided into 5 batching operations. The ore types in the 5 batching operations cover all types of ore. Therefore, the set S = {1, 2, 3, ..., 16} is defined. |Ti|=8, |T1∩T2|=6, |T2∩T3|=6, |T3∩T4|=6, |T4∩T5|=6, |T1∪T2∪T3∪T4∪T5|=16, where Ti represents the set of elements taken in the i-th time. Find all combinations of {T1, T2, T3, T4, T5}; and the total content of each major element in each ore blend is consistent with the target iron content, target silicon content, and target aluminum content.

[0042] S3: Search for the optimal solution using a backtracking algorithm and output all ore blending schemes that meet the requirements.

[0043] The optimal solution is found by using a backtracking algorithm. Each time, two types of ore are selected from the candidate ore for blending. First, they are sorted according to their iron content, and the two ores with the highest and second highest iron content are selected and added to the current blend. Due to the capacity limitations of the feeding equipment, only two types of ore are changed each time the formula is changed.

[0044] If the current recipe has 8 types of minerals, calculate and compare the iron content, and update the optimal solution. If the current recipe has less than 8 types of minerals, select two ores from the remaining candidate ores, continue searching downwards, and output the solution.

[0045] S4: Prune and optimize the ore blending scheme.

[0046] Pruning optimization includes pruning a subtree in a certain state if it is found that a certain ore blending scheme cannot meet the capacity limit of the feeding equipment during the search process, and then no longer searching for the subtree in that state.

[0047] The convex hull criterion is used to determine the relationship between the eight selected ores and the objective function. If the objective component falls outside the convex hull, the branch is pruned directly, and the subtree of that state is no longer searched.

[0048] If a piece of equipment in a material yard fails within a short period of time during the production process, and if 7 out of 8 types of ore fail to reach the target content, the subtree in that state will be pruned directly, and the search for subtrees in that state will cease.

[0049] The objective function is

[0050]

[0051] Among them, M1, M2 and M3 are the iron, silicon and aluminum contents of 8 kinds of ores in 5 blends, and they are all 5 rows and 8 columns matrices. In this blend, the iron content is 57.95, the silicon content is 5.16 and the aluminum content is 2.17. These values ​​are determined by the total blend.

[0052] The constraints include the following two items: First, x i，j Every element in the set is greater than 0.03 and less than 0.5, and this condition holds true for any value of i from 1 to 5. Where j indicates that only 8 batching bins can be used for each batching; secondly, for n = 0, 1, ..., 15, representing 16 types of ore, we have: Where N2 represents the combination of five blends of each ore, (r, c)∈N2 n This represents a tuple in a certain combination of minerals N2.

[0053] S5: Perform secondary planning on the obtained ore blending scheme to obtain the ore blending ratio scheme.

[0054] The quadratic programming method involves using the sum of squares of the differences between the total proportion of iron, silicon, and aluminum and the target iron, silicon, and aluminum contents as the objective function, and solving this function yields the final proportioning scheme for the fifth time.

[0055] Furthermore, a sintering blended ore optimal allocation system based on pruning backtracking is provided. The above-mentioned sintering blended ore optimal allocation method based on pruning backtracking includes implementing the steps of the above method in any computer programming language, and compiling and running the program on any operating system and any hardware architecture.

[0056] This embodiment also provides a computer device applicable to the optimal allocation method of sintered blended ore based on pruning backtracking, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the power distribution area household-transformer relationship identification method proposed in the above embodiment.

[0057] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0058] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the optimal allocation method for sintered blended ore based on pruning backtracking as proposed in the above embodiments.

[0059] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0060] In summary, this invention provides an efficient search algorithm that can find the optimal solution or near-optimal solution in a short time, providing strong support for decision-making and optimization in related fields. The method of this invention fully considers practical problems such as a large number of candidate minerals and strict formula restrictions, and has high feasibility and practicality.

[0061] Example 2

[0062] Reference Figures 1-3 This is the second embodiment of the present invention, which differs from the first embodiment in that it provides a specific application to verify the feasibility and practicality of the method and system of the present invention.

[0063] Compared to Example 1, it is further known that the mixed ore in Liugang's secondary material yard consists of 16 types of ore, while there are only 8 batching silos, as shown in Table 1.

[0064] Table 1. Types of Ore and Their Main Element Contents

[0065]

[0066]

[0067] Therefore, the ore blending process needs to be divided into multiple stages to ensure that the iron and silicon content of the mixed ore is the same in each stage. Due to the limited capacity of the feeding equipment, only two minerals can be replaced each time the formula is changed. The entire process is divided into 5 stages of ore blending, which requires that the mineral types in these 5 stages cover 16 types of minerals, and that the total proportion of the 5 stages of ore blending be consistent with the target proportion. Table 2 shows the different total proportions for different types of ore.

[0068] Table 2 Different total proportions for different types of ore

[0069]

[0070]

[0071] S1: Define the content of major elements and the total proportion according to the type of ore.

[0072] The main element content includes the iron, silicon, and aluminum content of each ore. The target iron, silicon, and aluminum content for all ores are calculated separately using the following formulas:

[0073]

[0074] Where n represents the number of ore types, and n is greater than 10; M represents the content of each major element in each ore; and k represents the total proportion of each ore.

[0075] According to the formula, the target iron content for all ores is calculated to be 57.95%, the target silicon content to be 5.16%, and the target aluminum content to be 2.17%.

[0076] S2: Model the ore blending problem based on actual parameters.

[0077] The actual parameters include 16 types of ore and 8 batching bins. Only 2 types of ore batching bins can be changed at a time, and the entire process is divided into 5 batching operations. The ore types in the 5 batching operations cover all types of ore. Therefore, the set S = {1, 2, 3, ..., 16} is defined. Let |Ti| = 8, |T1∩T2| = 6, |T2∩T3| = 6, |T3∩T4| = 6, |T4∩T5| = 6, and |T1∪T2∪T3∪T4∪T5| = 16, where Ti represents the set of elements taken in the i-th iteration. Find all combinations of {T1, T2, T3, T4, T5}.

[0078] S3: Search for the optimal solution using a backtracking algorithm and output all ore blending schemes that meet the requirements.

[0079] The optimal solution is found by using a backtracking algorithm. Each time, two types of ore are selected from the candidate ore for blending. First, they are sorted according to their iron content, and the two ores with the highest and second highest iron content are selected and added to the current blend. Due to the capacity limitations of the feeding equipment, only two types of ore are changed each time the formula is changed.

[0080] If the number of mineral types in the current ore blending scheme is equal to 8, then the iron content is calculated and compared, and the optimal solution is updated; if the number of mineral types in the current ore blending scheme is less than 8, then two types of ore are selected from the remaining candidate ores, and the search continues downward to output the scheme.

[0081] S4: Prune and optimize the ore blending scheme.

[0082] Pruning optimization includes pruning the subtree of a certain ore blending scheme that cannot meet the capacity limit of the feeding equipment during the search process, without searching the subtree of that state again.

[0083] The convex hull criterion is used to determine the relationship between the eight selected ores and the objective function. If the objective component falls outside the convex hull, the branch is pruned directly and the subtree of that state is no longer searched.

[0084] If a piece of equipment in a material yard fails to meet the target iron, silicon, and aluminum content when any 7 out of 8 types of ore fail to meet the target, the subtree will be pruned and no further subtree search will be performed.

[0085] Using the algorithm of this invention, 4265 optimal solutions were found after 126.4 seconds, with a deviation of 0.1529 for each optimal solution.

[0086] The objective function formula is:

[0087]

[0088] Among them, M1, M2 and M3 are the iron, silicon and aluminum contents of 8 kinds of ores in 5 blends, and they are all 5 rows and 8 columns matrices. In this blend, the target iron content is 57.95, the target silicon content is 5.16 and the target aluminum content is 2.17. These values ​​are determined by the total blend.

[0089] The constraints include the following two items: First, x i，j Every element in the set is greater than 0.03 and less than 0.5, and this condition holds true for any value of i from 1 to 5. Where j indicates that only 8 batching bins can be used to dispense materials each time ore is mixed.

[0090] Second, for n = 0, 1, ..., 15, representing 16 types of ore, we have: Where N2 represents the combination of five blends of each ore, (r, c)∈N2 n This represents a tuple in a certain combination of minerals N2.

[0091] S5: Perform secondary planning on the obtained ore blending scheme to obtain the ore blending ratio scheme.

[0092] The quadratic programming method involves using the sum of squares of the differences between the total proportion of iron, silicon, and aluminum and the target iron, silicon, and aluminum contents as the objective function, and solving this function yields the final proportioning scheme for the fifth time.

[0093] The method of this invention yields, as follows: Figure 2 and Figure 3 The final ore blending scheme and proportioning scheme, such as Figure 2 As shown, 0-7 represents 8 batching bins, and each time 8 different ores are added to the 8 batching bins for blending, and the total iron content, silicon content and aluminum content of each blending are consistent with the target iron content, target silicon content and target aluminum content; 0-4 represents the number of blending operations of 5.

[0094] like Figure 3 As shown, it needs to be combined with Figure 2 The resulting ore blending scheme, and Figure 2 The types of ore shown correspond to different blending ratios for each ore, such as... Figure 2 (0,0) represents environmentally friendly medium powder. Figure 3 The proportion at the corresponding position is 2.999888%; Figure 3 The sum of all the environmentally friendly medium powders is 20%, which is then divided by 5 times to get 4%, corresponding to the total proportion of environmentally friendly medium powders shown in Table 2.

[0095] In summary, this invention provides a highly efficient search algorithm that, compared to traditional manual calculations, can find the optimal or near-optimal solution in a shorter time, providing strong support for decision-making and optimization in related fields. This invention also provides an optimized ore blending method based on a backtracking algorithm, fully considering practical issues such as a large number of candidate minerals and strict formulation restrictions, demonstrating high feasibility and practicality. Furthermore, the algorithm proposed in this invention can be widely applied to combinatorial optimization problems in different fields, providing an effective means to improve production efficiency, reduce costs, and optimize decision-making.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimal distribution of sintered blend based on pruning backtracking, characterized in that: The specific steps include the following: The main element content and total proportion are defined according to the type of ore; The ore blending problem is modeled based on actual parameters; The optimal solution is searched using a backtracking algorithm, and all ore blending schemes that meet the requirements are output. The ore blending scheme was pruned and optimized. The obtained ore blending scheme is then subjected to secondary planning to obtain the ore blending ratio scheme. The pruning optimization includes, during the search process, pruning directly if a certain ore blending scheme is found to be unable to meet the capacity limitations of the feeding equipment; The convex hull criterion is used to determine the relationship between the eight selected ores and the objective function. If the objective component falls outside the convex hull, the branch is pruned directly. If a piece of equipment in a material yard fails to meet the target iron, silicon, and aluminum content when any 7 out of 8 types of ore fail to meet the target, the ore should be pruned directly. The objective function is: Among them, M1, M2 and M3 are the iron, silicon and aluminum contents of 8 kinds of ores in 5 proportions, and they are all 5 rows and 8 columns matrices. In this proportion, the iron content is 57.95, the silicon content is 5.16 and the aluminum content is 2.

17. These values ​​are determined by the total proportion. The constraints include the following two: Any one element is greater than 0.03 and less than 0.5, and for any i value from 1 to 5 Where j represents that only 8 bins can be put in each time of blending; second, for n = 0, 1,..., 15, representing 16 kinds of ores, there are: Where N2 represents the combination of 5 times of blending of each kind of ore, Represents a tuple in a certain ore combination N2.

2. The pruned backtracking based sinter blend optimization method of claim 1, wherein: The main element content includes the iron, silicon, and aluminum content of each ore. The target iron, silicon, and aluminum content for all ores are calculated using the following formulas: Where n represents the number of ore types, and n is greater than 10; M represents the content of each major element in each ore; and k represents the total proportion of each ore.

3. The optimal allocation method for sintered blended ore based on pruning backtracking according to claim 2, characterized in that: The actual parameters include 16 types of ore and 8 batching bins. Only 2 types of ore batching bins can be changed each time, and the entire process is divided into 5 batching operations. The ore types in the 5 batching operations cover all types of ore. Then, the set S = {1, 2, 3, ..., 16} is defined, Ti ⊆ S, |Ti| = 8, |T1 ∩ T2| = 6, |T2 ∩ T3| = 6, |T3 ∩ T4| = 6, |T4 ∩ T5| = 6, |T1 ∪ T2 ∪ T3 ∪ T4 ∪ T5| = 16, where Ti represents the set of elements taken in the i-th operation. All combinations of {T1, T2, T3, T4, T5} are calculated. The total content of each major element in each batching operation is consistent with the target iron content, target silicon content, and target aluminum content.

4. The optimal allocation method for sintered blended ore based on pruning backtracking according to claim 3, characterized in that: The method of searching for the optimal solution using the backtracking algorithm includes selecting two types of ore from the candidate ore each time. First, the ore is sorted according to its iron content, and the two ores with the highest and second highest iron content are selected and added to the current ore mixture. Due to the capacity limitations of the feeding equipment, only two types of ore are changed each time the formula is changed. If the number of mineral types in the current ore blending scheme is equal to 8, then the iron content is calculated and compared, and the optimal solution is updated; if the number of mineral types in the current ore blending scheme is less than 8, then two types of ore are selected from the remaining candidate ores, and the search continues downward to output the scheme.

5. The optimal allocation method for sintered blended ore based on pruning backtracking according to claim 4, characterized in that: The quadratic programming involves using the sum of squares of the differences between the total proportion of iron, silicon, and aluminum and the target iron, silicon, and aluminum contents as the objective function, and solving this function yields the final proportioning scheme for the fifth time.

6. An optimal allocation system for sintered blended ore based on pruning backtracking, based on the optimal allocation method for sintered blended ore based on pruning backtracking as described in any one of claims 1-5, characterized in that: Implement the steps of any of the methods described in claims 1 to 5 using any computer programming language, and compile and run the program on any operating system and any hardware architecture.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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