Block plan generation method and equipment for blending and batching, and medium

Abstract

The invention provides a method for generating a blending Block plan. The method comprises the following steps: acquiring first type data of related materials, wherein the first type data comprises one or more of inventory data, component inspection and test data and market price data; acquiring second type data including user heap demand data; and inputting the first type of data and the second type of data into a Block plan generation model, and executing a particle swarm optimization algorithm to obtain a Block plan after the total material cost is optimized. According to the method, the particle swarm optimization algorithm is introduced into the technical field of smelting burdening, and the high-dimensional optimization problems such as blending burdening Block plan generation can be converged to the optimal solution more quickly compared with the traditional genetic algorithm by utilizing the advantages of high convergence speed, few parameters, simplicity, easiness in implementation and the like of the algorithm; and the compiling quality and efficiency of the Block plan under the multi-constraint condition can be obviously improved. And meanwhile, the material cost factor is also considered when the Block plan is generated, and the production cost can be remarkably reduced for a blending ore heap which merges a ten-thousand-ton level.

Description

Technical Field

[0001] The present application relates to the technical field of smelting batching, and more specifically, to a method, device and medium for generating a mixing batching Block plan. Background Art

[0002] The mixing of iron-containing raw materials is an important part of the production of steel enterprises. The quality of the mixed ore plays a key role in the quality of sintered ore. The Block batching plan originated in Japan and was first adopted by Baosteel in my country. Due to its strong planning, easy production organization and operation, and quality control, the Block plan has been gradually promoted after its introduction.

[0003] Block plan refers to the division of the total plan of mixing and building into multiple block plans, requiring that the ingredients of each block plan should be as similar as possible, as equal as possible in quantity and as similar in composition as possible. Since the silicon and iron contents have the most significant impact on the sintering quality, the silicon and iron contents of each block plan should be as equal as possible.

[0004] To make the composition of each block plan basically the same as that of the large pile, and to ensure that the total proportion plan of the mixed ore large pile meets the requirements, tedious calculations are required. In the prior art, the formulation of block plans is solved by manual calculation or linear programming model. However, the workload of manual calculation is large, and linear programming requires the simplification of the model. Both cannot meet the high requirements for high quality and high efficiency of the generation of mixed ore block plans, and thus cannot meet the high requirements for precise control of the quality of mixed ore.

[0005] In addition, the commonly used block division method currently only focuses on factors such as material type, weight and chemical composition. If good planning and design are not done, it may lead to huge production cost expenditures for mixed ore piles of tens of thousands of tons, thereby increasing the operating burden of the enterprise. Summary of the invention

[0006] In response to at least one defect or improvement need in the prior art, the present application provides a method, device and medium for generating a mixing and batching Block plan, which are used to improve the quality and efficiency of Block plan preparation under multiple constraints and reduce production costs.

[0007] To achieve the above objectives, in a first aspect, the present application provides a method for generating a mixing and batching Block plan, comprising:

[0008] Acquire the first type of data including one or more of inventory data, component testing data and market price data about the material;

[0009] acquiring second type of data including user stack demand data;

[0010] The first type of data and the second type of data are input into the Block plan generation model, and the particle swarm optimization algorithm is executed to obtain the Block plan after the total material cost is optimized.

[0011] Furthermore, the construction method of the Block plan generation model includes:

[0012] The Block plan generation model obtains the type and quantity range data of the materials included in each Block plan from the second type of data;

[0013] Taking the function consisting of the total weight, cost and inventory of materials included in each Block plan as the target, a Block plan generation target function of each Block plan in the Block plan generation model is established;

[0014] The Block plan generation constraint conditions of the Block plan generation model are established by taking the type and quantity range data of the materials included in each Block plan, the SiO2 content value, TFe content value of the entire mixed pile and the inventory of the materials meeting the preset conditions as constraints.

[0015] Furthermore, the Block plan generates an objective function including:

[0016]

[0017] Where n represents the number of types of materials to be used in the mixed bulk plan; represents the material weight of the i-th material used in the j-th Block plan among the multiple Block plans into which the mixed bulk plan is split by the Block plan generation model; z j represents the total weight of all materials included in the j-th Block plan divided by the total price of the materials in the j-th Block plan; c i Represents the market price data of the i-th material, 1≤i≤n.

[0018] Furthermore, the Block plan generation constraints include:

[0019] The first constraint is:

[0020] Second constraint:

[0021] The third constraint is:

[0022] The fourth constraint is:

[0023] Among them, the first constraint condition represents the value range of the types and quantities of materials used in the j-th Block plan; the second constraint condition represents that the SiO2 content value of the j-th Block plan is equal to the SiO2 content value of the mixed large pile plan; the third constraint condition represents that the TFe content value of the j-th Block plan is equal to the TFe content value of the mixed large pile plan; the fourth constraint condition represents that the weight of the used material cannot exceed the inventory of the material;

[0024] a and b represent the minimum and maximum number of types of materials used in the jth Block plan, respectively; and Respectively represent the SiO2 content value, TFe content value and H2O content value of the i-th material; and p TFe They represent the SiO2 content and TFe content of the mixed bulk pile plan respectively;

[0025] The first to fourth constraints together constitute the constraints that the j-th Block plan needs to satisfy.

[0026] Furthermore, the execution of the particle swarm optimization algorithm to obtain the Block plan after the total material cost is optimized includes:

[0027] Execute the particle swarm optimization algorithm to generate a first Block plan, and then subtract the weight of the materials used in the Block plan from the mixed bulk plan to obtain the remaining mixed bulk plan;

[0028] Repeat the above optimization process until the number of types of remaining materials meets the preset range, the optimization process ends, and finally all block plans are generated.

[0029] Furthermore, the execution of the particle swarm optimization algorithm to obtain the Block plan after the total material cost is optimized specifically includes:

[0030] Step 1, set the particle swarm size to N, set the current iteration number t = 1, and the Block number j = 1; randomly initialize the positions of N particles according to the Block plan generation constraint conditions, each particle represents a Block plan, and the position is Initialize the velocity of each particle

[0031] Step 2: Generate an objective function based on the Block plan to obtain the fitness of each particle and record the individual best position of each particle. and the global optimal position g t ;

[0032] Step 3: Update the speed and position of each particle according to the first formula, wherein the first formula includes:

[0033]

[0034]

[0035] A linear decreasing weight strategy is adopted, and the weight is updated using a second formula, wherein the second formula includes:

[0036]

[0037] Among them, t represents the current iteration number, and each iteration gets a Block, that is, t = j; T represents the maximum number of iterations; w represents the inertia weight, w init represents the initial inertia weight, w end represents the inertia weight when the iteration reaches the maximum number of iterations; r1 and r2 represent random numbers; c1 represents the individual learning factor; c2 represents the group learning factor; represents the velocity vector of particle i in the tth iteration; Represents the material weight of the i-th material used in the t-th Block plan;

[0038] Step 4: Check the position of each particle to ensure that the constraint conditions for generating the Block plan are met;

[0039] Step 5: If the new fitness of the particle is better than its current individual best position, then update the individual best position; if the new fitness of the particle is better than its current global best position, then update the global best position;

[0040] Step 6: Determine whether the preset maximum number of iterations T is reached; if so, output the global optimal position as the final solution; otherwise, return to step 3;

[0041] Step 7: Deduct the weight of the materials used in the jth Block plan from the mixed bulk plan to obtain the remaining mixed bulk plan, and determine whether the number of types of materials in the remaining mixed bulk plan is within [a, b]. If so, return to step 1 for the next round of optimization.

[0042] Furthermore, the component inspection data includes one or more of SiO2 content value, TFe content value and H2O content value.

[0043] Furthermore, the user pile demand data includes one or more of demand data on the type and quantity of materials, demand data on pile weight, demand data on SiO2 content value, demand data on TFe content value and demand data on H2O content value.

[0044] In a second aspect, the present application provides an electronic device comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the computer program is executed by the processing unit, the processing unit is enabled to perform the steps of the mixing and batching Block plan generation method described in any one of the above items.

[0045] In a third aspect, the present application provides a storage medium storing a computer program executable by an access authentication device, wherein when the computer program runs on the access authentication device, the access authentication device is enabled to execute the steps of the mixing ingredient Block plan generation method described in any one of the above items.

[0046] In general, the above technical solutions conceived by the present application can achieve the following beneficial effects compared with the prior art:

[0047] This application introduces the particle swarm optimization algorithm into the field of smelting batching technology, and utilizes the advantages of the particle swarm optimization algorithm, such as fast convergence speed, few parameters, and simple and easy-to-implement algorithm, so that the generation of high-dimensional optimization problems such as mixed batching block plans can converge to the optimal solution faster than traditional algorithms such as genetic algorithms, which can significantly improve the preparation quality and efficiency of block plans under multiple constraints, and meet the higher requirements for precise control of mixed ore quality. At the same time, this application also takes into account the material cost factor when generating the block plan, which can significantly reduce the production cost for mixed ore piles of tens of thousands of tons. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 This is a core flow chart of a method for generating a mixing and batching Block plan provided in an embodiment of the present application.

[0050] Figure 2 A block diagram of an electronic device suitable for implementing the mixing and batching Block plan generation method described above provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0052] The terms "first", "second" or "nth" in the specification, claims or the above-mentioned drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0053] As described in the background technology section of the specification, to achieve that the components of each Block plan are basically the same as those of the large pile, and to ensure that the total proportion plan of the mixed ore pile meets the requirements, cumbersome calculations are required. In the prior art, the formulation of the Block plan is solved by manual calculation or by using a linear programming model. However, the workload of manual calculation is large, and linear programming requires the model to be simplified. Both cannot meet the high requirements for the high quality and high efficiency of the generation of the mixed ore Block plan, and thus cannot meet the high requirements for the precise control of the quality of the mixed ore. In addition, the currently commonly used Block division method mainly focuses on factors such as the type, weight and chemical composition of the material. If a good planning and design is not done, it may lead to huge production cost expenses for the mixed ore pile of tens of thousands of tons, thereby increasing the operating burden of the enterprise. In view of this, the present application proposes a method, device and medium for generating a mixed batching Block plan, which is used to improve the preparation quality and preparation efficiency of the Block plan under multiple constraints and reduce production costs.

[0054] refer to Figure 1 An embodiment of the present application proposes a method for generating a mixing and batching Block plan, which mainly includes the following three core steps.

[0055] Step 1: Obtain first type data including one or more of inventory data, component inspection data, and market price data related to the material.

[0056] Specifically, the material can be an iron-containing mixed ore pile. Inventory data can be expressed as data such as the mass or weight of various materials stored in the material warehouse. The composition inspection and testing data is the inspection or testing data of the material composition. The composition inspection and testing data can specifically include SiO2 content value, TFe content value (TFe in iron ore represents total iron content. In the iron ore field, TFe is a term used to describe the total iron content in iron ore. Specifically, it represents the total content of all forms of iron elements in the ore. This indicator is an important basis for evaluating the quality of iron ore.) and H2O content value. Market price data is the price data sold for various materials in the economic market.

[0057] Step 2: Acquire the second type of data including user stack demand data.

[0058] Customers or users have certain requirements or demands for dividing a mixed ore pile into multiple blocks for independent stacking. These user pile demand data usually include material type demand data (demand data on material type and quantity), material weight demand data (demand data on mixed pile weight) and material chemical composition demand data (including demand data on SiO2 content value, demand data on TFe content value and demand data on H2O content value, etc.).

[0059] Step 3: Input the first type of data and the second type of data into the Block plan generation model, execute the particle swarm optimization algorithm, and obtain the Block plan after the total material cost is optimized.

[0060] Specifically, the Block plan generation model obtains the range [a, b] of the number of types of materials contained in each Block plan from the user's requirements for the mixed pile, that is, each Block plan contains at least a types of materials and at most b types of materials.

[0061] Taking the function consisting of the total weight, cost and inventory of materials contained in each Block plan as the target, the objective function of each Block plan in the Block plan generation model is established.

[0062] The constraints in the Block plan generation model are established based on the range [a, b] of the number of types of materials included in each Block plan, the SiO2 content value and TFe content value of the entire mixed pile, and the inventory of materials meeting the pre-set conditions.

[0063] Suppose there are n types of materials to be used in a mixed bulk plan, and the material types are numbered in sequence. The range of material type number i is [1, n]. Suppose the market price of the i-th material is c i , inventory is S i .

[0064] The block plan generation model splits the above mixed large pile plan into multiple block plans, and numbers the split block plans in sequence. Suppose the weight of the material used in the jth block plan is Using this as the decision variable, establish the objective function in, It represents the weight of the i-th material used in the j-th Block plan among the multiple Block plans that the Block plan generation model splits the mixed bulk plan into; z j It represents the total weight of all materials included in the j-th Block plan divided by the total price of the materials in the j-th Block plan.

[0065] The constraints that the j-th Block plan needs to meet are as follows:

[0066] The first constraint is:

[0067]

[0068] Second constraint:

[0069]

[0070] The third constraint is:

[0071]

[0072] The fourth constraint is:

[0073]

[0074] Among them, the first constraint condition represents the value range of the types and quantities of materials used in the j-th Block plan, and the value range is [a, b]; the second constraint condition represents that the SiO2 content value of the j-th Block plan is equal to the SiO2 content value of the mixed pile plan, and the third constraint condition represents that the TFe content value of the j-th Block plan is equal to the TFe content value of the mixed pile plan, that is, the SiO2 content value and TFe content value of the entire mixed pile must meet the user's requirements or demands for dividing a mixed ore pile into multiple Blocks for independent stacking; the fourth constraint condition represents that the weight of the used material cannot exceed the inventory of the material.

[0075] a and b represent the minimum and maximum number of types of materials used in the jth Block plan, respectively; and Respectively represent the SiO2 content value, TFe content value and H2O content value of the i-th material; and p TFeThey represent the SiO2 content and TFe content of the mixed large pile plan respectively. The first to fourth constraints mentioned above together constitute the constraints that the jth Block plan needs to meet.

[0076] In one embodiment of the present application, the optimization algorithm specifically adopts a particle swarm optimization algorithm. First, the first Block plan is generated by the particle swarm optimization algorithm, and then the weight of the materials used in the Block plan is subtracted from the mixed bulk plan (i.e., the overall plan of the mixed bulk) to obtain the remaining mixed bulk plan. Then, this optimization calculation process is repeated until the number of types of remaining materials meets the preset range, and the optimization process ends, and finally all Block plans are generated. Executing the particle swarm optimization algorithm to obtain the Block plan after the total material cost is optimized specifically includes the following seven steps.

[0077] Step 31 (initialization): Set the particle swarm size to N, set the current iteration number t = 1, and the Block number j = 1; randomly initialize the positions of N particles according to the first to fourth constraints mentioned above, each particle represents a Block plan, and the position is Initialize the velocity of each particle

[0078] Step 32 (Fitness evaluation): Calculate the fitness of each particle using the following formula: This formula is the aforementioned objective function, because t represents the current number of iterations, and each iteration will correspond to a Block, that is, t = j, and the current number of iterations is consistent with the Block number. At the same time, record the individual best position of each particle and the global optimal position g t .

[0079] Step 33 (update particle speed and position): Update the speed and position of each particle according to the following formula:

[0080]

[0081]

[0082] Among them, w is the inertia weight, and the linear decreasing weight (LDW) strategy is adopted. The weight is updated using the following formula:

[0083]

[0084] Where T is the maximum number of iterations; w init is the initial inertia weight, set to 0.9; w endis the inertia weight when the iteration reaches the maximum number of iterations, which is set to 0.4; r1 and r2 are random numbers; c1 represents the individual learning factor; c2 represents the group learning factor; represents the velocity vector of particle i in the tth iteration; Represents the material weight of the i-th material used in the t-th Block plan.

[0085] Step 34 (Constraint Check): Perform constraint check on the position of each particle to ensure that the following constraints are met:

[0086] Material quantity constraint (i.e. the first constraint mentioned above):

[0087]

[0088] SiO2 content constraint (i.e. the second constraint mentioned above):

[0089]

[0090] TFe content constraint (i.e. the third constraint mentioned above):

[0091]

[0092] User inventory constraints (i.e. the fourth constraint mentioned above):

[0093]

[0094] Step 35 (update the best position): if the new fitness of the particle is better than its current individual best position, then update the individual best position; if the new fitness of the particle is better than its current global best position, then update the global best position.

[0095] Step 36 (termination condition): determine whether the preset maximum number of iterations T is reached; if so, output the global optimal position as the final solution; otherwise, return to step 33.

[0096] Step 37 (Optimize the next Block plan): Deduct the weight of the material used in the jth Block plan from the total plan of the mixed pile to obtain the total plan of the remaining mixed pile, and determine whether the number of material types in the total plan of the remaining mixed pile is within the quantity range [a, b]; if so, return to step 31 for the next round of optimization.

[0097] This application introduces the particle swarm optimization algorithm into the field of smelting batching technology, and utilizes the advantages of the particle swarm optimization algorithm, such as fast convergence speed, few parameters, and simple and easy-to-implement algorithm, so that the generation of high-dimensional optimization problems such as mixed batching block plans can converge to the optimal solution faster than traditional algorithms such as genetic algorithms, which can significantly improve the preparation quality and efficiency of block plans under multiple constraints, and meet the higher requirements for precise control of mixed ore quality. At the same time, this application also takes into account the material cost factor when generating the block plan, which can significantly reduce the production cost for mixed ore piles of tens of thousands of tons.

[0098] Figure 2 A block diagram of an electronic device suitable for implementing the above-described method for generating a mixing and batching Block plan according to an embodiment of the present application is schematically shown. Figure 2 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0099] like Figure 2 As shown, the electronic device 1000 described in this embodiment includes: a processor 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage part 1008 to a random access memory (RAM) 1003. The processor 1001 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (for example, an application-specific integrated circuit (ASIC)), and the like. The processor 1001 may also include an onboard memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for executing different actions of the mixing and batching Block plan generation method process according to an embodiment of the present application.

[0100] In RAM 1003, various programs and data required for the operation of system 1000 are stored. Processor 1001, ROM 1002 and RAM 1003 are connected to each other via bus 1004. Processor 1001 performs various operations of the mixing and batching Block plan generation method flow according to the embodiment of the present application by executing the programs in ROM 1002 and / or RAM 1003. It should be noted that the program can also be stored in one or more memories other than ROM 1002 and RAM 1003. Processor 1001 can also perform various operations of the mixing and batching Block plan generation method flow according to the embodiment of the present application by executing the programs stored in the one or more memories.

[0101] According to an embodiment of the present application, the electronic device 1000 may further include an input / output (I / O) interface 1005, which is also connected to the bus 1004. The system 1000 may further include one or more of the following components connected to the I / O interface 1005: an input part 1006 including a keyboard, a mouse, etc.; an output part 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage part 1008 including a hard disk, etc.; and a communication part 1009 including a network interface card such as a LAN card, a modem, etc. The communication part 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that the computer program read therefrom is installed into the storage part 1008 as needed.

[0102] According to the process of the mixing and batching Block plan generation method of the embodiment of the present application, it can be implemented as a computer software program. For example, the embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the mixing and batching Block plan generation method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1009, and / or installed from the removable medium 1011. When the computer program is executed by the processor 1001, the above-mentioned functions defined in the system of the embodiment of the present application are executed. According to the embodiment of the present application, the systems, devices, means, modules and / or units described above can be implemented by computer program modules.

[0103] The embodiments of the present application also provide a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the steps of the mixing and batching Block plan generation method according to the embodiments of the present application can be implemented.

[0104] According to an embodiment of the present application, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In an embodiment of the present application, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus, or a device. For example, according to an embodiment of the present application, the computer-readable storage medium may include one or more memories other than the ROM 1002 and / or RAM 1003 described above.

[0105] It should be noted that the functional modules in the various embodiments of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product.

[0106] The flowchart and / or block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flowchart and / or block diagram can represent a part of a module, program segment or code, and a part of the above-mentioned module, program segment or code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. It should also be noted that each box in the block diagram or flowchart, and the combination of the boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0107] Those skilled in the art will appreciate that the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present application. In particular, without departing from the spirit and teachings of the present application, the technical features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways, and all of these combinations and / or combinations fall within the scope of the present application.

[0108] Although the present application has been shown and described with reference to specific exemplary embodiments of the present application, it should be understood by those skilled in the art that various changes in form and details may be made to the present application without departing from the spirit and scope of the present application as defined by the appended claims and their equivalents. Therefore, the scope of the present application should not be limited to the above-mentioned embodiments, but should be determined not only by the appended claims, but also by the equivalents of the appended claims.

Claims

1. A method for generating a mixing and batching Block plan, characterized in that: include: Acquire the first type of data including one or more of inventory data, component testing data and market price data about the material; acquiring second type of data including user stack demand data; The first type of data and the second type of data are input into the Block plan generation model, and the particle swarm optimization algorithm is executed to obtain the Block plan after the total material cost is optimized.

2. The method for generating a mixing and batching Block plan according to claim 1, characterized in that: The construction method of the Block plan generation model includes: The Block plan generation model obtains the type and quantity range data of the materials included in each Block plan from the second type of data; Taking the function consisting of the total weight, cost and inventory of materials included in each Block plan as the target, a Block plan generation target function of each Block plan in the Block plan generation model is established; The Block plan generation constraint conditions of the Block plan generation model are established by taking the material type and quantity range data contained in each Block plan, the SiO2 content value, TFe content value of the entire mixed pile and the material inventory meeting the preset conditions as constraints.

3. The method for generating a mixing and batching Block plan according to claim 2, characterized in that: The Block plan generates an objective function including: Where n represents the number of types of materials to be used in the mixed bulk plan; represents the material weight of the i-th material used in the j-th Block plan among the multiple Block plans into which the mixed bulk plan is split by the Block plan generation model; z j represents the total weight of all materials included in the j-th Block plan divided by the total price of the materials in the j-th Block plan; c i Represents the market price data of the i-th material, 1≤i≤n.

4. The method for generating a mixing and batching Block plan according to claim 3, characterized in that: The Block plan generation constraints include: The first constraint is: Second constraint: The third constraint is: The fourth constraint is: Among them, the first constraint condition represents the value range of the types and quantities of materials used in the j-th Block plan; the second constraint condition represents that the SiO2 content value of the j-th Block plan is equal to the SiO2 content value of the mixed large pile plan; the third constraint condition represents that the TFe content value of the j-th Block plan is equal to the TFe content value of the mixed large pile plan; the fourth constraint condition represents that the weight of the used material cannot exceed the inventory of the material; a and b represent the minimum and maximum number of types of materials used in the jth Block plan, respectively; and Respectively represent the SiO2 content value, TFe content value and H2O content value of the i-th material; and p TFe They represent the SiO2 content and TFe content of the mixed bulk pile plan respectively; The first to fourth constraints together constitute the constraints that the j-th Block plan needs to satisfy.

5. The method for generating a mixing and batching Block plan according to claim 4, characterized in that: The block plan after executing the particle swarm optimization algorithm to obtain the optimized total material cost includes: Execute the particle swarm optimization algorithm to generate a first Block plan, and then subtract the weight of the materials used in the Block plan from the mixed bulk plan to obtain the remaining mixed bulk plan; Repeat the above optimization process until the number of types of remaining materials meets the preset range, the optimization process ends, and finally all block plans are generated.

6. The method for generating a mixing and batching Block plan according to claim 5, characterized in that: The execution of the particle swarm optimization algorithm to obtain the Block plan after the total material cost is optimized specifically includes: Step 1, set the particle swarm size to N, set the current iteration number t = 1, and the Block number j = 1; randomly initialize the positions of N particles according to the Block plan generation constraint conditions, each particle represents a Block plan, and the position is Initialize the velocity of each particle Step 2: Generate an objective function based on the Block plan to obtain the fitness of each particle and record the individual best position of each particle. and the global optimal position g t ; Step 3: Update the speed and position of each particle according to the first formula, wherein the first formula includes: A linear decreasing weight strategy is adopted, and the weight is updated using a second formula, wherein the second formula includes: Among them, t represents the current iteration number, and each iteration gets a Block, that is, t = j; T represents the maximum number of iterations; w represents the inertia weight, w init represents the initial inertia weight, w end represents the inertia weight when the iteration reaches the maximum number of iterations; r1 and r2 represent random numbers; c1 represents the individual learning factor; c2 represents the group learning factor; represents the velocity vector of particle i in the tth iteration; Represents the material weight of the i-th material used in the t-th Block plan; Step 4: Check the position of each particle to ensure that the constraint conditions for generating the Block plan are met; Step 5: If the new fitness of the particle is better than its current individual best position, then update the individual best position; if the new fitness of the particle is better than its current global best position, then update the global best position; Step 6: Determine whether the preset maximum number of iterations T is reached; if so, output the global optimal position as the final solution; otherwise, return to step 3; Step 7: Deduct the weight of the materials used in the jth Block plan from the mixed bulk plan to obtain the remaining mixed bulk plan, and determine whether the number of types of materials in the remaining mixed bulk plan is within [a, b]. If so, return to step 1 for the next round of optimization.

7. The method for generating a mixing and batching Block plan according to any one of claims 1 to 6, characterized in that: The component inspection data includes one or more of SiO2 content value, TFe content value and H2O content value.

8. The method for generating a mixing and batching Block plan according to any one of claims 1 to 6, characterized in that: The user pile demand data includes one or more of demand data on the type and quantity of materials, demand data on pile weight, demand data on SiO2 content value, demand data on TFe content value and demand data on H2O content value.

9. An electronic device, characterized in that: It includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the computer program is executed by the processing unit, the processing unit is able to execute the steps of the mixing and batching Block plan generation method described in any one of claims 1-8.

10. A storage medium, characterized in that: It stores a computer program executable by an access authentication device. When the computer program runs on the access authentication device, the access authentication device can execute the steps of the mixing and batching Block plan generation method described in any one of claims 1 to 8.

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