Multi-target coal blending optimization method and system, electronic equipment and storage medium

Abstract

The invention provides a multi-target coal blending optimization method and system, electronic equipment and a storage medium. The method comprises the following steps: taking basic parameters of each single coal as independent variables of a multi-target optimization coal blending model; based on each independent variable, constructing each target function variable, and constructing a constraint condition of each independent variable; based on each target function variable and the corresponding weight, constructing a target function of the multi-target optimization coal blending model; the multi-objective optimization coal blending model is solved, an optimal coal blending scheme is obtained, and the coal blending scheme comprises single coal for coal blending and the coal quality of the single coal. According to the method, automatic optimization of the coal blending scheme of the coal yard is realized by constructing the multi-target optimization coal blending model, and support is provided for coal blending of the coal yard.

Description

Technical Field

[0001] The present invention relates to the field of power coal, and more specifically, to a multi-objective coal blending optimization method, system, electronic equipment and storage medium. Background Art

[0002] In recent years, the imbalance between power coal supply and demand has led to a "coal shortage." The unstable coal quality of most coal-fired power plants has resulted in increased coal consumption, reduced power generation efficiency, and excessive pollutant emissions, impacting the safe and economical operation of generators. Coal blending is a key measure for power generation companies to reduce costs, increase efficiency, and enhance their core competitiveness. It is also an effective way to address the shortage of coal available to generators, the variability of coal types, and poor operating performance. Coal blending is gaining increasing attention and is the subject of in-depth research.

[0003] Traditional coal blending for power generation can be abstracted as a mathematical programming problem within certain constraints and objective functions. These typically involve the boiler's coal quality and combustion characteristics, with the goal of minimizing the price of the blended coal. However, optimization results based solely on coal price alone cannot fully reflect the practical significance of coal blending solutions. In power plant applications, it is also important to understand the safety and environmental performance of a particular coal blending solution, taking into account multiple factors such as coal storage, transportation, pulverizing, and combustion, in order to make comprehensive decisions. Summary of the Invention

[0004] Aiming at the defect of single-objective coal blending in the prior art, the present invention proposes a multi-objective coal blending optimization method, system, electronic equipment and storage medium.

[0005] According to a first aspect of the present invention, a multi-objective coal blending optimization method is provided, comprising: The basic parameters of each single coal are used as independent variables in the multi-objective optimization coal blending model; Based on each of the independent variables, construct each objective function variable and construct a constraint condition for each of the independent variables; Constructing the objective function of the multi-objective optimization coal blending model based on each objective function variable and the corresponding weight; The multi-objective optimization coal blending model is solved to obtain an optimal coal blending scheme, wherein the coal blending scheme includes the individual coals to be blended and the coal quality thereof.

[0006] On the basis of the above technical solution, the present invention can also make the following improvements.

[0007] Optionally, the basic parameters of each single coal are used as independent variables of the multi-objective optimization coal blending model, including: According to the current coal storage situation in the coal yard and the coal storage situation in the silo, determine each type of coal and its coal quality that can be used in coal blending; The basic parameters of each single coal are obtained as independent variables of the multi-objective optimization coal blending model.

[0008] Optionally, the basic parameters of each single coal include the calorific value of each single coal Q i , volatile matter V i , ash content A i , moisture M i , sulfur content S i , ash melting point ST i , ash content and coal price P i , i Indicates the index of a single coal.

[0009] Optionally, constructing each objective function variable based on each independent variable includes: Construct all objective function variables involved in the coal blending process and form the alternative set V of factor indicators: V= { V 1 ，V 2 ，...,V n}={ P, R S , R W , R J , R Z , SO 2 ，…} Where, P, R W ，R J ，R Z , SO 2 respectively represent coal price, ignition characteristics, burnout characteristics, slagging characteristics and SO2 emission characteristics, V 1 ，V 2 ，...,V n Represents n objective function variables; According to the current actual application of coal blending, a plurality of objective function variables are selected from the factor index alternative set V as the objective function variables of the multi-objective optimization coal blending model.

[0010] Optionally, constructing a constraint condition for each of the independent variables includes: Constraints on the ratio of single coal: ∑X i =1,Xi ≥0 Constraints on heat output: Q i =f Q (X i ，Q i )≥Q A Constraints on volatile matter: V i =f V (X i ，V i )≥V A Ash content constraints: A i =f A (X i ，A i ) ≤A B Moisture constraints: M i =f M (X i ，M i )≤M B Sulfur content constraints: S i =f S (X i ，S i )≤S B Constraints on ash melting point: ST i ≥ST A Among them, X i represents the proportion of the i-th type of coal, subscript A represents the lower limit of the index, and subscript B represents the upper limit of the index. f Q (X i ，Q i )It represents the functional relationship between the proportion of single coal and calorific value, f V (X i ，V i ) It represents the functional relationship between the proportion of single coal and volatile matter, f A (X i ，A i ) It represents the functional relationship between the proportion of single coal and ash content, f M (X i ，M i ) It represents the functional relationship between the proportion of single coal and moisture, f S (X i ，S i ) It represents the functional relationship between the proportion of single coal and sulfur content, Q A Indicates the heat threshold. V A represents the volatile matter threshold, A B represents the ash threshold, M B represents the moisture threshold, S B represents the sulfur threshold, ST A Indicates the ash melting point threshold.

[0011] Optionally, constructing the objective function of the multi-objective optimization coal blending model based on each objective function variable and the corresponding weight includes: Normalize the m objective function variables selected from the factor indicator alternative set V to obtain the normalized m objective function variables V’ ={ V 1’ ，V 2’ ，...,V m’}, m≥2, m is a positive integer; Determine the weights of m objective function variables a= { a 1 ，a 2 ，....，a m}; The objective function of the multi-objective optimization coal blending model is: Z=min(a·V') .

[0012] According to a second aspect of the present invention, a multi-objective coal blending optimization system is provided, comprising: An acquisition module is used to use the basic parameters of each single coal as independent variables of the multi-objective optimization coal blending model; A construction module is used to construct each objective function variable based on each of the independent variables and to construct a constraint condition for each of the independent variables; and to construct an objective function of the multi-objective optimization coal blending model based on each of the objective function variables and corresponding weights; The solution module is used to solve the multi-objective optimization coal blending model to obtain an optimal coal blending solution, wherein the coal blending solution includes the individual coals to be blended and their coal qualities.

[0013] According to a third aspect of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the processor is configured to implement the steps of a multi-objective coal blending optimization method when executing a computer management program stored in the memory.

[0014] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer management program is stored. When the computer management program is executed by a processor, the steps of the multi-objective coal blending optimization method are implemented.

[0015] The present invention provides a multi-objective coal blending optimization method, system, electronic device, and storage medium. The method uses the basic parameters of each individual coal as independent variables of a multi-objective optimization coal blending model. Based on each independent variable, each objective function variable is constructed, along with constraints for each independent variable. Based on each objective function variable and its corresponding weight, an objective function of the multi-objective optimization coal blending model is constructed. The multi-objective optimization coal blending model is solved to obtain an optimal coal blending solution, which includes the individual coals to be blended and their quality. By constructing a multi-objective optimization coal blending model, the present invention automatically optimizes coal blending solutions for coal yards, providing support for coal blending in coal yards. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a multi-objective coal blending optimization method provided by the present invention; Figure 2 A schematic structural diagram of a multi-objective coal blending optimization system provided by the present invention; Figure 3 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention; Figure 4 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention can be arbitrarily combined with each other to form a feasible technical solution. This combination is not restricted by the sequence of steps and / or structural composition mode, but must be based on the ability of ordinary technicians in this field to implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0018] Figure 1 The present invention provides a multi-objective coal blending optimization method flow chart, such as Figure 1 As shown, the method includes: Step 1: The basic parameters of each single coal are used as independent variables of the multi-objective optimization coal blending model.

[0019] In a possible embodiment of the present invention, the basic parameters of each single coal are used as independent variables of the multi-objective optimization coal blending model, including: According to the current coal storage situation in the coal yard and the coal storage situation in the silo, determine each type of coal and its coal quality that can be used in coal blending; The basic parameters of each single coal are obtained as independent variables of the multi-objective optimization coal blending model.

[0020] It can be understood that coal blending in a coal yard means being able to automatically find the best solution in real time based on the current coal storage and combustion conditions.

[0021] In an embodiment of the present invention, an optimal coal blending solution is intelligently found based on a multi-objective optimization coal blending model.

[0022] Among them, a multi-objective optimization problem usually includes a set of Y parameters (independent variables), a set of K objective functions, and a set of L constraints. The mathematical description is as follows: miny1=( f 1( x ), f 2( x ),…, f p ( x )) maxy2=( f p+1 ( x ), f p+2 ( x ),…, f K ( x ))(p <K) Subject to: g (x)=(g1(x),g2(x),…,g L (x))≤0 Among them, x is the independent variable vector, y=y1+y2 is the target vector, the index in y1 is called the negative index, the index in y2 is called the positive index, and g(x) is the constraint condition used to limit the feasible domain.

[0023] The input parameters in coal blending include basic coal properties, mainly industrial analysis, ash content and coal price. These input parameters are also part of the constraints.

[0024] In the application scenario of intelligent coal blending, first, based on the current coal storage situation in the coal yard, including the current coal storage situation in the coal yard and the coal storage situation in the silo, each type of coal that can participate in coal blending and its coal quality are determined, and the basic parameters of each type of coal that can participate in coal blending are obtained as independent variables of the multi-objective optimization coal blending model.

[0025] In the embodiment of the present invention, the basic parameters of each single coal include the calorific value of each single coal. Q i , volatile matter V i , ash content A i , moisture M i , sulfur content S i , ash melting point ST i , ash content and coal price P i , i Indicates the index of a single coal.

[0026] Step 2: Based on each of the independent variables, construct each objective function variable and construct the constraint conditions of each of the independent variables.

[0027] In a possible embodiment of the present invention, constructing each objective function variable based on each independent variable includes: Construct all objective function variables involved in the coal blending process and form the alternative set V of factor indicators: V={ V 1 ，V 2 ，...,V n}={ P, R S , R W , R J , R Z , SO 2 ，…} Where, P, R W ，R J ，R Z , SO 2 respectively represent coal price, ignition characteristics, burnout characteristics, slagging characteristics and SO2 emission characteristics, V 1 ，V 2 ，...,V n Represents n objective function variables.

[0028] Among them, the objective function variable indicators include negative indicators and positive indicators. The positive indicators aim to be maximized, and the negative indicators aim to be minimized.

[0029] According to the current actual application of coal blending, from the alternative set of factor indicators V 0 A plurality of objective function variables are selected as objective function variables of the multi-objective optimization coal blending model.

[0030] It can be understood that based on the independent variables in the coal blending process determined in step 1, all objective function variables involved in the coal blending process are constructed based on the independent variables to form the factor index alternative set V. When subsequently optimizing coal blending, some objective function variables can be selected from the factor index alternative set V as the objective function variables of the multi-objective optimization coal blending model based on actual conditions.

[0031] Construct constraints on each independent variable: Constraints on the ratio of single coal: ∑X i =1,X i ≥0 Constraints on heat output: Q i =f Q (X i ，Q i )≥Q A Constraints on volatile matter: V i=f V (X i ，V i )≥V A Ash content constraints: A i =f A (X i ，A i ) ≤A B Moisture constraints: M i =f M (X i ，M i )≤M B Sulfur content constraints: S i =f S (X i ，S i )≤S B Constraints on ash melting point: ST i ≥ST A Among them, X i represents the proportion of the i-th type of coal, subscript A represents the lower limit of the index, and subscript B represents the upper limit of the index. f Q (X i ，Q i ) It represents the functional relationship between the proportion of single coal and calorific value, f V (X i ，V i ) It represents the functional relationship between the proportion of single coal and volatile matter, f A (X i ，A i )It represents the functional relationship between the proportion of single coal and ash content, f M (X i ，M i ) It represents the functional relationship between the proportion of single coal and moisture, f S (X i ，S i ) It represents the functional relationship between the proportion of single coal and sulfur content, Q A Indicates the heat threshold. V A represents the volatile matter threshold, A B represents the ash threshold, M B represents the moisture threshold, S B represents the sulfur threshold, ST A Indicates the ash melting point threshold.

[0032] Step 3: constructing the objective function of the multi-objective optimization coal blending model based on each objective function variable and the corresponding weight.

[0033] It is understandable that in one embodiment of the present invention, the coal blending operator can V Select m objective functions to form factor index vectors f .

[0034] Normalize the m objective function variables selected from the indicator alternative set V to obtain the normalized m objective function variables V’ ={ V 1’ ，V 2’ ，...,V m’}, m≥2, m is a positive integer; Determine the weights of m objective function variables a= { a 1 ，a 2 ，....，a m}; The objective function of the multi-objective optimization coal blending model is: Z=min(a·V') .

[0035] For example, in one embodiment of the present invention, three objectives are selected: coal price, spontaneous combustion characteristics, and SO2 emissions. All three objectives are negative indicators, that is, the goal is to minimize the factor indicator vector: f= { P, R S SO 2} And give the factor weights of each indicator to form a weight vector a： a= { a 1 ，a 2 ，a 3} (4) Normalize each indicator to eliminate the influence of dimension and obtain the normalized factor indicator vector corresponding to each scheme r j

[0036] Among them, the normalization method is a multi-objective fuzzy decision-making method.

[0037] The normalized factor index vector is multiplied by the weight vector to construct the objective function Z of the multi-objective optimization coal blending model: Z=min(a·r j ) .

[0038] Step 4: Solve the multi-objective optimization coal blending model to obtain an optimal coal blending solution, wherein the coal blending solution includes the individual coals to be blended and their coal qualities.

[0039] It can be understood that, based on the constructed multi-objective optimization coal blending model, including the independent variables, the constraints of each independent variable, and the objective function, the constructed multi-objective optimization coal blending model is solved to obtain the optimal coal blending solution. The optimal coal blending solution includes the individual coals and their coal qualities.

[0040] In an embodiment of the present invention, during the implementation of coal blending, the single coals that can participate in coal blending and their coal qualities are first determined, and the set of all feasible solutions, i.e., the decision domain, is calculated based on the constraints. Then, a suitable objective function is constructed, and the optimal coal blending solution is searched for using an optimization algorithm based on the objective function.

[0041] The objective function of coal blending is constructed by multi-objective fuzzy decision-making method, and it can be seen that all the objective functions in coal blending constitute the alternative set of factor indicators V={P, R S , R W , R J , R Z , SO2,…}where P, R W , R J , RZ , SO2 represent coal price, ignition characteristics, burnout characteristics, slagging characteristics, and SO2 emission characteristics, respectively. In practical applications, other objectives can be dynamically added or removed as needed. Suppose coal price, spontaneous combustion characteristics, and SO2 emissions are all negative indicators, meaning the goal is to minimize them.

[0042] Ultimately, the solution provides the optimal coal blending plan for coal stored in the coal yard or silo, including the loading plan, specifically the combination of individual coal types and pulverizers. The system analyzes the match between the coal yard's inventory and the blending requirements in real time, optimizes the optimal time for fuel delivery to the plant on a weekly basis, and provides guidance for fuel procurement scheduling. Furthermore, based on the coal inventory at a specific point in time, it comprehensively calculates the amount of incoming coal types and the amount of coal types to be blended daily. It then predicts the coal yard inventory and coal type mix over a period of months, enabling automated and intelligent coal blending for the power plant.

[0043] See also Figure 2 , is a multi-objective coal blending optimization system of the present invention, the system comprising: An acquisition module 201 is used to use the basic parameters of each single coal as independent variables of a multi-objective optimization coal blending model; A construction module 202 is configured to construct each objective function variable based on each of the independent variables, and to construct a constraint condition for each of the independent variables; and to construct an objective function of the multi-objective optimization coal blending model based on each of the objective function variables and corresponding weights; The solving module 203 is used to solve the multi-objective optimization coal blending model to obtain an optimal coal blending solution, wherein the coal blending solution includes the individual coals to be blended and their coal qualities.

[0044] It can be understood that the multi-objective coal blending optimization system provided by the present invention corresponds to the multi-objective coal blending optimization method provided in the aforementioned embodiments. The relevant technical features of the multi-objective coal blending optimization system can refer to the relevant technical features of the multi-objective coal blending optimization method, which will not be repeated here.

[0045] See also Figure 3 , Figure 3 Schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. Figure 3As shown, an embodiment of the present invention provides an electronic device 300, including a memory 310, a processor 320 and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, the following steps are implemented: the basic parameters of each single coal are used as independent variables of a multi-objective optimization coal blending model; based on each of the independent variables, each objective function variable is constructed, and constraints for each of the independent variables are constructed; based on each of the objective function variables and corresponding weights, an objective function of the multi-objective optimization coal blending model is constructed; the multi-objective optimization coal blending model is solved to obtain an optimal coal blending scheme, wherein the coal blending scheme includes the single coals to be blended and their coal qualities.

[0046] See also Figure 4 , Figure 4 Schematic diagram of an embodiment of a computer-readable storage medium provided by the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 400, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented: the basic parameters of each single coal are used as independent variables of the multi-objective optimization coal blending model; based on each of the independent variables, each objective function variable is constructed, and the constraint conditions of each independent variable are constructed; based on each of the objective function variables and the corresponding weights, the objective function of the multi-objective optimization coal blending model is constructed; the multi-objective optimization coal blending model is solved to obtain the optimal coal blending scheme, which includes the single coal for blending and its coal quality.

[0047] The embodiments of the present invention provide a multi-objective coal blending optimization method, system, electronic device, and storage medium. The method uses the basic parameters of each single coal as the independent variable of the multi-objective optimization coal blending model; constructs each objective function variable based on each independent variable, and constructs the constraint conditions of each independent variable; constructs the objective function of the multi-objective optimization coal blending model based on each objective function variable and the corresponding weight; solves the multi-objective optimization coal blending model to obtain the optimal coal blending solution, which includes the single coals to be blended and their coal quality. The present invention realizes the automatic optimization of coal yard coal blending solutions by constructing a multi-objective optimization coal blending model, providing support for coal blending in coal yards.

[0048] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0049] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0050] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0051] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0052] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0053] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0054] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A multi-objective coal blending optimization method, characterized in that: include: The basic parameters of each single coal are used as independent variables in the multi-objective optimization coal blending model; Based on each of the independent variables, construct each objective function variable and construct a constraint condition for each of the independent variables; Constructing the objective function of the multi-objective optimization coal blending model based on each objective function variable and the corresponding weight; The multi-objective optimization coal blending model is solved to obtain an optimal coal blending scheme, wherein the coal blending scheme includes the individual coals to be blended and the coal quality thereof.

2. The multi-objective coal blending optimization method according to claim 1 or 2, characterized in that: The basic parameters of each single coal are used as independent variables of the multi-objective optimization coal blending model, including: According to the current coal storage situation in the coal yard and the coal storage situation in the silo, determine each type of coal and its coal quality that can be used in coal blending; The basic parameters of each single coal are obtained as independent variables of the multi-objective optimization coal blending model.

3. The multi-objective coal blending optimization method according to claim 1 or 2, characterized in that: The basic parameters of each single coal include the calorific value of each single coal Q i , volatile matter V i , ash content A i , moisture M i , sulfur content S i , ash melting point ST i , ash content and coal price P i , i Indicates the index of a single coal.

4. The multi-objective coal blending optimization method according to claim 1, characterized in that: The step of constructing each objective function variable based on each independent variable includes: Construct all objective function variables involved in the coal blending process and form the alternative set V of factor indicators: V= { V 1 ，V 2 ，...,V n}={ P, R S , R W , R J , R Z , SO 2 ，…} Where, P, R W ，R J ，R Z , SO 2 respectively represent coal price, ignition characteristics, burnout characteristics, slagging characteristics and SO2 emission characteristics, V 1 ， V 2 ，...,V n Represents n objective function variables; According to the current actual application of coal blending, a plurality of objective function variables are selected from the factor index alternative set V as objective function variables of the multi-objective optimization coal blending model.

5. The multi-objective coal blending optimization method according to claim 3, characterized in that: The constraining conditions for each of the independent variables are constructed, including: Constraints on the ratio of single coal: ∑X i =1,X i ≥0 Constraints on heat output: Q i =f Q (X i ，Q i )≥Q A Constraints on volatile matter: V i =f V (X i ，V i )≥V A Ash content constraints: A i =f A (X i ，A i ) ≤A B Moisture constraints: M i =f M (X i ，M i )≤M B Sulfur content constraints: S i =f S (X i ，S i )≤S B Constraints on ash melting point: ST i ≥ST A Among them, X i represents the proportion of the i-th type of coal, subscript A represents the lower limit of the index, and subscript B represents the upper limit of the index. f Q (X i ， Q i ) It represents the functional relationship between the proportion of single coal and calorific value, f V (X i ，V i ) It represents the functional relationship between the proportion of single coal and volatile matter, f A (X i ，A i ) It represents the functional relationship between the proportion of single coal and ash content, f M (X i ，M i ) It represents the functional relationship between the proportion of single coal and moisture, f S (X i ，S i ) It represents the functional relationship between the proportion of single coal and sulfur content, Q A Indicates the heat threshold. V A represents the volatile matter threshold, A B represents the ash threshold, M B represents the moisture threshold, S B represents the sulfur threshold, ST A Indicates the ash melting point threshold.

6. The multi-objective coal blending optimization method according to claim 4, characterized in that: The objective function of the multi-objective optimization coal blending model is constructed based on each objective function variable and the corresponding weight, including: Normalize the m objective function variables selected from the factor indicator alternative set V to obtain the normalized m objective function variables V’ ={ V 1’ ，V 2’ ，...,V m’ }, m≥2, m is a positive integer; Determine the weights of m objective function variables a= { a 1 ，a 2 ，....，a m }; The objective function of the multi-objective optimization coal blending model is: Z=min(a·V') 。 7. A multi-objective coal blending optimization system, characterized in that: include: An acquisition module is used to use the basic parameters of each single coal as independent variables of the multi-objective optimization coal blending model; A construction module, configured to construct each objective function variable based on each of the independent variables, and to construct a constraint condition for each of the independent variables; and constructing an objective function of the multi-objective optimization coal blending model based on each objective function variable and the corresponding weight; The solution module is used to solve the multi-objective optimization coal blending model to obtain an optimal coal blending solution, wherein the coal blending solution includes the individual coals to be blended and their coal qualities.

8. An electronic device, comprising a memory and a processor, wherein the processor is configured to implement the steps of the multi-objective coal blending optimization method according to any one of claims 1 to 6 when executing a computer management program stored in the memory.

9. A computer-readable storage medium, characterized in that A computer management program is stored thereon, and when the computer management program is executed by the processor, the steps of the multi-objective coal blending optimization method according to any one of claims 1 to 6 are implemented.

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