A method for generating mining plans based on fruit fly optimization algorithm

Abstract

The present invention discloses a method for generating a mining scheme based on a fruit fly optimization algorithm, including constructing a three-dimensional model of each mining block according to a mining exploration engineering distribution map, constructing a mathematical model of the mining block according to the three-dimensional model of the mining block; dividing each mining block three-dimensional model into cubic mining block three-dimensional models of the same size, and constructing a mathematical model of the mining block according to the mining block three-dimensional model; setting initialization parameters of the fruit fly optimization algorithm; constructing a fitness function, constraint conditions and priority principle of the fruit fly optimization algorithm according to the mining block mathematical model and initialization parameters; running the fruit fly optimization algorithm, iteratively searching until the iteration termination condition is met, outputting the optimal fruit fly individual position as the optimal mining scheme, and using it to guide actual mining work. The present invention comprehensively considers factors such as mining volume, mining cost and market price, and generates an optimal mining scheme through multi-objective optimization search, so as to maximize mining benefits, and has broad application prospects.

Description

Technical Field

[0001] The invention relates to the technical field of mine exploitation schemes, and in particular to a method for generating a mine exploitation scheme based on a fruit fly optimization algorithm. Background Art

[0002] In the traditional mining process, mining plans are mainly based on manual experience and historical data. It is difficult to comprehensively consider multiple factors such as mining volume, ore type and grade, market price fluctuations and mining sequence. As a result, mining plans often only focus on certain aspects, leading to problems such as high mining costs, low mining efficiency and unreasonable mining sequence.

[0003] The fruit fly optimization algorithm is an emerging swarm intelligence optimization algorithm based on the bionic principle of fruit fly foraging behavior. The formulation of a mining plan is a complex optimization problem that requires consideration of multiple factors, such as mining volume, mining cost, and mining sequence. Traditional machine learning optimization methods often find it difficult to fully consider these factors and are prone to falling into local optimal solutions. The fruit fly optimization algorithm, as a global optimization algorithm, can better solve this problem. Summary of the invention

[0004] The purpose of the present invention is to provide a method for generating a mine mining plan based on a fruit fly optimization algorithm to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for generating a mining plan based on a fruit fly optimization algorithm, comprising:

[0007] S1: constructing a three-dimensional model of each ore block according to the mine exploration project distribution map, and constructing a mathematical model of the ore block according to the three-dimensional model of the ore block, wherein the mine is composed of a plurality of the ore blocks;

[0008] S2: Divide the three-dimensional models of each ore block in S1 into three-dimensional cubic ore block models of the same size, and construct a mathematical model of the ore block based on the three-dimensional ore block models, wherein each ore block is composed of a plurality of the ore blocks;

[0009] S3: Set the initialization parameters of the fruit fly optimization algorithm;

[0010] S4: construct the fitness function of the fruit fly optimization algorithm based on the mathematical model of the ore block in S2 and the initialization parameters in S3;

[0011] S5: setting the constraints and priority principles of the fruit fly optimization algorithm according to the mathematical model of the ore block in S2 and the initialization parameters in S3;

[0012] S6: Run the fruit fly optimization algorithm, calculate the fruit fly fitness value through the fitness function according to the fruit fly position, update the fruit fly position, update the global optimal fitness value, iterate the search until the iteration termination condition is met, and output the optimal fruit fly individual position as the optimal mining plan;

[0013] S7: Apply the mining plan obtained in S6 to actual mining work.

[0014] Further preferably, in S1, the mathematical model of the ore block is as follows:

[0015]

[0016]

[0017] in, Represents all mining blocks, Indicates the number of mining blocks. Indicates Mineral blocks, Indicates The number of the mining block, Indicates The boundary line of each mining block, Indicates The ore density of each ore block, Indicates The type of mineral material in each mining section, Indicates The ore grade of a mining block.

[0018] Further preferably, in S2, the size of the cubic ore block is the average mining volume of a mining team in one shift.

[0019] Further preferably, in S2, the mathematical model of the ore block is as follows:

[0020]

[0021] in, Indicates A nugget, Indicates The block number of the block, represents the side length of the cube block, Indicates The three-dimensional coordinates of the center point of each ore block, Indicates The density of the ore in a block, Indicates The type of mineral material in each block, Indicates The mineral grade of each ore block, Indicates the total number of blocks.

[0022] Further preferably, in S3, the initialization parameters include the fruit fly population size The maximum number of iterations is , search space , the initialization position of each fruit fly is any mine block, and the global optimal fitness value is initialized to 0;

[0023] in, Indicates from Select from different elements The number of permutations of elements, Represents the planning cycle of the mining plan, The number of blocks planned to be mined in the mine mining plan. Indicates the total number of blocks.

[0024] Further preferably, in S4, the fitness function is as follows:

[0025]

[0026]

[0027] in, represents the position of the fruit fly, Indicates A nugget, The number of blocks planned to be mined in the mine mining plan. represents the fitness function of the fruit fly optimization algorithm, represents the side length of the cube block, represents the normalized unit location cost, Indicates The density of the ore in a block, Indicates The mineral grade of each ore block, Indicates The mineral material type corresponding to each ore block At the selling price corresponding to the planned mining date, Indicates Mineral Blocks The three-dimensional coordinates of the center point position, represents the normalized unit floating cost.

[0028] Further preferably, in S5, the constraint condition is as follows:

[0029]

[0030] in, and Belongs to the fruit fly position Any two blocks contained in and Respectively and The order of mining, and Respectively and Center point The absolute value of the axis coordinate, and Respectively and Center point The absolute value of the axis coordinate.

[0031] Further preferably, in S5, the priority principle is that when the fitness values ​​corresponding to any two fruit fly positions are consistent, the fruit fly with the least number of mining blocks involved is preferentially selected, which is expressed by the following formula:

[0032]

[0033] in, represents the fruit fly population, and represents any two fruit fly positions, express The fitness value of express The fitness value of express The cardinality of the included mining blocks, express The cardinality of the included mining blocks, To find the minimum function, Represents a selection operator.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention constructs a mine into a three-dimensional model of a mine section block through an exploration engineering distribution map, further constructs the three-dimensional model of the mine section block into a three-dimensional model of a mine block, and respectively establishes mathematical models of the mine section block and the mine block. The fruit fly optimization algorithm is introduced to simulate the problem of formulating a mining plan as the foraging behavior of fruit flies, and the initialization parameters of the optimization algorithm are set. The fitness function, constraint conditions and priority principle of the optimization algorithm are determined according to the mathematical models of the mine section block and the mine block. The designed fruit fly optimization algorithm is used to perform multi-objective optimization search on the solution space to find the optimal mining plan for guiding mining work.

[0036] The cubic block size proposed in the present invention is the average mining volume of a mining team, which controls the granularity of the mining plan to the standard minimum unit and is consistent with the granularity of actual mining work, thereby realizing the refined management of mining plan formulation and mining work implementation.

[0037] The fitness function of the fruit fly optimization algorithm designed in the present invention comprehensively considers factors such as the number of mining blocks, mining sequence, mineral price, floating cost and location cost, and dynamically searches and generates the optimal mining plan based on the mining cost and market price to maximize the mining efficiency.

[0038] The constraints and priority principles of the fruit fly optimization algorithm designed by the present invention are designed from top to bottom and from front to back mining constraints based on actual mining work rules. In addition, based on actual mining costs, a priority principle of giving priority to mining the same ore block is designed. Both the constraints and priority principles are consistent with actual mining rules, so that the output mining plan has more practical guiding value.

[0039] In summary, the method of generating mining plans based on the fruit fly optimization algorithm has broad application prospects and important practical significance. It can realize the global optimization search of mining plan problems and provide strong support for mining work. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention is a flow chart of a method for generating a mine mining plan based on a fruit fly optimization algorithm. DETAILED DESCRIPTION

[0041] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0042] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0043] Embodiment 1: As attached Figure 1 As shown, this embodiment provides a method for generating a mining plan based on a fruit fly optimization algorithm, comprising the following steps:

[0044] S1: constructing a three-dimensional model of each ore block according to the mine exploration project distribution map, and constructing a mathematical model of the ore block according to the three-dimensional model of the ore block, wherein the mine is composed of a plurality of the ore blocks;

[0045] A mine is composed of multiple mining blocks. The mine exploration project distribution map contains information such as the mining block number, boundary line, ore density, ore type and ore grade of each mining block.

[0046] The present invention adopts Unity 3D modeling tool, utilizes the coordinate system of mine exploration engineering distribution map and combines the geographical location boundary of mine block to build the 3D model of mine block.

[0047] Assume that after the model is built, A mining block model, then The block model of a mining section is expressed as follows:

[0048]

[0049] in, Represents all mining blocks, Indicates Mineral blocks, Indicates the number of mining blocks.

[0050] Each ore block contains five attribute information: ore block number, boundary line, ore density, ore type and ore grade. The boundary line is composed of a series of boundary points, the boundary points are composed of three-dimensional coordinates, the ore type is a set enumeration sequence, and the ore grade is expressed in mass percentage (%).

[0051] The mathematical model of the ore block is expressed as follows:

[0052]

[0053] in, Indicates The numbering of each mining block is based on the set rules and is not repeated. Indicates The boundary line of each mining block, Indicates The ore density of each mining block, Indicates The type of mineral material in each mining section, Indicates The ore grade of a mining block.

[0054] S2: Divide the three-dimensional models of each ore block in S1 into three-dimensional cubic ore block models of the same size, and construct a mathematical model of the ore block based on the three-dimensional ore block models, wherein each ore block is composed of a plurality of the ore blocks;

[0055] According to the historical mining data of the mine, the average mining volume of a mining team is obtained. The unit of measurement of mining volume is cubic meter / shift. The mining volume is converted into a cube structure, and the side length of the cube is recorded as ,Right now is the side length of the cubic nugget.

[0056] Based on the coordinate system of the 3D model of the mining section, the 3D model in the SI step is divided into 3D models of cubic mining blocks of the same size, and the boundary and position information of the divided mining blocks are determined by using the polyhedron intersection and projection algorithm. That is, the mining section consists of multiple mining blocks.

[0057] The 3D model of the ore block is divided into cubes of the same size. The 3D model of the ore block is realized by the Unity 3D modeling tool, and the side length of the cube is set to , start the tool to automatically divide, and after completion, output the relevant divided information.

[0058] The attribute information of the ore block includes the ore block number, the center point position, the eight vertex positions, the ore density, the ore type and the ore grade.

[0059] The block number is numbered according to the set rules, and the block numbers under each mining section block are not repeated. The ore density, ore material category and ore material grade of the ore block are based on the corresponding attribute information of the mining section block that intersects with the ore block, that is, the ore density, ore material category and ore material grade of the ore block are the corresponding ore density, ore material category and ore material grade information of the mining section block.

[0060] Assumptions After the mining blocks are divided into mining blocks, A block of ore, The ore block is expressed as follows:

[0061]

[0062] in, Represents all mineral blocks, Indicates A nugget, Indicates the total number of blocks.

[0063] No. Mineral Blocks The mathematical model is expressed as:

[0064]

[0065] in, Indicates The block number of the block, Indicates The three-dimensional coordinates of the center point of each block, Indicates The density of the ore in a block, Indicates The type of mineral material in each block, Indicates The mineral grade of each ore block, Indicates The three-dimensional coordinates of the eight vertices of the block, Indicates the side length of a cube block.

[0066] In the above formula, the three-dimensional coordinates of the eight vertex positions can be calculated through the three-dimensional coordinates of the center point and the side length of the cube ore block, so the above formula can be simplified to:

[0067]

[0068] S3: Set the initialization parameters of the fruit fly optimization algorithm;

[0069] The planning cycle of the mining plan is set as , in days, and a maximum of three shifts can be carried out each day. From the description of step S1, we know that each shift can mine one ore block, so the cycle The maximum amount that can be mined is Mineral blocks, it is agreed that at least one mineral block will be mined, that is .

[0070] Assumption cycle Mined within Mineral blocks, . Set the fruit fly population to The maximum number of iterations is , search space , the initialization position of each fruit fly is any mine block, and the global optimal fitness value is initialized to 0. Among them, Indicates from Select from different elements The number of permutations of elements.

[0071] S4: construct the fitness function of the fruit fly optimization algorithm based on the mathematical model of the ore block in S2 and the initialization parameters in S3;

[0072] The market price of the mined ore is floating, which is related to factors such as ore type, ore grade and market supply and demand. The present invention focuses on the floating changes in the market price of different ore types and converts the periodic The price of internal ore is expressed as the following two-dimensional matrix:

[0073]

[0074] in, It represents the selling price of a certain type of mineral material on a certain day in period T. Indicates Type of mineral The normalized unit price of the day, .

[0075] Mining The revenue of a block can be expressed as:

[0076]

[0077] in, Indicates mining The total revenue of each block, Indicates The selling price of the mineral material category corresponding to each block on the planned mining date.

[0078] The cost of mining can be generally divided into fixed costs, floating costs and location costs. Fixed costs refer to costs that do not change with the increase in the amount of mineral materials mined, such as exploration, wages, equipment depreciation, land and management costs. Floating costs refer to costs that change with the increase in the amount of mineral materials mined, and are proportional to the amount of mining, such as energy, manpower and equipment losses. Location costs refer to the cost changes caused by different mining locations under the same mining volume. For example, when the mining depth increases, the costs of energy consumption, mineral transportation and ventilation will increase.

[0079] Because fixed costs do not change with the amount of mining, the impact on the mining efficiency of the mine is fixed. Therefore, this paper only considers floating costs and location costs. Among them, floating costs are proportional to the amount of mining, that is, floating costs are linearly proportional to the number of mined blocks. Assume that the normalized unit floating cost is expressed as Indicates that mining The total floating cost of a block can be expressed as:

[0080]

[0081] The location cost is related to the mining location. The present invention reflects the location cost relationship by the distance between the center point of the ore block and the coordinate origin. Assuming that the normalized unit location cost is Indicates that mining The total location cost of a block can be expressed as:

[0082]

[0083] in Indicates Mineral Blocks The three-dimensional coordinates of the center point position.

[0084] Through the above analysis, mining The benefit of a block can be reflected by deducting the total floating cost and the total location cost from the total revenue, which can be expressed as follows:

[0085]

[0086] The problem of developing a mining plan is simulated as the foraging behavior of fruit flies. To develop a mining plan is to assume that Find within blocks and specify the mining order of the blocks to maximize the overall mining efficiency.

[0087] Discrete modeling of the fruit fly optimization algorithm. For each fruit fly , each iteration searches for The mining blocks and mining order, as well as the corresponding mining benefits. That is, the fruit fly position represents the mining plan, and the fitness value calculated by the fruit fly position represents the mining benefit. The optimal mining plan is the mining plan represented by the fruit fly position with the largest fitness value.

[0088] Therefore, the fruit fly position, i.e., the mining plan, can be expressed as:

[0089]

[0090] in, represents the position of the fruit fly, Indicates A nugget, The number of ore blocks planned to be mined in the mine mining plan.

[0091] Therefore, the fitness function of the fruit fly optimization algorithm is designed as:

[0092]

[0093] in Represents the fitness function of the fruit fly optimization algorithm.

[0094] S5: setting the constraints and priority principles of the fruit fly optimization algorithm according to the mathematical model of the ore block in S2 and the initialization parameters in S3;

[0095] Mining has a certain mining order, which is carried out from top to bottom and from front to back according to the geographical location. Therefore, for any mining plan of a block searched by a fruit fly, for any two blocks, the center point position of the block mined later is Coordinates and The coordinates must be greater than the center point of the first mined block. Coordinates and Coordinates. The above constraints are expressed by the following formula:

[0096]

[0097] in, and Belongs to the fruit fly position Any two blocks contained in and Indicates the mining order of the ore blocks, that is The mining sequence is later than The order of mining, and Respectively and Center point The absolute value of the axis coordinate, and Respectively and Center point The absolute value of the axis coordinate.

[0098] After each iteration is completed, when the fitness value of the fruit fly is calculated through the fitness function according to the position of the fruit fly, it is also necessary to determine whether the above constraints are met, and then select the optimal fruit fly individual if the above constraints are met.

[0099] Mining must also follow the principle of priority, that is, priority should be given to mining the same mining block. The ore types and grades of the same mining block are often consistent, and the mining cost and production cost are relatively lower.

[0100] However, the priority principle is not a constraint condition, that is, when the fitness values ​​corresponding to the positions of two fruit flies are the same, the fruit fly with the least number of mining blocks involved is preferred. The above priority principle is expressed by the following formula:

[0101]

[0102] in, represents the fruit fly population, and represents any two fruit fly positions, express The fitness value of express The fitness value of express The cardinality of the included mining blocks, express The cardinality of the included mining blocks, To find the minimum function, Represents a selection operator.

[0103] S6: Run the fruit fly optimization algorithm, calculate the fruit fly fitness value through the fitness function according to the fruit fly position, update the fruit fly position, update the global optimal fitness value, iterate the search until the iteration termination condition is met, and output the optimal fruit fly individual position as the optimal mining plan;

[0104] After each iteration, the fitness value of each fruit fly is calculated through the fitness function according to the position of the fruit fly, and then the fitness values ​​of each fruit fly are compared to obtain the maximum fitness value, that is, the optimal fruit fly individual and the fitness value of the optimal fruit fly individual are selected, which is expressed as the following formula:

[0105]

[0106] in, represents the fitness value of the optimal fruit fly individual, and its corresponding fruit fly is the optimal fruit fly individual. Represents the maximum value function.

[0107] Assign the position of the best fruit fly to other fruit flies Individuals, do further optimization searches.

[0108] Comparing the fitness values ​​of the best fruit fly individuals And the global optimal fitness value ,if , then Pay , update the global optimal fitness value.

[0109] Reached the number of iterations After that, the output of the optimal fruit fly individual position is the optimal mining plan, and the global optimal fitness value is the corresponding maximum mining benefit. After completion, exit the algorithm.

[0110] S7: Apply the mining plan obtained in S6 to actual mining work.

[0111] The optimal mining plan obtained in S6 is used in actual mining work to guide the actual mining work.

[0112] The following is a description of the experimental process of the specific implementation method. The three-dimensional model of the ore block is constructed based on the distribution map of the mine exploration project, and the three-dimensional model of the ore block is constructed based on the three-dimensional model of the ore block. The statistical analysis of the historical mining data of the mine shows that the mining volume of each mining shift is 1565m 3 , converting the excavated volume into a cubic structure, the side length of the cube is obtained to be 11.61m.

[0113] In the 3D modeling software, the side length of the ore block is configured, that is, the side length of the cube is 11.61, and the 3D modeling software is used to divide the 3D model of the ore section block into ore block models of set sizes. The ore block data after division are shown in Table 1 below.

[0114] Table 1: Mineral block data table after division

[0115]

[0116] As can be seen from Table 1, each row of data represents a mineral block, which specifically includes five attribute information: mineral block number, center point location, ore density, mineral material category and mineral material grade. A total of 34 mineral blocks are divided.

[0117] Among them, the block number is composed of three parts, separated by "." in the middle. The first part is the fixed character "NU", which represents the block identification. The middle part is composed of three digits, indicating the block number of the mine section. The third part is composed of four digits, indicating the number. It is required that the number under each mine section block cannot be repeated.

[0118] The center point position is composed of three coordinates in a three-dimensional coordinate system, namely the x-axis, y-axis, and z-axis coordinates, separated by "," in the middle, and the unit is m.

[0119] The unit of ore density is t / m 3 . The grade of mineral materials is expressed as a percentage. The mineral material category consists of three parts, separated by ".". The first part is the fixed character "CL", which indicates the mineral material category identification. The second part consists of two digits, indicating the major category number, such as iron, copper, and aluminum. The third part consists of three digits, indicating the sub-category number under the major category. For example, the major category of iron includes magnetite, hematite, limonite and other sub-categories.

[0120] As can be seen from Table 1, the 34 ore blocks involve 3 ore blocks, which are numbered 013, 004 and 005, respectively. 013 is divided into 14 ore blocks, and 004 and 005 are divided into 10 ore blocks. From the ore density column, it can be seen that the ore density of the three ore blocks is significantly different. From the ore category column, it can be seen that the 34 ore blocks all belong to a large category of ore numbered 03.

[0121] The planned mining period is set to 7 days, specifically from March 5 to March 11. "3.5" is used to represent the date March 5, and other dates are represented by analogy. The selling price of each type of mineral material in the planned period is collected and sorted out, as shown in Table 2 below.

[0122] Table 2: Price list of each type of mineral material during the planning period

[0123]

[0124] As can be seen from Table 2, each row represents the selling price of a certain type of mineral material on different dates, in units of RMB / ton. There are a total of 7 mineral material categories involved, and all 7 mineral material categories belong to the 03 major category. From the data in the table, it can be observed that the selling price of each type of mineral material has obvious changes during the planning period. For example, the selling price of the mineral material of category CL.03.003 increased significantly on March 6 and March 7, and the selling price decreased significantly starting from March 8 and thereafter.

[0125] The normalized unit floating cost is obtained through statistical analysis of historical mining and sales data. The normalized unit location cost is 98 yuan / ton. It is 520 yuan / ton.

[0126] Set the fruit fly population to 60 and the maximum number of iterations to , Indicates the number of blocks mined during the mining cycle, search space .

[0127] According to the fruit fly optimization algorithm designed in the embodiment and the initialization parameters of the optimization algorithm set, the optimization algorithm is run, and the iterative search outputs the optimal fruit fly individual position, that is, the optimal mining plan, after satisfying the iteration termination condition, as shown in Table 3 below.

[0128] Table 3: Optimal mining plan

[0129]

[0130] As shown in Table 3, within the 7-day mining cycle, 18 blocks were mined in total. The algorithm gives the mined blocks corresponding to each mining date. The 1: / 2: / 3: before the block number indicates the mining order of the blocks. For example, on March 6, three blocks were planned to be mined, that is, three shifts were carried out, each shift mined one block, and the mining order was NU.005.0003 to NU.005.0004 and finally NU.005.0005.

[0131] It can be seen from the algorithm design of the embodiment that during the mining cycle, the fruit fly position is calculated according to the fitness function, and the constraints and priority principles must be met to update the fruit fly position and the global optimal fitness value. The size of the fitness value is related to the center point position of the ore block, the ore density, the ore grade and the ore price. For example, the ore block closest to the origin is mined first, or the ore with an increased price is mined first, or the ore block with a larger ore density is mined first, etc., and a multi-objective optimization search is performed to output the optimal mining plan.

[0132] Combined with Table 2, it can be seen that on March 8 and March 9, the selling prices of mineral materials classified as CL.03.007, CL.03.008 and CL.03.009 increased significantly, the selling prices of mineral materials classified as CL.03.003 and CL.03.004 decreased significantly, and the selling prices of mineral materials classified as CL.03.012 and CL.03.013 remained basically unchanged.

[0133] Combined with Table 1, it can be seen that among the three types of CL.03.007, CL.03.008 and CL.03.009, the CL.03.007 type has the highest ore density. Furthermore, compared with the CL.03.008 and CL.03.009 types of ore, the center point of the ore block corresponding to the CL.03.007 type of ore is closer to the origin.

[0134] Through the above analysis, on March 8 and 9, the mining blocks NU.013.0010, NU.013.0011, NU.013.0012, NU.013.0013, NU.013.0014, and NU.013.0015 were mined sequentially, and full load mining was carried out every day, that is, three shifts were carried out every day, and the mining efficiency generated was maximized.

[0135] For example, on March 10 and March 11, the price of mineral materials remained basically unchanged, but the NU.013.0019 ore block started to be farther away from the origin, while the NU.004.0002 and NU.004.0003 ore blocks were closer to the origin, and the corresponding mineral materials had better grade and greater ore density. Therefore, the mining plan on March 11 was adjusted to NU.004.0002 and NU.004.0003 to maximize the mining efficiency.

[0136] When determining whether it is the optimal fruit fly position, the constraints and priority principles must also be met, that is, the mining order from top to bottom and from front to back, as well as the priority principle of giving priority to the same mining block. As can be seen from Table 3, according to the mining date sequence, the z-axis coordinates and y-axis coordinates of the center point position of the mining block both meet the rules from top to bottom and from front to back. At the same time, the priority principle of giving priority to the same mining block is also met, and at least the mining block is not replaced within one mining date.

[0137] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting from any point of view, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any reference numerals in the claims should not be regarded as limiting the claims involved.

[0138] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for generating a mining plan based on a fruit fly optimization algorithm, characterized in that: The following steps are involved: S1: constructing a three-dimensional model of each ore block according to the mine exploration project distribution map, and constructing a mathematical model of the ore block according to the three-dimensional model of the ore block, wherein the mine is composed of a plurality of the ore blocks; S2: Divide the three-dimensional models of each ore block in S1 into three-dimensional cubic ore block models of the same size, and construct a mathematical model of the ore block based on the three-dimensional ore block models, wherein each ore block is composed of a plurality of the ore blocks; S3: Set the initialization parameters of the fruit fly optimization algorithm; S4: construct the fitness function of the fruit fly optimization algorithm based on the mathematical model of the ore block in S2 and the initialization parameters in S3; S5: setting the constraints and priority principles of the fruit fly optimization algorithm according to the mathematical model of the ore block in S2 and the initialization parameters in S3; S6: Run the fruit fly optimization algorithm, calculate the fruit fly fitness value through the fitness function according to the fruit fly position, update the fruit fly position, update the global optimal fitness value, iterate the search until the iteration termination condition is met, and output the optimal fruit fly individual position as the optimal mining plan; S7: Apply the mining plan obtained in S6 to actual mining work.

2. The method for generating a mining plan based on a fruit fly optimization algorithm according to claim 1, characterized in that: In S1, the mathematical model of the ore block is as follows: in, Represents all mining blocks, Indicates the number of mining blocks. Indicates Mineral blocks, Indicates The number of the mining block, Indicates The boundary line of each mining block, Indicates The ore density of each mining block, Indicates The type of mineral material in each mining section, Indicates The ore grade of a mining block.

3. The method for generating a mining plan based on a fruit fly optimization algorithm according to claim 1, characterized in that: In S2, the size of the cubic ore block is the average mining volume of a mining team.

4. The method for generating a mining plan based on a fruit fly optimization algorithm according to claim 1, characterized in that: In S2, the mathematical model of the ore block is as follows: in, Indicates A nugget, Indicates The block number of the block, Represents the side length of the cube block, Indicates The three-dimensional coordinates of the center point of each ore block, Indicates The density of the ore in a block, Indicates The type of mineral material in each block, Indicates The mineral grade of each ore block, Indicates the total number of blocks.

5. The method for generating a mining plan based on a fruit fly optimization algorithm according to claim 1, characterized in that: In S3, the initialization parameters include the fruit fly population size. The maximum number of iterations is , search space , the initialization position of each fruit fly is any mine block, and the global optimal fitness value is initialized to 0; in, Indicates from Select from different elements The number of permutations of elements, Represents the planning cycle of the mining plan, The number of blocks planned to be mined in the mine mining plan. Indicates the total number of blocks.

6. The method for generating a mining plan based on a fruit fly optimization algorithm according to claim 1, characterized in that: In S4, the fitness function is as follows: in, represents the position of the fruit fly, Indicates A nugget, The number of blocks planned to be mined in the mine mining plan. represents the fitness function of the fruit fly optimization algorithm, Represents the side length of the cube block, represents the normalized unit location cost, Indicates The density of the ore in a block, Indicates The mineral grade of each ore block, Indicates The mineral material type corresponding to each ore block At the selling price corresponding to the planned mining date, Indicates Mineral Blocks The three-dimensional coordinates of the center point position, represents the normalized unit floating cost.

7. The method for generating a mining plan based on a fruit fly optimization algorithm according to claim 6, characterized in that: In S5, the constraint condition is as follows: in, and Belongs to the fruit fly position Any two blocks contained in and Respectively and The order of mining, and Respectively and Center point The absolute value of the axis coordinate, and Respectively and Center point The absolute value of the axis coordinate.

8. The method for generating a mining plan based on a fruit fly optimization algorithm according to claim 6, characterized in that: In S5, the priority principle is that when the fitness values ​​corresponding to any two fruit fly positions are consistent, the fruit fly with the least number of mining blocks involved is preferentially selected, which is expressed by the following formula: in, represents the fruit fly population, and represents any two fruit fly positions, express The fitness value of express The fitness value of express The cardinality of the included mining blocks, express The cardinality of the included mining blocks, To find the minimum function, Represents a selection operator.

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