Method and device for measuring and calculating square meter uranium quantity of sandstone uranium mine boundary

Abstract

The invention belongs to the field of uranium ore resource mining, and particularly relates to a sandstone uranium ore rock boundary square meter uranium quantity optimization method and device. A traditional mining area mining design generally adopts a fixed boundary square meter uranium amount, considers a single target, and is difficult to adapt to complex and changeable geological conditions and economic environments. The method comprises the following steps: constructing a mining area multivariate information digital library by acquiring geological information of a sandstone uranium mine mining area; implicit interpolation reconstruction is carried out on the mining area multi-source information database to obtain a spatial geometric model, spatial constraint and spatial filtering are carried out, a spatial block model is obtained, geological parameters and economic parameters are further obtained, and an economic benefit and resource benefit coupling multi-target optimization model is achieved and iterative optimization is carried out. The device comprises a graphic display module, a scheme selection module, a parameter input module, an optimization model construction module, an optimization calculation module and a result visualization module. According to the method, boundary square meter uranium quantity optimization based on dynamic economy and resource double targets is realized, and the utilization rate of mineral resources is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of uranium ore mining, and particularly relates to a method and device for measuring and calculating the boundary square meter uranium content of sandstone uranium ore. BACKGROUND

[0002] With the adjustment of energy structure, the proportion of clean energy new energy represented by nuclear power is becoming larger and larger, and the demand for uranium resources is becoming larger and larger, among which the proportion of sandstone uranium ore is the largest. In the process of mining sandstone uranium ore, the optimization of boundary square meter uranium content plays a crucial role in improving resource utilization efficiency, reducing mining cost and enhancing economic benefit.

[0003] Traditional mining area mining design usually adopts fixed boundary square meter uranium content. Although this method is simple and easy to implement, it is difficult to adapt to complex and changeable geological conditions and economic environment, thereby leading to problems such as resource waste and low economic benefit. At present, although some technical solutions for uranium mining optimization have been proposed, they mostly only focus on the optimization of a single target, such as simply pursuing maximum profit, and ignoring the balance and coordination between multiple targets. In actual mining, multiple mutually restrictive targets need to be considered at the same time, such as profit and resource utilization efficiency, short-term income and long-term sustainable development, etc.

[0004] In addition, the prior art fails to fully utilize advanced algorithm and model construction methods when dealing with the optimization problem of boundary square meter uranium content, resulting in limited accuracy and reliability of the optimization result. Traditional optimization algorithms are prone to fall into local optimal solution when facing complex multi-objective and multi-constraint optimization problems, and cannot effectively explore the entire solution space, thereby failing to provide comprehensive and reasonable optimization scheme for decision makers. SUMMARY

[0005] Therefore, the embodiments of the present application provide a sandstone uranium ore boundary square meter uranium content optimization method and device, aiming to determine the minimum boundary square meter uranium content of sandstone uranium ore leaching end and mining area sealing edge.

[0006] In order to achieve the above purpose, the embodiments of the present application provide the following solutions:

[0007] A method for measuring and calculating the boundary square meter uranium content of sandstone uranium ore, comprising the following steps:

[0008] Step 1: Obtain the geological information and economic cost information of the sandstone uranium ore mining area;

[0009] Step 2: Build a multi-element information database;

[0010] Step 3: Build a spatial model;

[0011] Step 4: Build a multi-objective optimization model;

[0012] Step five: iteratively optimize the multi-objective optimization model.

[0013] The step one specifically comprises that the geological information comprises mining area geological lithology information, grade distribution information and pumping and injection borehole spatial coordinate information; economic information of the mining area is acquired, and the economic data at least comprises metal selling price, discount rate, unit capital cost, borehole per meter capital cost, unit production total cost, unit production operation cost and cost function.

[0014] The method for acquiring the cost function is that: by collecting actual mining area each pumping and injection unit production index and production cost economic data, with the in-situ leaching production, the relationship between the remaining square meter uranium quantity and the uranium mass concentration of the pumping and injection unit is constructed, the relationship between the uranium mass concentration and the unit metal production cost is constructed, the relationship between the unit metal production cost and the unit square meter uranium quantity is constructed through the uranium mass concentration as the data connection point, and thus the cost function is acquired.

[0015] The step two specifically comprises that: a lithology database is constructed according to the mining area geological lithology information; a grade database is constructed according to the grade distribution information; a borehole mouth coordinate database is constructed according to the pumping and injection borehole spatial coordinate information; and a mining area multi-element information digital library is constructed according to the lithology database, the grade database and the borehole mouth coordinate database.

[0016] The step three specifically comprises that: the mining area multi-element information digital library is directly valued and reconstructed by a radial basis interpolation and an isosurface approximation method to obtain a spatial geometric model; and the spatial geometric model is subjected to spatial constraint and spatial filtering to obtain a spatial block segment model.

[0017] The calculation formula of the radial basis interpolation is:

[0018]

[0019] Wherein, x is the position of the point to be interpolated, x i is the position of the i-th known data point, N is the number of known data points, is a radial basis kernel function, and p(x) is a specific low-order polynomial term. The spatial geometric model is directly valued by the spatial constraint and spatial filtering method to obtain the spatial block segment model, which specifically comprises:

[0020] The base point coordinates of the spatial geometric model are determined, and a three-dimensional coordinate system is established with the base point coordinates as the origin; the spatial geometric model is divided into a*b*c unit blocks, and the size of any unit block is determined according to the original data accuracy, geological characteristics, calculation efficiency and required model accuracy.

[0021] The fourth step specifically comprises: obtaining geological indexes of each pumping and injection unit in the mining area according to the spatial block segment model, the geological indexes of the pumping and injection unit including unit area, unit grade, unit ore body volume and ore body density; obtaining square meter uranium of each pumping and injection unit according to the geological indexes of each pumping and injection unit, including unit area, unit ore body grade, unit ore body volume and ore body density of permeable rock and whole rock in each pumping and injection unit; constructing a multi-objective optimization model of economic benefit and resource benefit coupling according to the geological indexes and economic data of each pumping and injection unit;

[0022] The square meter uranium of each pumping and injection unit u i (unit: kg / m 2 ) is calculated by the following formula:

[0023]

[0024] Among them, V i is the ore body volume of each pumping and injection unit, g i is the ore body grade of each pumping and injection unit, and p is the ore body density, and S i is the area of each pumping and injection unit.

[0025] The fifth step specifically comprises: taking the improved NSGA-II algorithm for the multi-objective optimization model, and iteratively optimizing according to the upper and lower limits of the target boundary square meter uranium to form a set of optimized Pareto optimal boundary square meter uranium solutions and corresponding economic benefit and resource benefit data sets; the specific method is: within the preset square meter uranium range, the improved NSGA-II algorithm intelligent optimization algorithm iteratively optimizes the multi-objective optimization model according to the preset conditions, and repeats the steps of "constructing a corresponding multi-objective optimization model of economic benefit and resource benefit coupling according to the optimization scheme", "obtaining a multi-objective function solution set under the current square meter uranium data set by using the improved NSGA-II algorithm", until the maximum iteration number is reached and the iteration is stopped, and the optimized Pareto optimal boundary square meter uranium solution set and the corresponding resource benefit and resource benefit data set are obtained.

[0026] The improved NSGA-II algorithm comprises: in the population initialization stage, the initial population individuals are generated by using symmetric Latin hypercube design to ensure uniform coverage of each dimension parameter in the design space;

[0027] In the mutation and crossover stage, an adaptive control parameter is used to automatically adjust the scale factor and the crossover rate of each generation in the iteration process, and the specific formula is:

[0028]

[0029] Among them, is the scale factor of each generation, a cross rate for each generation, is a normal distribution function, Ψ is a mean, is a variance; Uniformerand(δ l ,δ u ) is a uniform distribution function, δ l and δ u are lower and upper limits of the uniform distribution function, respectively.

[0030] The multi-objective optimization model coupling economic benefits and resource benefits comprises the following steps:

[0031] The optimization scheme is divided into an end-of-leaching scheme and a mining area boundary sealing scheme according to the uranium mine production stage and the mining area design stage, and a multi-objective optimization model corresponding to the scheme is constructed;

[0032] According to the end-of-leaching scheme, economic benefits are represented by total profits I of the mining area, and resource benefits are represented by leaching rates ε of the mining area;

[0033] The multi-objective optimization model of the end-of-leaching scheme is:

[0034]

[0035] According to the mining area boundary sealing scheme, economic benefits are represented by total net present values NPV of newly added fictitious units, and resource benefits are represented by indices φ of the newly added fictitious units, and economic benefits G j,2 created by each unit are all greater than operating costs required by the unit as constraint conditions;

[0036] The multi-objective optimization model of the mining area boundary sealing scheme is:

[0037] Optionally, the multi-objective optimization model corresponding to the scheme comprises the following steps:

[0038] In the end-of-leaching scheme, the total profits I of the mining area are calculated according to the following formula:

[0039]

[0040] wherein, I i is a profit of each unit, N is a number of pumping and injection units in the mining area, i is an index of the pumping and injection unit, u j is a remaining square meter uranium amount (kg / m 2 ) of the unit at the end of leaching, u i is a square meter uranium amount (kg / m 2 ) of each unit in the model, S i is an area (km 2c is the cost function; P is the metal selling price (ten thousand yuan / ton); q is the average infrastructure cost per unit (ten thousand yuan); du indicates that the integral is performed on the variable u (uranium quantity per square meter);

[0041] In the leaching termination scheme, the leaching rate ε of the mining area is calculated using the following formula:

[0042]

[0043] Where u0 is the original controlled amount of uranium per square meter in the mining area; u j The amount of uranium remaining per square meter of the cell at the end of leaching (kg / m²) 2 );

[0044] In the mining area sealing scheme, the formula for calculating the total net present value (NPV) of the newly added fictitious unit is as follows:

[0045]

[0046] Among them, NPV j Let be the net present value of the j-th newly added fictitious unit; j is the number of newly added fictitious units;

[0047] The net present value (NPV) of the j-th newly added fictitious unit j The specific calculation steps are as follows:

[0048]

[0049] Q j =u j ×S j ;

[0050] G j,1 =Q j ×ε×(P-c1)-q j ;

[0051] q j = k×m×n;

[0052]

[0053] Where: t j The service life (in years) of the j-th newly added virtual unit; Q j Control the amount of metal (tons) for the j-th newly added fictitious unit; Q z,j Let be the average annual production capacity (tons / year) of j newly added fictitious units; ε be the leaching rate (%); Q be... j Control the amount of metal (tons) for the j-th newly added fictitious unit; u j The amount of uranium per square meter (kg / m²) controlled by the j-th newly added virtual unit 2 );S jArea of the jth newly added fictitious unit (km 2 ) ; G j,1 Total profit under the total cost; P is the unit metal sales price (ten thousand yuan / ton) ; c1 is the unit production total cost under the total cost (ten thousand yuan / ton) ; q j Capital cost of the jth newly added fictitious unit (ten thousand yuan) ; k is the average capital cost per meter of drilling; m is the average depth of drilling (m) ; n is the number of drilling holes of the pumping and injecting unit; g j Average annual profit of the jth newly added fictitious unit, T j Integer part of t j , d is the discount rate;

[0054] In the mining area edge sealing scheme, the index of the newly added fictitious unit is The calculation formula is as follows:

[0055]

[0056] Optionally, the economic benefit constraint is added in the mining area edge sealing scheme, and specifically includes:

[0057] The specific calculation formula of the economic benefit constraint is:

[0058] G j,2 = Q j × ε × (P-c2) - q j ≥ 0;

[0059] Wherein: c2 is the unit production operating cost under the operating cost; G j,2 ≥ 0, which means that the economic benefit value created by each newly added fictitious unit needs to be greater than the operating cost required for the unit production.

[0060] A device for measuring the boundary planar uranium content of sandstone uranium mine, comprising a graphic display module, a scheme selection module, a parameter input module, an optimization model construction module, an optimization calculation module and a result visualization module.

[0061] The graphic display module comprises a data display area, a parameter setting area, a result display area and a save function area, realizing the whole-process visualization of "input-calculation-output-save";

[0062] The scheme selection module comprises a selection interface for providing the leaching end scheme and the mining area edge sealing scheme, and the system automatically loads the parameter input interface and the calculation model of the corresponding scheme according to the selection;

[0063] The parameter input module is connected with the scheme selection module and is used for collecting corresponding geological parameters, economic parameters and constraint conditions according to the selected scheme;

[0064] The optimization model construction module includes a parameter input module connected thereto, which is used for automatically constructing a boundary square meter uranium content optimization model according to the collected parameters, and different objective functions and constraint conditions are adopted for different schemes.

[0065] The optimization calculation module includes an optimization model construction module connected thereto, which is used for solving the optimization model by using an INSGA-II algorithm to obtain an optimal boundary square meter uranium content scheme.

[0066] The result visualization module includes a table display optimization result, which contains an optimal square meter uranium content and corresponding economic benefits and resource benefits, and supports local saving.

[0067] The sandstone uranium mine rock boundary square meter uranium content optimization method and device provided by the application obtain geophysical prospecting and interpretation information of an in-situ leaching mining area, obtain economic and geological parameter data of a corresponding scheme, construct a multi-objective optimization model of the corresponding scheme, solve the model by using an improved NSGA-II algorithm, and visualize the optimization result; the optimization result includes an optimal boundary square meter uranium content data set and a corresponding resource benefit and resource benefit Pareto solution set, uranium mine enterprise decision makers can deeply analyze and evaluate the optimization result, which provides a strong basis for uranium mine enterprise decision making, can be used for guiding production and design of a mining area, and has great engineering practical significance. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 It is a flowchart of a sandstone uranium mine boundary square meter uranium content optimization method of an embodiment of the application;

[0069] Figure 2 It is a production cost type schematic diagram of an embodiment of the application;

[0070] Figure 3 It is a cost function fitting process schematic diagram of an embodiment of the application;

[0071] Figure 4 It is a sandstone rock layer model schematic diagram of a target block implicit interpolation provided by an embodiment of the application;

[0072] Figure 5 It is a target block mining area pumping and injection unit model schematic diagram provided by an embodiment of the application;

[0073] Figure 6 It is a target block mining area grade block segment model schematic diagram provided by an embodiment of the application;

[0074] Figure 7 It is a flowchart of a target block mining area edge sealing scheme economic benefit model construction provided by an embodiment of the application;

[0075] Figure 8A Pareto optimal solution set diagram of an end-of-leaching scheme provided by an embodiment of the present application;

[0076] Figure 9 A Pareto optimal solution set diagram of a stope edge sealing scheme provided by an embodiment of the present application;

[0077] Figure 10 A flowchart of a sandstone uranium mine boundary square meter uranium content optimization device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0078] The embodiment of the present application provides a sandstone uranium mine boundary square meter uranium content optimization method to solve the problem of low resource utilization caused by the limitation of the traditional fixed boundary standard. Figure 1 An exemplary flow of the above-mentioned sandstone uranium mine boundary square meter uranium content optimization method is shown. The steps are described in detail as follows.

[0079] Step 1: Obtain the geological information of the sandstone uranium mine stope; the geological information at least includes: stope geological lithology information, grade distribution information and space coordinate information of pumping and injection boreholes; obtain the economic information of the stope; the economic data at least includes: metal selling price, discount rate, unit capital cost, drilling per meter capital cost, unit production total cost, unit production operating cost and cost function;

[0080] The unit production total cost and the unit production operating cost are obtained as follows:

[0081] Step 11: As Figure 2 The cost types of the stope production are shown, the stope production total cost is obtained according to all production basic production costs and research and development expenditures of the stope; the stope operating cost is obtained by deducting other basic production costs, depreciation of manufacturing expenses, amortization of low-value consumables, amortization of intangible assets, other manufacturing expenses and company management personnel cost calculated according to 35% of the basic production cost of employee compensation from the stope production total cost;

[0082] Step 12: Obtain the unit production total cost c1 under the total cost and the unit production operating cost c2 under the operating cost according to the annual production of the stope;

[0083] In an example, according to the real annual operating cost data of the stope and the annual production data of the stope, the unit production total cost c1 is obtained as 392,800 yuan, the unit production operating cost c2 is obtained as 300,600 yuan, the unit average capital cost is obtained as 331,500 yuan, and the metal selling price is obtained as 853,000 yuan / ton;

[0084] The cost function is obtained as follows:

[0085] Step 13: Based on the collected production indicators and economic data of production costs of each injection unit in the actual mining area, as injection production progresses, construct the relationship between the remaining uranium quantity per square meter and the uranium mass concentration in the injection unit, and construct the relationship between the uranium mass concentration and the unit metal production cost.

[0086] Step 14: By using uranium mass concentration as the data linking point, construct the relationship between cost and uranium quantity per unit square meter to obtain the cost function;

[0087] In one example, in an acid-leached sandstone uranium ore production mining area, steps 13 and 14 are combined, such as... Figure 3 As shown, firstly, the uranium concentration of the extracted liquid and the remaining uranium quantity per square meter of each injection unit in the mining area are collected. Then, the production and concentration are statistically analyzed separately for four quarters, with the total cost remaining consistent for each quarter. This yields the relationship between uranium mass concentration and unit cost. Using uranium mass concentration as the data linking point, the relationship between cost and uranium quantity per square meter of unit is constructed, resulting in the cost function c = 846.78 × (2.95u 3 -16.66u 2 +34.49u) -1.03 .

[0088] In another example, using a neutral leaching sandstone uranium ore production area, combining steps 13 and 14, the cost function c = 405.3 × (0.43u) is also obtained. 3 -3.41u 2 +19u) -0.662 .

[0089] Step 2: Construct a lithology database based on the geological and lithological information of the mining area; construct a grade database based on the grade distribution information; construct a borehole coordinate database based on the spatial coordinate information of the injection boreholes; construct a multi-dimensional information digital database of the mining area based on the lithology database, the grade database, and the borehole coordinate database;

[0090] Step 3: Based on the multi-dimensional information digital database of the mining area, implicit interpolation reconstruction is performed using radial basis interpolation and isosurface approximation methods to obtain a spatial geometric model; spatial constraints and spatial filtering are applied to the spatial geometric model to obtain a spatial block model;

[0091] In one example, data from the mining area's multi-dimensional information database is used to construct a spatial geometric model through radial basis interpolation and isosurface approximation methods, resulting in a model such as... Figure 4 The sandstone strata model shown in the mining area and such Figure 5 The extracted unit model is shown; then, spatial constraint and spatial filtering methods are used to directly assign values ​​to the spatial geometric model, resulting in the assigned spatial block model, as shown. Figure 6 The model shown is a grade block model.

[0092] Step 31: the calculation formula of the radial basis interpolation is:

[0093]

[0094] wherein x is a known data point, ω i is a weight coefficient, p(x) is a polynomial, is a radial basis kernel function, and N is a constant.

[0095] Step 32: directly assigning the spatial geometric model by using the space constraint method and the space filtering method to obtain a spatial block segment model; specifically including:

[0096] Step 321: determining the base point coordinates of the spatial geometric model, and establishing a three-dimensional coordinate system with the base point coordinates as the origin;

[0097] In an example, the base point coordinates of the spatial geometric model are determined for positioning. All spatial relationships need to be positioned based on the base point coordinates, and the spatial relative positions are calculated, so as to convert the original digital model into a spatial geometric model.

[0098] Step 322: dividing the spatial geometric model into a*b*c unit blocks, and determining the size of any unit block;

[0099] In an example, the unit block refers to dividing or segmenting the spatial geometric model according to a certain size in space, for example, dividing the three-dimensional space of the mining area into multiple small unit blocks according to the size of 2m*2m*0.5m, and correspondingly, segmenting the spatial geometric model into multiple small unit blocks according to the scale.

[0100] In another example, in combination with steps 321 and 322, the base point coordinates of the spatial geometric model, the size of the unit block in the X, Y, and Z directions of the three-dimensional coordinate system, and the number thereof are specified. The base point coordinates of the spatial geometric model are X=699170.00, Y=4884350.00, and Z=965.00, the size of the unit block in the X, Y, and Z directions of the three-dimensional coordinate system is 2.0, 2.0, and 0.5, and the number of unit blocks in the X, Y, and Z directions is 114.0, 250.0, and 264.0, respectively.

[0101] Step 323: estimating the gamma distribution data of the geological body and the distribution data of the ore body per square meter uranium represented by any unit block by using an estimation method to obtain the unit block estimation; the estimation method at least includes a Kriging method or a geostatistical method.

[0102] In one example, the ore body grade of the unit block needs to be evaluated because in the entire spatial geometry model, the grade value is only known at the position of the exploration well, and other points in space need to be evaluated / interpolated according to a small amount of known data using the Kriging method or geostatistical method.

[0103] Step 4: Obtain the geological indicators of each pumping and injection unit in the mining area according to the spatial block segment model, the geological indicators of the pumping and injection unit including: unit area, unit grade, unit ore body volume and ore body density, etc.; obtain the square meter uranium of each pumping and injection unit according to the geological indicators of each pumping and injection unit; and construct a multi-objective optimization model coupled with economic benefits and resource benefits according to the geological indicators and economic data of each pumping and injection unit;

[0104] Step 41: Coupling the spatial block segment model and the multi-element information digital model of the mining area to obtain a multi-element information coupling model, thereby obtaining the geological indicators of each pumping and injection unit; and obtaining the square meter uranium of each pumping and injection unit according to the geological indicators of each pumping and injection unit;

[0105] In one example, the geological indicators of each pumping and injection unit obtained through the spatial block segment model include: a total of 26 pumping and injection units in the mining area, the average square meter uranium of the permeable rock layer in the ore bed of the pumping and injection unit is 2.54 kg / m 2 , the average square meter uranium of all rock layers is 3.14 kg / m 2 , and the average unit area is 1.457 km 2 .

[0106] Step 42: The optimization scheme is divided into leaching end scheme and mining area sealing scheme according to the uranium mine production stage and the mining area design stage;

[0107] Step 43: Select the required optimization scheme to construct a multi-objective optimization model coupled with economic benefits and resource benefits;

[0108] In one example, for the obtained mining area unit geological data and mining area mining economic cost data, the leaching end scheme and the mining area sealing scheme are selected to construct a multi-objective optimization model coupled with corresponding economic benefits and resource benefits.

[0109] Step 431: According to the leaching end scheme, the economic benefit is represented by the total profit I of the mining area, and the resource benefit is represented by the leaching rate ε of the mining area;

[0110] The multi-objective optimization model of the leaching end scheme is:

[0111]

[0112] Step 432: According to the mining area sealing scheme, the economic benefit is represented by the total net present value of the new fictitious unit, the resource benefit is represented by the index of the new fictitious unit, and the economic benefit constraint is added.

[0113] The multi-objective optimization model for the edge sealing scheme of the mining area is as follows:

[0114]

[0115] Step 44: Based on the obtained geological data of the mining area unit and the economic cost data of mining in the mining area, construct a multi-objective optimization model for the leaching termination scheme, specifically including:

[0116] Economic benefits are represented by the total profit I of the mining area, and the calculation formula is as follows:

[0117]

[0118] Among them, I i The profit for each unit, where N is the number of injection units in the mining area; i is the index of the injection unit; u j The amount of uranium remaining per square meter of the cell at the end of leaching (kg / m²) 2 );u i The amount of uranium per square meter (kg / m²) in each cell of the model. 2 );S i The area (km²) of each unit in the model 2 c is the cost function; P is the metal selling price (ten thousand yuan / ton); q is the average infrastructure cost per unit (ten thousand yuan);

[0119] Resource efficiency is represented by the leaching rate ε in the mining area, and the calculation formula is as follows:

[0120]

[0121] Where u0 is the original controlled amount of uranium per square meter in the mining area;

[0122] Step 45: Based on the obtained geological data of the mining area unit and the economic cost data of mining in the mining area, construct a multi-objective optimization model for the mining area sealing scheme, specifically including:

[0123] Economic benefits are represented by the total net present value (NPV) of the newly added fictitious units, calculated using the following formula:

[0124]

[0125] Among them, NPV j Let be the net present value of the j-th newly added fictitious unit; j is the number of newly added fictitious units;

[0126] like Figure 3 As shown, the net present value (NPV) of the j-th newly added fictitious unit is... j Specific calculation steps:

[0127] Step 451: Calculate the mining time t for the j-th newly added virtual unit.j :

[0128]

[0129] Q j = u j × S j ;

[0130] wherein: Q j is the controlled amount of metal (tons) of the jth newly-imaginary unit; Q z,j is the average annual production capacity (tons / year) of the jth newly-imaginary unit; ε is the leaching rate (%); u j is the controlled amount of uranium per square meter (kg / m 2 ) of the jth newly-imaginary unit; S j is the area (km 2 ) of the jth newly-imaginary unit;

[0131] Step 452: calculate the economic benefit of mining of the jth imaginary outward-expanding unit:

[0132] G j,1 = Q j × ε × (P - c1) - q j ;

[0133] q j = k × m × n;

[0134] wherein: P is the unit metal sales price (ten thousand yuan / ton); c1 is the unit production full cost (ten thousand yuan / ton) under the full cost; q j is the capital cost (ten thousand yuan) of the jth newly-imaginary unit; k is the average capital cost per meter of drilling (ten thousand yuan); m is the average depth of drilling (m); n is the number of drillings of the pumping and injecting unit;

[0135] In one example, the leaching rate ε is set to 75%, the average capital cost per meter of drilling k is set to 0.14 ten thousand yuan; the average depth of drilling m is set to 110 m, and the area S j of the newly-imaginary unit is 1.457 km 2 .

[0136] Step 453: calculate the average annual profit g j of the jth imaginary outward-expanding unit:

[0137]

[0138] Step 454: calculate the net present value NPV j of the jth imaginary outward-expanding unit:

[0139]

[0140] Wherein: T j is the integer part of t j , d is the discount rate;

[0141] The resource benefit is expressed by a new fictitious unit index, and the calculation formula is as follows:

[0142]

[0143] The economic benefit created by each unit needs to be greater than the operating cost of the unit as an economic benefit constraint, and the calculation formula is as follows:

[0144] G j,2 = Q j × ε × (P-c2)-q j ≥ 0;

[0145] Wherein, c2 is the unit production and operation cost under the operating cost (ten thousand yuan / ton);

[0146] Step 5: According to the preset conditions, the multi-objective optimization model is iteratively optimized by the improved NSGA-II algorithm to form a set of optimized Pareto optimal boundary square meter uranium solution set and its corresponding economic benefit and resource benefit data set. The preset conditions include: the upper and lower limits of the target boundary square meter uranium.

[0147] In an example, through the multi-objective optimization model of economic benefit and resource benefit coupling constructed, the optimization of boundary square meter uranium is carried out under the given constraint condition with the maximization of economic benefit and resource benefit as the optimization target. The improved NSGA-II algorithm is iteratively optimized to obtain a set of Pareto optimal boundary square meter uranium solutions. For the two sub-targets of economic maximization and resource maximization, if the square meter uranium solution x a is not worse than the solution x b , and at least one sub-target makes the square meter uranium solution x a better than the square meter uranium solution x b . Then this time square meter uranium solution is better than the previous square meter uranium solution. Through 100 iterations of optimization, the optimal Pareto solution set under the economic benefit and resource benefit target is obtained.

[0148] The set of Pareto optimal boundary square meter uranium solutions obtained by the improved NSGA-II algorithm iterative optimization includes:

[0149] Step 51: The upper and lower limits of the target boundary square meter uranium are preset, and the population is initialized by using the symmetric Latin hypercube design;

[0150] The process of population initialization by symmetric Latin hypercube design is as follows:

[0151] Determine the population size NP×M. M is the dimension of the decision variable;

[0152] Each decision variable interval [X l ,X u ] is divided into NP equal-length subintervals;

[0153] An NP x M matrix U is generated, each column of U being a random combination of {1, 2, …, NP} and satisfying that for any row vector u i = [u i,1 , u i,2 , …, u i,M ] of U, there exists a row vector u j = [NP+1-u i,1 , NP+1-u i,2 , …, NP+1-u i,M ] also of U, and the matrix U is called a symmetric Latin hypercubic matrix.

[0154] Each row of the matrix U corresponds to a subinterval, and a sample point is randomly selected in the subinterval, thereby obtaining NP sample points, i.e., an initial population.

[0155] In an example, the upper and lower limits of the target boundary planar uranium content of the leaching end scheme are set to u j ∈ [0, 1], the initial population NP is set to 100, and the dimension M of the decision variable is set to 1; the upper and lower limits of the target boundary planar uranium content of the stope sealing scheme are set to u j ∈ [0.1, 1], the initial population NP is set to 100, and the dimension M of the decision variable, i.e., the number of newly added fictitious units j, is set to 14.

[0156] Step 52: An economic benefit and resource benefit coupled multi-objective optimization model is constructed according to the obtained geological indexes and economic data of each extraction unit;

[0157] Step 53: Economic function values and resource function values of population individuals are calculated;

[0158] Step 54: Adaptive mutation operators and adaptive control parameters are used for adaptive mutation operation and crossover operation to generate a new planar uranium content solution set;

[0159] The mutation and crossover operation specifically includes:

[0160] The mutation operation includes:

[0161]

[0162] In the formula, G T is the current iteration number, and G max is the maximum iteration number.

[0163] The crossover operation includes:

[0164]

[0165] where CR is the crossover rate CR ∈ [0, 1]; rand j (0, 1) is a random number generated from a uniform distribution on [0, 1]; k T is a random integer from [1, 2, …, M].

[0166] The adaptive mutation operator and the adaptive control parameters include:

[0167] The scale factor and the crossover rate of each generation are randomly generated to ensure the diversity of the population. The normal distribution and the uniform distribution are used to generate the scale factor and the crossover rate of each generation.

[0168]

[0169] In the formula, N is a normal distribution function, Ψ is the mean, is the variance; Uniformrand(δ l , δ u ) is a uniform distribution function, δ l and δ u are the lower limit and the upper limit of the uniform distribution function, respectively.

[0170] In an example, the Ψ and parameters of the scale factor are set to 0.7 and 0.1, respectively; the δ l and δ u parameters of the crossover rate are set to 0 and 1, respectively.

[0171] Step 55: fast non-dominated sorting, calculation of crowding distance, crowding distance sorting, and elitist reserve strategy, to generate a new generation of optimal planar meter uranium solution set.

[0172] Step 56: iteratively perform steps 53, 54, and 55 until the preset maximum number of iterations is reached, and then stop the iteration and output the Pareto optimal boundary planar meter uranium solution set and the corresponding economic benefit and resource benefit data set;

[0173] In an example, the maximum number of iterations is set to 100, and the output Pareto optimal solution set is as shown in Figure 8 , Figure 9 .

[0174] In another aspect, in one example, the present application also provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for optimizing the boundary square meter uranium content of sandstone uranium mine as described above when executing the computer program. Figure 10 As shown, the method comprises a graphical user interface, a scheme selection module, a parameter input module, an optimization model construction module, an optimization calculation module, and a result visualization module.

[0175] In summary, the embodiment of the present application generates an optimized boundary square meter uranium content solution set quickly from the perspective of economic benefit and resource benefit, first constructs a mining area multi-source information digital model through geological properties, grade distribution, and pumping unit distribution; second, constructs an economic benefit and resource benefit coupling degree multi-objective optimization model according to the production cost index and geological index of the mining area; and finally iteratively optimizes the boundary square meter uranium content to maximize the economic benefit and resource benefit until the optimal boundary square meter uranium content solution set is generated.

[0176] The above is merely a specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for estimating the areal grade of a sandstone uranium deposit, characterized in that, It comprises the following steps: Step one: obtaining the geological information and economic cost information of the sandstone uranium mining area; Step two: constructing a multi-information database; Step three: constructing a spatial model; Step four: constructing a multi-objective optimization model; Step five: iteratively optimizing the multi-objective optimization model.

2. The method according to claim 1, wherein the method is characterized by, The geological information includes the geological lithology information, grade distribution information, and spatial coordinate information of the extraction and injection borehole of the mining area; the economic information of the mining area is obtained, and the economic data at least includes the metal selling price, discount rate, unit capital cost, borehole per meter capital cost, unit production total cost, unit production and operation cost, and cost function; The method for obtaining the cost function is: by collecting the production indicators of each extraction and injection unit in the actual mining area and the production cost economic data, with the progress of in-situ leaching production, the relationship between the remaining square meter uranium amount and the uranium mass concentration of the extraction and injection unit is constructed, the relationship between the uranium mass concentration and the unit metal production cost is constructed, and through the uranium mass concentration as the data connection point, the relationship between the unit metal production cost and the unit square meter uranium amount is constructed, so that the cost function is obtained.

3. The method according to claim 2, wherein the method is characterized in that, The step two specifically comprises: constructing a lithology database according to the geological lithology information of the mining area; constructing a grade database according to the grade distribution information; constructing a borehole coordinate database according to the spatial coordinate information of the extraction and injection borehole; and constructing a multi-information database of the mining area according to the lithology database, the grade database, and the borehole coordinate database.

4. The method according to claim 3, wherein the method is characterized in that, The step three specifically comprises: the multi-information database of the mining area is subjected to implicit interpolation reconstruction through radial basis interpolation and isosurface approximation method to obtain a spatial geometric model; and the spatial geometric model is subjected to spatial constraint and spatial filtering to obtain a spatial block segment model. The calculation formula of the radial basis interpolation is: where x is the position of the point to be interpolated, x i is the position of the i-th known data point, and N is the number of known data points, is a radial basis kernel function, and N is a constant; p(x) is a specific low-order polynomial term; The spatial geometric model is directly assigned by the spatial constraint and spatial filtering method to obtain the spatial block segment model, which specifically comprises: The base point coordinates of the spatial geometric model are determined, and a three-dimensional coordinate system is established with the base point coordinates as the origin; the spatial geometric model is divided into a*b*c unit blocks, and the size of any unit block is determined according to the original data accuracy, geological characteristics, calculation efficiency, and required model accuracy.

5. The method according to claim 4, wherein the method is characterized in that, The step four specifically comprises: obtaining the geological indicators of each extraction and injection unit in the mining area according to the spatial block segment model, wherein the geological indicators of the extraction and injection unit include unit area, unit grade, unit ore body volume, and ore body density; obtaining the square meter uranium amount of each extraction and injection unit according to the geological indicators of each extraction and injection unit, including the unit area, unit ore body grade, unit ore body volume, and ore body density of the permeable rock layer and the whole rock layer in each extraction and injection unit; and constructing a multi-objective optimization model coupled with economic benefit and resource benefit according to the geological indicators and economic data of each extraction and injection unit; The each extraction unit square meter uranium content u i (unit: kg / m 2 ) calculation formula is: where V i is the volume of the ore body of each pumping unit, g i is the grade of the ore body of each pumping unit, p is the density of the ore body, S i is the area of each pumping unit.

6. The method according to claim 5, wherein the method is characterized in that, The step five specifically comprises: taking the improved NSGA-II algorithm for the multi-objective optimization model, iteratively optimizing according to the upper and lower limits of the target boundary square meter uranium content, forming a set of optimized Pareto optimal boundary square meter uranium content solutions and corresponding economic benefit and resource benefit data sets; the specific method is: in the preset square meter uranium content range, the improved NSGA-II algorithm intelligent optimization algorithm is used to iteratively optimize the multi-objective optimization model according to the preset conditions, and the steps of "constructing the corresponding economic benefit and resource benefit coupled multi-objective optimization model according to the optimization scheme" and "obtaining the multi-objective function solution set under the current square meter uranium content data set by using the improved NSGA-II algorithm" are repeated until the maximum iteration number is reached to stop iteration, and the optimized Pareto optimal boundary square meter uranium content solution set and the corresponding resource benefit and resource benefit data set are obtained.

7. The method according to claim 5, characterized in that: The economic benefit and resource benefit coupled multi-objective optimization model is constructed, specifically comprising: The optimization scheme is divided into leaching end scheme and mining area sealing scheme according to the uranium mine production stage and mining area design stage, and the multi-objective optimization model of the corresponding scheme is constructed; According to the leaching end scheme, the economic benefit is represented by the total profit I of the mining area, and the resource benefit is represented by the leaching rate ε of the mining area; The multi-objective optimization model of the leaching end scheme is: According to the sealing edge scheme of the mining area, the economic benefit is represented by the total net present value NPV of the newly created fictitious unit, the resource benefit is represented by the index φ of the newly created fictitious unit, and the economic benefit created by each unit G j,2 is required to be greater than the operating cost required by the unit as a constraint. The multi-objective optimization model of the mining area sealing scheme is: Optionally, the multi-objective optimization model of the corresponding scheme is constructed, specifically comprising: In the leaching end scheme, the total profit I of the mining area is calculated as follows: where I i is the profit of each unit, N is the number of pumping and injection units in the mining area, i is the index of pumping and injection units, u j is the remaining square meter uranium amount (kg / m 2 ) of the unit at the end of leaching; u i is the square meter uranium amount (kg / m 2 ) of each unit in the model; S i is the area (km 2 ) of each unit in the model; c is the cost function; P is the metal sales price (ten thousand yuan / ton); q is the average capital cost of each unit (ten thousand yuan); du indicates that the integral is performed on the variable u (square meter uranium amount); In the leaching end scheme, the leaching rate ε of the mining area is calculated as follows: wherein u0 is the original controlled square meter uranium amount of the mining area; u j is the square meter uranium amount (kg / m2) remaining in the unit at the end of leaching; 2 ) In the mining area sealing scheme, the total net present value NPV of the newly added virtual unit is calculated as follows: Wherein, NPV j is the net present value of the jth newly added fictitious unit; j is the number of newly added fictitious units; The jth newly added fictitious unit net present value NPV j The specific calculation steps are as follows: Q j = u j x S j ; G j,1 = Q j x e x (P - c1) - q j ; q j = k x m x n; Wherein: t j is the service life of the jth newly added fictitious unit (years) ; Q j is the controlled metal quantity of the jth newly added fictitious unit (tons) ; Q z,j is the annual production capacity of the jth newly added fictitious unit (tons / year) ; ε is the leaching rate ( % ) ; Q j is the controlled metal quantity of the jth newly added fictitious unit (tons) ; u j is the controlled square meter uranium quantity of the jth newly added fictitious unit (kg / m 2 ) ; S j is the area of the jth newly added fictitious unit (km 2 ) ; G j,1 is the total profit under the total cost; P is the unit metal sales price (ten thousand yuan / ton) ; c1 is the unit production total cost under the total cost (ten thousand yuan / ton) ; Q j is the capital construction cost of the jth newly added fictitious unit (ten thousand yuan) ; k is the average capital construction cost per meter of drilling; m is the average depth of drilling (m) ; n is the number of drillings of the pumping and injection unit; g j is the average annual profit of the jth newly added fictitious unit, T j is the integer part of t j , and d is the discount rate. In the mining area edge sealing scheme, the newly added imaginary unit index The calculation formula is as follows: Optionally, the economic benefit constraint is added in the mining area sealing scheme, specifically comprising: The specific calculation formula of the economic benefit constraint is: G j,2 = Q j × ε × (P - c2) - q j ≥ 0; Wherein: c2 is the unit production cost under the operating cost; G j,2 ≥ 0, indicating that the economic benefit value created by each new fictitious unit must be greater than the operating cost required for the unit production.

8. The method according to claim 6, characterized in that: The improved NSGA-II algorithm comprises: in the population initialization stage, the initial population individuals are generated by using the symmetric Latin hypercube design to ensure uniform coverage of the parameters in each dimension of the design space; In the mutation and crossover stage, the adaptive control parameter is used to automatically adjust the scale factor and the crossover rate of each generation in the iteration process, and the specific formula is: wherein, is a scale factor for each generation, is a crossover rate for each generation, is a normal distribution function, Ψ is a mean, is a variance; Uniformrand(δ l ,δ u ) is a uniform distribution function, δ l and δ u are lower and upper limits of the uniform distribution function, respectively.

9. A device for measuring and calculating the uranium content of a sandstone uranium deposit boundary square meter, characterized in that: The graphic display module, the scheme selection module, the parameter input module, the optimization model construction module, the optimization calculation module and the result visualization module are included.

10. The device for measuring the boundary square meter uranium content of sandstone uranium ore according to claim 9, characterized in that: The graphic display module includes a data display area, a parameter setting area, a result display area, a save function area, and realizes the "input-computation-output-save" whole process visualization; The scheme selection module includes a selection interface for providing the leaching end scheme and the mining area sealing scheme, and the system automatically loads the parameter input interface and the calculation model corresponding to the selected scheme; The parameter input module is connected with the scheme selection module and is used to collect the corresponding geological parameters, economic parameters and constraint conditions according to the selected scheme; The optimization model construction module is connected with the parameter input module and is used for automatically constructing the boundary square meter uranium content optimization model according to the collected parameters, and different objective functions and constraint conditions are adopted for different schemes. The optimization calculation module is connected with the optimization model construction module and is used for solving the optimization model by using the INSGA-II algorithm to obtain an optimal boundary square meter uranium content scheme. The result visualization module includes a table for displaying the optimization results, and the table contains the optimal square meter uranium content, the corresponding economic benefits and resource benefits, and supports local saving.

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