A method, apparatus, device, and medium for regional optimization of iron-manganese encrusted mineral resources

Abstract

The application discloses a kind of iron manganese encrustation mineral regional optimization method, device, equipment and medium.The method comprises the following steps: dividing reference area to obtain reference grid unit;According to the first area index of survey specification and influence factor data;According to the polygonal region of reference area division, the target polygonal region is obtained by the first area index to the reference grid unit and is labeled to obtain labeled grid unit;The second area index is weighted and analyzed, and the target score of each labeled grid unit under different weights is obtained, and the target grid unit is determined according to the target score of labeled grid unit and slope area ratio;Third area index is determined from target grid unit, and the target selection scheme is determined according to third area index.The method is considered on the basis of various factor characteristics of reference area, and comprehensive analysis is carried out using quantitative means, to improve the accuracy of regional optimization result.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration technology, and in particular to a method, device, equipment and medium for regional optimization of ferromanganese crust minerals. Background Art

[0002] Regional optimization is necessary for the exploration and development of ferromanganese crust minerals. However, regional optimization involves many factors, which may lead to incomplete considerations. Conducting comprehensive analysis of multiple factors such as resources, environment, and mining, and research on regional optimization methods are crucial to proposing optimal plans and ensuring the maximization of resource reserves.

[0003] However, the existing technical solutions are mainly based on the resource factors of the crust distribution area, and lack consideration of environmental factors, which leads to one-sided analysis results and cannot well characterize the advantages and disadvantages of the block. Summary of the Invention

[0004] The present invention provides a method, device, equipment and medium for regional optimization of ferromanganese crust minerals, so as to overcome the one-sidedness of factors affecting regional optimization of ferromanganese crust minerals.

[0005] According to one aspect of the present invention, a method for regional optimization of ferromanganese crust minerals is provided, comprising:

[0006] The reference area is divided to obtain reference grid units; the reference area is the area where crust minerals need to be optimized, and the reference area can be divided into a number of polygonal areas according to different influencing factors; the polygonal areas are the ranges of spatial distribution of different influencing factors; the influencing factors are resource factors, environmental factors, and mining factors that affect the optimization of minerals in the reference area;

[0007] Determine a first regional index based on the exploration specification and the influencing factor data; the first regional index is used to characterize the type and quantity of mineral characteristics, biological characteristics, and slope characteristics in the polygonal area corresponding to the reference area; the influencing factor data is used to characterize the mineral characteristics, biological characteristics, and slope characteristics of the reference area;

[0008] Assigning values ​​to polygonal areas divided by the reference area according to preset conditions to obtain target polygonal areas, and labeling the reference grid cells using the first area index to obtain labeled grid cells; the preset conditions are used to clarify the index values ​​and selection conditions of various influencing factors in the reference area;

[0009] Performing a weighted analysis on the second regional index to obtain target scores for each labeled grid cell under different weights, and determining a target grid cell based on the target score and the slope-area ratio of the labeled grid cell; the second regional index is used to characterize the type and quantity of mineral features, biological features, and slope features in the labeled grid cell;

[0010] A third regional index is determined from the target grid unit, and a target selection scheme is determined based on the third regional index; the third regional index is used to characterize the amount of crust resources corresponding to the target grid unit.

[0011] According to another aspect of the present invention, a regional optimization device for ferromanganese crust minerals is provided, comprising:

[0012] A grid determination module is used to divide a reference area to obtain reference grid units; the reference area is an area where crust minerals need to be optimized, and the reference area can be divided into a number of polygonal areas according to different influencing factors; the polygonal areas are the ranges of spatial distribution of different influencing factors; the influencing factors are resource factors, environmental factors, and mining factors that affect the optimization of minerals in the reference area;

[0013] A regional index determination module is configured to determine a first regional index based on exploration specifications and influencing factor data; the first regional index is configured to characterize the type and quantity of mineral characteristics, biological characteristics, and slope characteristics in a polygonal area corresponding to a reference area; the influencing factor data is configured to characterize the mineral characteristics, biological characteristics, and slope characteristics of the reference area;

[0014] a labeled grid unit determination module, configured to assign values ​​to polygonal areas divided into the reference area according to preset conditions to obtain target polygonal areas, and to label the reference grid units using the first area index to obtain labeled grid units; the preset conditions are used to specify the index values ​​and selection conditions of various influencing factors within the reference area;

[0015] a target grid unit determination module, configured to perform a weighted analysis on the second regional index to obtain a target score for each labeled grid unit under different weights, and determine the target grid unit based on the target score and the slope-area ratio of the labeled grid unit; the second regional index is used to characterize the type and quantity of mineral characteristics, biological characteristics, and slope characteristics in the labeled grid unit;

[0016] The target selection scheme determination module is used to determine a third regional index from the target grid unit and determine a target selection scheme according to the third regional index; the third regional index is used to characterize the crust resource amount corresponding to the target grid unit.

[0017] According to another aspect of the present invention, an electronic device is provided, comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the regional optimization method for ferromanganese crust minerals according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the regional optimization method for ferromanganese crust minerals according to any embodiment of the present invention when executed.

[0022] The technical solution of the embodiment of the present invention divides the reference area to obtain reference grid units; determines a first regional index based on exploration specifications and influencing factor data, which can quantify the types and parameters of the influencing factors contained in the reference area, providing a basis for the subsequent assignment of polygonal areas; assigns values ​​to the polygonal areas divided by the reference area according to preset conditions to obtain target polygonal areas, and labels the reference grid units using the first regional index to obtain labeled grid units; performs a weighted analysis on the second regional index to obtain target scores for each labeled grid unit under different weights, and determines the target grid unit based on the target score of the labeled grid unit and the slope area ratio; determines a third regional index from the target grid unit, and determines a target selection scheme based on the third regional index, which can determine the optimal resource distribution ratio that maximizes the amount of crust resources in the target grid. This method grids the reference area and selects the optimal area based on the distribution ratio of resources within the grid cells. On the basis of fully considering resource, environmental and mining factors, it uses quantitative means to comprehensively and integratedly analyze different types of data information and isolated parameter indicators, thereby improving the accuracy of regional optimization results; it also enables the selected areas to achieve the requirements of maximizing mineral resource reserves, protecting important biological communities and facilitating mineral mining.

[0023] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A flow chart of a method for regional optimization of ferromanganese crust minerals provided in an embodiment of the present invention;

[0026] Figure 2 A flowchart of obtaining mineral characteristic indicators from a grid unit provided by an embodiment of the present invention;

[0027] Figure 3 A flowchart of obtaining biometric indicators from a grid unit provided by an embodiment of the present invention;

[0028] Figure 4 A flow chart of obtaining slope characteristic index of a grid unit provided by an embodiment of the present invention;

[0029] Figure 5 A schematic diagram of the change in crust resource quantity under different candidate weights provided by an embodiment of the present invention;

[0030] Figure 6 A flow chart of another method for regional optimization of ferromanganese crust minerals provided in an embodiment of the present invention;

[0031] Figure 7 A schematic structural diagram of a regional optimization device for ferromanganese crust minerals provided by an embodiment of the present invention;

[0032] Figure 8 A schematic structural diagram of an electronic device for implementing a method for regional optimization of ferromanganese crust minerals according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] Figure 1 This is a flow chart of a method for regional optimization of ferromanganese crust minerals provided by an embodiment of the present invention. This embodiment is applicable to the case of optimizing blocks within the distribution area of ​​ferromanganese crust minerals. The method can be executed by a regional optimization device for ferromanganese crust minerals. The regional optimization device for ferromanganese crust minerals can be implemented in the form of hardware and / or software. The regional optimization device for ferromanganese crust minerals can be configured in any electronic device with network communication function. Figure 1 As shown, the method includes:

[0036] S110 , dividing the reference area to obtain reference grid units.

[0037] Among them, the reference area is the area where crust minerals need to be optimized, and the reference area can be divided into several polygonal areas according to different influencing factors.

[0038] The polygonal area represents the spatial distribution of different influencing factors, which include resource factors, environmental factors, and mining factors that affect the selection of mineral resources within the reference area.

[0039] Furthermore, the polygonal area of ​​the mining area is determined by superimposing the geological sampling stations using the engineering spacing method and the Thiessen polygon method, which represents the control range of the geological sampling stations in the geographic space.

[0040] Among them, the engineering spacing method is used to determine the control range of geological sampling stations by generating buffer zones based on the average spacing of geological sampling stations.

[0041] The Thiessen polygon method is a spatial segmentation method based on geometric principles. The resulting polygonal region is composed of a continuous polygon formed by a set of perpendicular bisectors connecting two adjacent lines. For a given set of geological sampling stations, each station corresponds to a Thiessen polygon, and the distance from any point within this polygon to that station is less than the distance to every other station.

[0042] Specifically, each square area of ​​a preset size in the reference area is divided according to a preset grid size to obtain reference grid units.

[0043] For example, for every 20km in the reference area 2 The square block is subdivided into 16 square grid units of approximately 1.12 km × 1.12 km, forming a reference grid unit that fully covers the area.

[0044] S120. Determine the first regional index according to the exploration specifications and influencing factor data.

[0045] Among them, the first regional index is used to characterize the type and quantity of mineral characteristics, biological characteristics and slope characteristics in the polygonal area corresponding to the reference area.

[0046] Furthermore, the first regional index includes: a first factor index, a second factor index, and a third factor index. The first factor index is used to characterize the amount of crustal resources within the region; the second factor index is used to characterize the membership of the most important biome type within the region; and the third factor index is used to characterize the slope distribution range within the region according to a preset slope.

[0047] Among them, the membership value is used to characterize the importance of the biological community type. The larger the membership value, the more important the corresponding biological community is, which means it needs to be protected and cannot be destroyed.

[0048] Among them, the influencing factor data are used to characterize the mineral characteristics, biological characteristics and slope characteristics of the reference area.

[0049] Specifically, the influencing factor data of the polygonal area divided by the reference area is determined, the corresponding mineral, biological and slope characteristic indicators are determined according to the influencing factor data, and the various characteristic indicators obtained are integrated to obtain the first area indicator.

[0050] Furthermore, since there is more than one reference grid, more than one first area index is obtained, so they are mapped using a dictionary or table, such as dict = {reference grid 1: "first area index 1", reference grid 2: "first area index 2", ...}.

[0051] Furthermore, the first factor assesses the target metal's resource abundance based on the metal's value within the area. The second factor is based on the most important biome types and their corresponding membership levels within the area. The third factor determines the ratio of the slope area required for mining to the reference grid area based on the area's slope data.

[0052] Wherein, the target metal may be metallic cobalt.

[0053] S130 , assigning values ​​to polygonal areas divided by the reference area according to preset conditions to obtain target polygonal areas, and labeling reference grid units using a first area index to obtain labeled grid units.

[0054] Among them, the preset conditions are used to clarify the index values ​​and selection conditions of various influencing factors in the reference area.

[0055] The preset conditions may include: the crust thickness is not less than the preset crust thickness and the target metal grade is not less than the preset mineral grade, and the slope data is greater than the preset slope. The preset crust thickness can be 4cm; the preset mineral grade can be 0.35%; the preset slope is 25°

[0056] Specifically, the polygonal areas divided by the reference area are screened according to preset conditions. The polygonal areas that meet the conditions of crust thickness greater than the preset crust thickness and target metal grade greater than the preset mineral product grade are used as mining area polygonal areas. The polygonal areas divided according to the preset slope are used as slope polygonal areas. The ranges of different types of biomes in the reference area are divided according to the preset biome importance to obtain biome polygonal areas. The mining area polygonal areas, biome polygonal areas, and slope polygonal areas are assigned values ​​respectively to obtain the target polygonal area. The target polygon is superimposed on the reference grid cell, and the reference grid cell is annotated with various characteristic indicators of the target polygon to obtain an annotated grid cell.

[0057] For example, Figure 2 The figure shows a schematic diagram of the mining area polygon with a crust thickness greater than the preset crust thickness and a target metal grade greater than the preset mineral product grade superimposed on the reference grid unit. It can be seen from the figure that the reference grid unit intercepts the mining area polygon, and part of the mining area polygon is allocated to Figure 2 The rest of the parts are allocated to other reference grid cells.

[0058] For example, Figure 3 As shown in the figure, the biome polygon is superimposed on the reference grid cell. It can be seen from the figure that the reference grid cell intercepts the biome polygon and the part of the biome polygon is allocated to Figure 3 The rest of the grid is allocated to other reference grid cells, and different colors represent different biological populations.

[0059] For example, Figure 4 The figure shows a schematic diagram of the slope distribution area with slope data greater than the preset slope superimposed on the reference grid unit. It can be seen from the figure that the reference grid unit intercepts the mining area polygon, and part of the slope distribution area is allocated to Figure 4The rest of the parts are allocated to other reference grid cells.

[0060] S140 , performing weighted analysis on the second regional index to obtain target scores of each marked grid cell under different weights, and determining a target grid cell according to the target score of the marked grid cell and the slope area ratio.

[0061] Among them, the second regional index is used to characterize the type and quantity of mineral features, biological features and slope features in the marked grid cells.

[0062] Specifically, a second regional index is determined from the target polygonal area and the reference grid cells. The second regional index is weighted and summed using a preset ratio to obtain a target score for the labeled grid cells. The target scores of the labeled grid cells are sorted to obtain candidate labeled grid cells whose scores meet the preset score. The candidate labeled grid cells whose scores meet the preset score are then screened based on the slope-area ratio to obtain the target grid cell.

[0063] Furthermore, the specific process of determining the target score is as follows: normalizing the second regional indicator, unifying the data representing different factors into the same numerical range, and calculating through a weighted model according to a preset ratio to obtain the target score of the marked grid unit.

[0064] The weighted model can be expressed as follows:

[0065] Score = resource factor × resource factor weight × 1 + environmental factor × environmental factor weight × (-1)

[0066] Resource factor weight + environmental factor weight = 1,

[0067] Among them, Score is the target score of each marked grid unit, resource factor is the normalized first factor indicator, and environmental factor is the second factor indicator.

[0068] Furthermore, the positive and negative settings of the weights are because mineral factors are favorable for regional optimization, so the weight of mineral factors should be multiplied by 1; environmental factors are unfavorable for regional optimization, so the weight of environmental factors should be multiplied by -1.

[0069] S150: Determine a third area index from the target grid unit, and determine a target selection scheme based on the third area index.

[0070] Among them, the third regional indicator is used to characterize the amount of crust resources corresponding to the target grid unit.

[0071] Specifically, the third regional index is determined from the target grid unit, and the crust resource quantity of the third regional index is evaluated through the candidate weight combination. The grid unit area corresponding to the candidate weight combination when the crust resource quantity change curve begins to have an inflection point is used as the target selection scheme.

[0072] The candidate weights are used to characterize the impact of mineral distribution and biome distribution on crust resource abundance in the first and second factor indicators. Different weight combinations will result in different crust resource abundance.

[0073] For example, Figure 5 The figure below shows the change in crust resource abundance under different weights. It can be seen from the figure that when the candidate weight is 7:3, the crust resource abundance begins to decline significantly. Therefore, if the candidate weight is 7:3, the target selection scheme is to retain the target grid cells with a mineral resource and biological community ratio of 7:3.

[0074] For example, Figure 6 As shown in the figure, resource factors, environmental factors, and mining factors are first determined based on the collected data. The mineral, biological, and slope characteristics corresponding to these three factors are then assigned to a grid cell group divided according to a preset grid size, namely the reference grid cell. The reference grid cell and the corresponding target polygon area are superimposed and annotated to obtain grid cells containing mineral, biological, and slope information, namely the annotated grid cells. The annotated grid cells are then screened according to the candidate weights to determine the target grid cells to be retained or discarded. Among them, mineral characteristics are used to determine the first factor indicator; biological characteristics are used to determine the second factor indicator; and slope characteristics are used to determine the third factor indicator.

[0075] Optionally, determining the first regional index according to the exploration specification and influencing factor data includes steps A1-A5:

[0076] Step A1: Determine influencing factor data.

[0077] The influencing factor data is used to characterize the type and quantity distribution of mineral characteristics, biological characteristics, and slope characteristics within the reference area. The influencing factor data includes at least: mineral resource data, biological community data, and mining slope data.

[0078] For example, mineral resource data: a data layer of the spatial distribution of ferromanganese crust minerals, which mainly includes: the crust thickness, metal grade, wet density and moisture content index values ​​of the sample and the control area of ​​the geological station, wherein the control area of ​​the geological station is the area of ​​the polygon corresponding to the geological sampling station.

[0079] Furthermore, biome data: a data layer showing the spatial distribution of biomes. This layer consists of several different types of regions, each representing a different combination of biomes, and each type of biome has different importance. The biomes are ranked by importance, and the biocodes are assigned values ​​of 1, 2, ..., and so on, from low to high.

[0080] Furthermore, mining slope data: terrain slope data layer, which is calculated by spatial analysis of bathymetric data.

[0081] Step A2: Determine the first factor index based on the mineral resource data.

[0082] Among them, the first factor indicator is used to characterize the corresponding crust resources in the area.

[0083] Specifically, the mineral resource data is screened, and the first factor index is determined based on the screened data.

[0084] For example, since the wet density and moisture content of crusts have little variation, they are eliminated, and the crust thickness, target metal grade, and control area are retained. The first factor index is obtained by multiplying the three obtained index values.

[0085] Furthermore, the first factor index can be expressed as follows:

[0086] R=H×CEG×S

[0087] CEG=d / a×Mn+Co+b / a×Ni+c / a×Cu,

[0088] Where R is the first factor indicator; H is the crust thickness of each polygon within the reference area; and S is the area of ​​the polygonal region. CEG is the target metal grade, calculated based on the international market values ​​of manganese (Mn), copper (Cu), cobalt (Co), and nickel (Ni). This is calculated by averaging the values ​​of Co at a USD / ton, Ni at b USD / ton, Cu at c USD / ton, and Mn at d USD / ton, assuming the Co metal price is 1 and summing the Mn, Cu, and Ni prices converted to the target metal prices.

[0089] In the above steps, if the crust thickness is large, the target metal grade is high, and the controlled area is large, it means that the corresponding reference grid has good resource endowment and can contain more target metals.

[0090] Step A3: Determine the second factor index based on the biome data.

[0091] Among them, the second factor index is used to characterize the membership of the most important biological community type in the region.

[0092] Specifically, according to the type and importance of the biological community, the membership value of the biological community type is determined, and the obtained membership value is used as the second factor indicator.

[0093] For example, the membership value in the second factor index can be expressed by the following formula:

[0094] ∑μ i =1,(0<μ i <1; i=1,2,…,n),

[0095] Among them, μ i is the membership value; n represents the number of different biological communities.

[0096] Step A4: Determine the third factor index based on the mining slope data.

[0097] Among them, the third factor index is used to characterize the slope distribution range corresponding to the preset slope in the area.

[0098] Specifically, mining slope data is selected according to preset slope data, and the third factor index is determined according to the selected slope data.

[0099] For example, the third factor index can be expressed by the following formula:

[0100] P M =S Ms / S cell ,(0≤P M ≤1),

[0101] Among them, P M is the third factor indicator, S Ms is the area of ​​the mining area with a slope greater than 25° within the reference grid cell, S cell is the area of ​​the reference grid cell.

[0102] Step A5: Use the first factor index, the second factor index, and the third factor index as the first regional index.

[0103] Specifically, the first factor index, the second factor index and the third factor index are integrated to obtain the first regional index.

[0104] Optionally, assigning values ​​to polygonal areas divided by the reference area according to preset conditions to obtain a target polygonal area includes steps B1-B4:

[0105] Step B1: Divide the geological sampling station control range in the reference area according to the preset crust thickness and the preset mineral product position to obtain a polygonal area of ​​the mining area.

[0106] Specifically, the polygonal areas in which the crust thickness is greater than the preset thickness and the target metal grade is greater than the preset mineral product grade among the polygonal areas divided by the reference area are regarded as mineral polygonal areas.

[0107] For example, polygons in the polygonal areas divided by the reference area with a crust thickness of not less than 4 cm and a metal cobalt grade of not less than 0.35% are retained as mining areas, and polygons that do not meet the above conditions are deleted as non-mining areas.

[0108] Step B2: Divide the ranges of different types of biomes within the reference area according to the preset biome importance to obtain biome polygonal areas.

[0109] Specifically, the polygonal areas where different types of biomes are divided in the reference area are located are used as biome polygonal areas.

[0110] Step B3: Divide the slope distribution range of the reference area according to the preset slope to obtain slope polygonal areas.

[0111] Specifically, the slope data of the polygonal areas divided by the reference area are compared with the preset slope, the distribution range of the slope greater than the preset slope is divided, and the polygonal areas with a slope greater than the preset slope are merged and retained to obtain the slope polygonal area.

[0112] Exemplarily, the slope data is divided into two categories: no more than 25° and greater than 25°. The part with a slope no more than 25° is deleted, and the polygons with a slope greater than 25° are merged to generate a polygonal area with a slope greater than 25° as the slope polygon area.

[0113] In the above steps, the preset slope is set to determine whether the current polygonal area is suitable for mining, that is, the area with a slope not greater than the critical value is conducive to mining, and the area with a slope greater than the critical value is not conducive to mining.

[0114] Step B4: Assign values ​​to the mining area polygon area, the biome polygon area, and the slope polygon area respectively to obtain the target polygon area.

[0115] Specifically, the mining area polygonal area, the biome polygonal area, and the slope polygonal area are overlapped according to their positions in the reference grid to obtain a target polygonal area.

[0116] Optionally, labeling the reference grid cells using the first area index to obtain the labeled grid cells includes steps C1-C2:

[0117] Step C1: determining a second area index according to the target polygonal area and the reference grid unit.

[0118] Specifically, the corresponding regional index data is determined from the first regional index according to the reference grid unit number corresponding to the target polygonal region, and is used as the second regional index.

[0119] Step C2: label the reference grid unit using the second area index to obtain a labeled grid unit.

[0120] Specifically, the reference grid unit number is matched with the corresponding second area indicator data, and the matched second area indicator is used for labeling to obtain a labeled grid unit.

[0121] Exemplarily, the labeling process is: adding the second indicator data to the attribute table corresponding to the reference grid unit according to the corresponding data attributes.

[0122] For example, the marking method may use the Join tool.

[0123] Optionally, determining the second area index according to the target polygonal area and the first area index includes steps D1-D3:

[0124] Step D1: Determine the equivalent resource volume within the marked grid cell based on the mining area polygon area and the reference grid cell.

[0125] The equivalent resource volume is the product of the crust thickness, equivalent grade and controlled area within the marked grid cell.

[0126] Specifically, according to the polygonal area of ​​the mining area, three characteristic indicators of crust thickness, equivalent grade and controlled area corresponding to the reference grid unit are matched from the first factor indicator, and the three characteristic indicators are multiplied to obtain the equivalent resource volume.

[0127] Step D2: Determine the membership degree corresponding to the most important biome type in the marked grid cell based on the biome polygon area and the reference grid cell.

[0128] Specifically, the membership value of the most important biome type corresponding to the reference grid unit is matched from the second factor indicator data according to the biome polygon area, and the obtained membership value corresponding to the most important biome type is used as the biome information.

[0129] Step D3: Determine the ratio of the area of ​​the mining area that meets the preset slope in the marked grid cell to the area of ​​the grid cell according to the slope polygon area and the reference grid cell.

[0130] Specifically, the slope polygon area is matched to the corresponding slope data from the third factor indicator, and the area of ​​the mining area polygon with a greater than preset slope is calculated based on the preset slope. The areas of the slope polygons within the grid cells are combined and summed, and then divided by the area of ​​the grid cell to obtain the ratio of the mining area to the area of ​​the grid cell.

[0131] Optionally, determining the target grid cell according to the target score and slope area ratio of the marked grid cell includes steps E1-E2:

[0132] Step E1: sort the target scores of the marked grid cells, retain the marked grid cells with target scores greater than a preset score, and obtain the grid cells to be screened.

[0133] Specifically, the target scores of the marked grid cells are sorted in descending order, and the grid cells with target scores greater than a preset score are retained as the grid cells to be screened.

[0134] Step E2: Grid cells whose slope area ratio is smaller than a preset slope area ratio among the grid cells to be screened are used as target grid cells.

[0135] For example, assuming that the preset slope area ratio is 50%, the grid cells with slope area ratios less than 50% in the grid to be screened are used as target grid cells.

[0136] Optionally, a third area index is determined from the target grid unit, and a target selection scheme is determined according to the third area index, including steps F1-F3:

[0137] Step F1: Obtain a third area index from the target grid cell.

[0138] Specifically, the third area index is obtained from the attribute table of the target grid cell.

[0139] Step F2: Determine a plurality of weighted calculation results based on the third regional index and the candidate weight combinations.

[0140] The candidate weight combination is the weight of the resource factors and environmental factors in the pre-set target grid unit.

[0141] Furthermore, the candidate weight combinations include: resource factor weight assignment of 1.0, environmental factor weight assignment of 0; resource factor weight assignment of 0.9, environmental factor weight assignment of 0.1; resource factor weight assignment of 0.8, environmental factor weight assignment of 0.2; resource factor weight assignment of 0.7, environmental factor weight assignment of 0.3; resource factor weight assignment of 0.6, environmental factor weight assignment of 0.4; resource factor weight assignment of 0.5, environmental factor weight assignment of 0.5.

[0142] Specifically, the crust resource volume corresponding to the target grid cell is calculated based on the candidate weight combination and the third regional index. Since there is more than one set of candidate weight combinations, more than one normalized crust resource volume result is obtained.

[0143] Step F3: Determine a target weight from the candidate preset weights based on a number of weighted calculation results, and determine a target selection scheme for the grid unit area corresponding to the target weight.

[0144] Specifically, a target weight is determined from candidate preset weights according to a number of weighted calculation results, and a target selection scheme is constructed according to the target weight.

[0145] For example, Figure 5 As shown, when the weight is 10:0, the crust resource volume is 1; when the weight is 9:1, the crust resource volume is about 0.996; when the weight is 8:2, the crust resource volume is about 0.995; when the weight is 7:3, the crust resource volume is about 0.992; when the weight is 6:4, the crust resource volume is about 0.972; when the weight is 5:5, the crust resource volume is about 0.929.

[0146] Furthermore, when the weight is 7:3, the crust resource variation curve begins to show an inflection point, after which the crust resource shows a clear downward trend. Based on this, 7:3 is used as the target weight, and the target selection scheme is to retain the target grid area where the ratio of resource factors to environmental factors is 7:3.

[0147] The technical solution of this embodiment divides the reference area to obtain reference grid units; determines the first regional index based on the exploration specifications and influencing factor data, which can quantify the types and parameters of the influencing factors contained in the reference area, providing a basis for the subsequent assignment of polygonal areas; assigns values ​​to the polygonal areas divided by the reference area according to preset conditions to obtain target polygonal areas, and labels the reference grid units using the first regional index to obtain labeled grid units; performs a weighted analysis on the second regional index to obtain target scores for each labeled grid unit under different weights, and determines the target grid unit based on the target score of the labeled grid unit and the slope area ratio; determines the third regional index from the target grid unit, and determines the target selection scheme based on the third regional index, which can determine the optimal resource distribution ratio that maximizes the amount of crust resources in the target grid. This method grids the reference area and selects the optimal area based on the distribution ratio of resources within the grid cells. On the basis of fully considering resource, environmental and mining factors, it uses quantitative means to comprehensively and integratedly analyze different types of data information and isolated parameter indicators, thereby improving the accuracy of regional optimization results; it also enables the selected areas to achieve the requirements of maximizing mineral resource reserves, protecting important biological communities and facilitating mineral mining.

[0148] Figure 7This is a schematic diagram of the structure of a regional optimization device for ferromanganese crust minerals provided by an embodiment of the present invention. This embodiment is applicable to the case of optimizing blocks within the ferromanganese crust area. The regional optimization device for ferromanganese crust minerals can be implemented in the form of hardware and / or software. The regional optimization device for ferromanganese crust minerals can be configured in any electronic device with network communication function. Figure 7 As shown, the device includes: a grid determination module 210, a region index determination module 220, a marked grid unit determination module 230, a target grid unit determination module 240 and a target selection scheme determination module 250, wherein:

[0149] Grid determination module 210: used to divide the reference area to obtain reference grid cells; the reference area is the area where crust minerals need to be selected, and the reference area can be divided into a number of polygonal areas according to different influencing factors; the polygonal areas are the ranges of spatial distribution of different influencing factors; the influencing factors are resource factors, environmental factors, and mining factors that affect the selection of minerals in the reference area;

[0150] Regional index determination module 220: used to determine a first regional index based on the exploration specification and the influencing factor data; the first regional index is used to characterize the type and quantity of mineral characteristics, biological characteristics, and slope characteristics in the polygonal area corresponding to the reference area; the influencing factor data is used to characterize the mineral characteristics, biological characteristics, and slope characteristics of the reference area;

[0151] The labeled grid unit determination module 230 is configured to assign values ​​to polygonal regions divided into the reference region according to preset conditions to obtain target polygonal regions, and to label the reference grid units using a first region index to obtain labeled grid units. The preset conditions are used to specify the index values ​​and selection conditions of various influencing factors within the reference region.

[0152] Target grid unit determination module 240 is configured to perform a weighted analysis on the second regional index to obtain target scores for each labeled grid unit under different weights, and to determine the target grid unit based on the target score and slope-area ratio of the labeled grid unit; the second regional index is used to characterize the type and quantity of mineral features, biological features, and slope features in the labeled grid unit;

[0153] The target selection scheme determination module 250 is used to determine a third regional index from the target grid unit and determine a target selection scheme according to the third regional index; the third regional index is used to characterize the amount of crust resources corresponding to the target grid unit.

[0154] Optionally, the regional indicator determination module 220 includes:

[0155] Influencing factor data determination unit: used to determine influencing factor data; influencing factor data is used to characterize the type and quantity distribution of mineral characteristics, biological characteristics and slope characteristics in the reference area; influencing factor data at least includes: mineral resource data, biological community data and mining slope data;

[0156] A first factor index determination unit is used to determine a first factor index based on mineral resource data; the first factor index is used to characterize the corresponding crust resource amount in the area;

[0157] A second factor index determining unit is used to determine a second factor index based on the biome data; the second factor index is used to represent the membership degree of the most important biome type corresponding to the region;

[0158] A third factor index determination unit is used to determine a third factor index based on the mining slope data; the third factor index is used to characterize the slope distribution range corresponding to the preset slope in the area;

[0159] The first region index determining unit is configured to use the first factor index, the second factor index, and the third factor index as the first region index.

[0160] Optionally, the labeling grid unit determination module 230 includes:

[0161] Mining area polygonal region determination unit: used to divide the geological sampling station control range in the reference area according to the preset crust thickness and the preset mineral product position to obtain the mining area polygonal region;

[0162] Biome polygon area determination unit: divides the ranges of different types of biomes in the reference area according to the importance of preset biomes to obtain biome polygon areas;

[0163] Slope polygon area determination unit: used for dividing the slope distribution range of the reference area according to the preset slope to obtain the slope polygon area;

[0164] Target polygon area determination unit: used to assign values ​​to the mining area polygon area, the biological community polygon area and the slope polygon area respectively to obtain the target polygon area.

[0165] Optionally, the labeling grid unit determination module 230 includes:

[0166] A second area index determining unit: configured to determine a second area index according to the target polygonal area and the reference grid unit;

[0167] The labeled grid unit determination unit is used to label the reference grid unit through the second area index to obtain the labeled grid unit.

[0168] Optionally, the second area indicator determination unit includes:

[0169] Mineral Resource Determination Unit: Used to determine the equivalent resource within the marked grid cell based on the mining polygon area and the reference grid cell; the equivalent resource is the product of the crust thickness, equivalent grade and controlled area within the marked grid cell;

[0170] Biome information determination unit: used to determine the membership degree corresponding to the most important biome type in the marked grid cell based on the biome polygon area and the reference grid cell;

[0171] Slope area ratio determination unit: used to determine the ratio of the area of ​​the mining area that meets the preset slope in the marked grid unit to the area of ​​the grid unit based on the slope polygon area and the reference grid unit.

[0172] Optionally, the target grid unit determination module 240 includes:

[0173] Determining units of the grid layer to be screened: used to sort the target scores of the marked grid units, retain the marked grid units with target scores greater than the preset scores, and obtain the grid units to be screened;

[0174] Target grid unit determination unit: used to take the grid units whose slope area ratio is less than the preset slope area ratio among the grid units to be screened as target grid units.

[0175] Optionally, the target selection scheme determination module 250 includes:

[0176] A third area index determining unit: configured to obtain a third area index from a target grid cell;

[0177] Crust resource determination unit: used to determine a number of weighted calculation results based on the third regional index and candidate weight combinations; the candidate weight combinations are the weights of resource factors and environmental factors in the pre-set target grid cell;

[0178] Target selection scheme determination unit: used to determine the target weight from the candidate preset weights according to a number of weighted calculation results, and determine the target grid area corresponding to the target weight as the target selection scheme.

[0179] The regional optimization device for ferromanganese crust minerals provided in the embodiments of the present invention can execute the regional optimization method for ferromanganese crust minerals provided in any of the above embodiments of the present invention, and has the corresponding functions and beneficial effects of executing the regional optimization method for ferromanganese crust minerals. For detailed processes, please refer to the relevant operations of the regional optimization method for ferromanganese crust minerals in the above embodiments.

[0180] Figure 8A schematic diagram of the structure of an electronic device for implementing a regional optimization method for ferromanganese crust minerals according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0181] like Figure 8 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0182] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0183] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for determining the preferred crust resource region.

[0184] In some embodiments, the regional optimization method for ferromanganese crust mineral resources can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the regional optimization method for ferromanganese crust mineral resources described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the regional optimization method for ferromanganese crust mineral resources by any other appropriate means (e.g., by means of firmware).

[0185] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0186] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0187] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0188] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0189] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0190] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0191] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0192] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for regional optimization of ferromanganese crust minerals, characterized in that: include: Divide the reference area to obtain reference grid cells; The reference area is an area where crust minerals need to be optimized, and the reference area can be divided into a number of polygonal areas according to different influencing factors; the polygonal areas are the ranges of spatial distribution of different influencing factors; the influencing factors are resource factors, environmental factors and mining factors that affect the optimization of minerals in the reference area; Determine a first regional index based on the exploration specification and the influencing factor data; the first regional index is used to characterize the type and quantity of mineral characteristics, biological characteristics, and slope characteristics in the polygonal area corresponding to the reference area; the influencing factor data is used to characterize the mineral characteristics, biological characteristics, and slope characteristics of the reference area; Assign values ​​to the polygonal areas divided by the reference area according to preset conditions to obtain the target polygonal area; The method includes labeling the reference grid unit using the first area index to obtain a labeled grid unit, comprising: determining, from the first area index according to the reference grid unit number corresponding to the target polygonal area, corresponding area index data and using the data as a second area index; and labeling the reference grid unit using the second area index to obtain a labeled grid unit. The preset conditions are used to clarify the index values ​​and selection conditions of various influencing factors in the reference area; Performing a weighted analysis on the second regional index to obtain target scores for each labeled grid cell under different weights, and determining a target grid cell based on the target score and the slope-area ratio of the labeled grid cell; the second regional index is used to characterize the type and quantity of mineral features, biological features, and slope features in the labeled grid cell; A third regional index is determined from the target grid unit, and a target selection scheme is determined based on the third regional index; the third regional index is used to characterize the amount of crust resources corresponding to the target grid unit.

2. The method according to claim 1, characterized in that Determining the first regional indicator based on the exploration specifications and influencing factor data includes: Determining influencing factor data; the influencing factor data includes: mineral resource data, biome data and mining slope data; Determining a first factor index based on the mineral resource data; the first factor index is used to characterize the corresponding crust resource amount in the area; Determining a second factor index based on the biome data; the second factor index is used to characterize the membership of the most important biome type corresponding to the region; Determine a third factor index based on the mining slope data; the third factor index is used to characterize the slope distribution range corresponding to the preset slope in the area; The first factor index, the second factor index, and the third factor index are used as first regional indexes.

3. The method according to claim 1, characterized in that Assign values ​​to the polygonal areas divided by the reference area according to preset conditions to obtain the target polygonal area, including: The control range of geological sampling stations in the reference area is divided according to the preset crust thickness and the preset mineral product position to obtain the mining area polygonal area; The ranges of different types of biomes in the reference area are divided according to the importance of the preset biomes to obtain biome polygonal areas; Divide the slope distribution range of the reference area according to the preset slope to obtain a slope polygon area; The mining area polygonal area, the biological community polygonal area and the slope polygonal area are assigned values ​​respectively to obtain a target polygonal area.

4. The method according to claim 3, characterized in that Determining the corresponding regional indicator data from the first regional indicator according to the reference grid unit number corresponding to the target polygonal area and using the data as the second regional indicator includes: Determine the equivalent resource volume within the marked grid cell based on the mining area polygonal area and the reference grid cell; the equivalent resource volume is the product of the crust thickness, equivalent grade and controlled area within the marked grid cell; Determine the membership degree corresponding to the most important biome type within the annotated grid cell based on the biome polygon area and the reference grid cell; The ratio of the mining area that meets the preset slope in the marked grid cell to the area of ​​the grid cell is determined based on the slope polygon area and the reference grid cell.

5. The method according to claim 1, characterized in that Determining a target grid cell according to the target score and the slope area ratio of the marked grid cell includes: Sort the target scores of the marked grid cells, retain the marked grid cells whose target scores are greater than the preset scores, and obtain the grid cells to be screened; The grid cells whose slope area ratio is smaller than the preset slope area ratio among the grid cells to be screened are used as target grid cells.

6. The method according to claim 1, characterized in that Determining a third area index from the target grid unit, and determining a target selection scheme according to the third area index, including: Obtaining a third area index from the target grid cell; Determining a plurality of weighted calculation results based on the third regional indicator and candidate weight combinations; the candidate weight combinations are pre-set weights of resource factors and environmental factors within the target grid unit; A target weight is determined from the candidate preset weights according to the plurality of weighted calculation results, and a target grid area corresponding to the target weight is determined as a target selection scheme.

7. A regional optimization device for ferromanganese crust minerals, characterized in that: include: A grid determination module is used to divide the reference area into reference grid units; The reference area is an area where crust minerals need to be optimized, and the reference area can be divided into a number of polygonal areas according to different influencing factors; the polygonal areas are the ranges of spatial distribution of different influencing factors; the influencing factors are resource factors, environmental factors and mining factors that affect the optimization of minerals in the reference area; A regional index determination module is used to determine a first regional index based on the exploration specification and influencing factor data; the first regional index is used to characterize the type and quantity of mineral characteristics, biological characteristics and slope characteristics in the polygonal area corresponding to the reference area; The influencing factor data is used to characterize the mineral characteristics, biological characteristics and slope characteristics of the reference area; A marking grid unit determination module includes: a second area index determination unit and a marking grid unit determination unit; The labeled grid unit determination module is configured to assign values ​​to polygonal areas divided by the reference area according to preset conditions to obtain a target polygonal area, and to label the reference grid units using the first area index to obtain labeled grid units; the second area index determination unit is configured to determine, based on the reference grid unit number corresponding to the target polygonal area, the area index data corresponding thereto from the first area index, and use the data as the second area index; the labeled grid unit determination unit is configured to label the reference grid units using the second area index to obtain labeled grid units; the preset conditions are configured to clarify the index values ​​and selection conditions of various influencing factors within the reference area; a target grid unit determination module, configured to perform a weighted analysis on the second regional index to obtain a target score for each labeled grid unit under different weights, and determine the target grid unit based on the target score and the slope-area ratio of the labeled grid unit; the second regional index is used to characterize the type and quantity of mineral characteristics, biological characteristics, and slope characteristics in the labeled grid unit; The target selection scheme determination module is used to determine a third regional index from the target grid unit and determine a target selection scheme according to the third regional index; the third regional index is used to characterize the crust resource amount corresponding to the target grid unit.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, wherein the computer program is executed by the at least one processor so as to enable the at least one processor to perform the regional optimization method for ferromanganese crust minerals according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the regional optimization method for ferromanganese crust minerals according to any one of claims 1 to 6 when executed.

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