Intensive development optimization method and system for uranium mine field

Abstract

The embodiment of the invention provides an intensive development optimization method and system for a uranium mine field, and belongs to the technical field of uranium mine field mining optimization. The method comprises the steps that a DBSCAN algorithm and an HAC algorithm are adopted to obtain a plurality of mine field subareas; determining a search area according to each mining field partition area, optimizing the shaft alternative position based on a dung beetle algorithm, and determining a final shaft position; a plurality of hydrometallurgy plant position primary selection points are obtained; an entropy weight method and a preset evaluation method are adopted to determine hydrometallurgy plant position alternative points; planning a transportation route of the ore deposit resources from each ore outlet point to each hydrometallurgy plant position alternative point to obtain an ore transportation scheme corresponding to each hydrometallurgy plant position alternative point; determining a middle-section optimal ore outlet point; and optimizing the ore transportation paths of all ore deposits in each ore field subarea by adopting a grey wolf algorithm to obtain an optimization result. Through the provided scheme, a transportation layout meeting the lowest total cost is formed, and large-scale, intensive and intelligent development of the uranium mine base is realized.

Description

Technical Field

[0001] This application relates to the field of uranium field mining optimization technology, and in particular to a method and system for intensive development and optimization of uranium fields. Background Technology

[0002] The current development of uranium ore fields consisting of scattered small and medium-sized ore bodies is difficult to meet the needs of large-scale development. Traditional optimization methods have not built a global optimization system covering ore field zoning, shaft site selection, hydrometallurgical plant layout and transportation network, and cannot take into account multiple factors such as development system planning and surface transportation roads for large-scale ore deposit development. Summary of the Invention

[0003] To address the aforementioned technical problems, embodiments of this application provide a method and system for the intensive development and optimization of uranium ore fields.

[0004] In a first aspect, embodiments of this application provide an optimized method for the intensive development of uranium ore fields, the method comprising: The DBSCAN algorithm is used to perform preliminary clustering of the ore bodies in each ore deposit to obtain multiple ore resource-intensive areas. The HAC algorithm is then used to perform partitioning and aggregation of the multiple ore resource-intensive areas within the overall mining field to obtain multiple mining field partition areas. The search area is determined according to the respective mining area zones. The search area is then divided into grids to obtain multiple search units. One of the multiple search units is randomly selected as the initial shaft candidate location. The total transport work from each ore block to the initial shaft candidate location is calculated. The shaft candidate location is optimized based on the dung beetle algorithm. With the principle of minimizing transport work, the shaft candidate location is continuously adjusted through spatial iterative search to obtain the target shaft candidate location with the lowest total transport work. The final shaft location is determined based on the target shaft candidate location and the existing shaft location. The weighted distance algorithm, dung beetle optimization algorithm, and centroid method were used to initially select the locations of the hydrometallurgical plant, resulting in multiple preliminary selection points. Based on key factors for hydrometallurgical plant site selection, a hydrometallurgical plant location evaluation model was constructed. The entropy weight method and a pre-defined evaluation method were used to comprehensively evaluate each of the preliminary selection points, resulting in candidate locations for the hydrometallurgical plant. Finally, transportation routes for mineral resources from each ore extraction point to each candidate hydrometallurgical plant location were planned, yielding ore transportation schemes corresponding to each candidate hydrometallurgical plant location. Before mining begins, intermediate roadways are divided according to the distribution of mineral resources and mining technology. The whale optimization algorithm is used to find the location with the minimum transportation work from the mineral resource point to the intermediate roadway, which is taken as the optimal ore extraction point in the intermediate section. The gray wolf algorithm is used to optimize the ore transportation path of all mineral deposits in each mining area, and the optimization results are obtained. The optimization results include the construction location of the regional transportation platform and the ore transportation path that minimizes the total ore transportation cost of the region.

[0005] Secondly, embodiments of this application provide an intensive development optimization system for uranium ore fields, the system comprising: The partitioning module is used to perform preliminary clustering of ore bodies in each ore deposit using the DBSCAN algorithm to obtain multiple ore resource-intensive areas. The HAC algorithm is then used to perform partitioning and aggregation of the multiple ore resource-intensive areas within the overall mining field to obtain multiple mining field partitioning areas. The shaft location processing module is used to determine the search area according to the respective mining area zones, divide the search area into grids to obtain multiple search units, randomly select one unit from the multiple search units as the initial shaft candidate location, calculate the transportation work of each ore block to the initial shaft candidate location, optimize the shaft candidate location based on the dung beetle algorithm, and continuously adjust the shaft candidate location through spatial iterative search to obtain the target shaft candidate location with the lowest total transportation work, based on the principle of minimizing transportation work. The final shaft location is determined based on the target shaft candidate location and the existing shaft location. The hydrometallurgical plant location processing module is used to perform initial site selection for hydrometallurgical plants using weighted distance algorithm, dung beetle optimization algorithm, and centroid method, respectively, to obtain multiple initial site selection points. Based on key factors for hydrometallurgical plant site selection, a hydrometallurgical plant location evaluation model is constructed. Using entropy weight method and preset evaluation method, the initial site selection points are comprehensively evaluated to obtain candidate hydrometallurgical plant locations. Finally, the module plans the transportation routes of mineral resources from each ore extraction point to each candidate hydrometallurgical plant location, obtaining ore transportation schemes corresponding to each candidate hydrometallurgical plant location. The transportation route optimization module is used to divide the intermediate roadways according to the distribution of mineral resources and mining technology before mining begins. The whale optimization algorithm is used to find the location with the minimum transportation work from the mineral resource point to the intermediate roadway, which is taken as the optimal ore extraction point in the intermediate section. The gray wolf algorithm is used to optimize the ore transportation routes of all mineral deposits in each mining area, and the optimization results are obtained. The optimization results include the construction location of the regional transportation platform and the mine head transportation route that minimizes the total ore transportation cost of the region.

[0006] The aforementioned method and system for intensive development optimization of uranium ore fields provided in this application employs a spatial clustering algorithm to intelligently partition ore field resources. Density- and hierarchical clustering algorithms are used to cluster the internal areas of the ore deposit and the overall ore field area, respectively, achieving rational partitioning of ore field resources in two stages and providing a basis for intensive ore field development. The dung beetle algorithm is used to optimize the location of ore shafts, employing global and local search strategies to find the grid unit with the minimum transportation effort. Based on this, the optimal shaft location is determined after comprehensive evaluation, considering the existing shaft locations, geological conditions, and environmental impact factors, providing a reasonable surface ore extraction location for the underground development and transportation system. A combination of qualitative and quantitative methods is used to optimize the location of hydrometallurgical plants. Site selection principles are established, multiple methods are used, and various influencing factors are quantitatively described, achieving initial site selection for hydrometallurgical plants. A site selection evaluation index system is constructed, comprehensively considering geological, water source, transportation, and environmental factors to qualitatively optimize the location of hydrometallurgical plants, providing surface transportation routes and transportation endpoints for ore transportation. By using the whale algorithm to optimize the optimal convergence and extraction location of resources in the middle section, the total transportation work of ore convergence within the middle section is minimized. At the same time, based on the existing development engineering design of the uranium mine base, the location of the transportation platform is reasonably optimized and selected, transforming the complex regional transportation network into a network flow problem. A system network model is established, and the gray wolf algorithm is used to perform intensive optimization of the development transportation system, resulting in the optimal connection location of the transportation platform in each zone and the optimal transportation path for transporting ore from each deposit to the hydrometallurgical plant.

[0007] In summary, a comprehensive solution for the intensive development of uranium mining bases, encompassing ore field zoning, shaft site selection, hydrometallurgical plant layout, and transportation network optimization, is proposed. This solution aims to create a development and transportation network layout that meets the needs of the entire mine lifecycle and minimizes total costs, thereby enabling the large-scale, intensive, and intelligent development of uranium mining bases. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be considered as a limitation on the scope of protection of this application. In the various drawings, similar components are numbered similarly.

[0009] Figure 1 A schematic flowchart of an optimized method for intensive development of uranium ore fields provided in an embodiment of this application is shown. Figure 2 This paper presents a schematic diagram showing the comparison of DBSCAN clustering before and after implementation in an embodiment of this application. Figure 3 This illustration shows a tree-like structure of the clustering results of the HAC clustering algorithm provided in an embodiment of this application. Figure 4This illustration shows the effect of the DBSCAN clustering algorithm on a certain mineral deposit as provided in an embodiment of this application. Figure 5 This illustration shows the effect of the HAC clustering algorithm provided in this embodiment after processing; Figure 6 This illustration shows a schematic diagram of the shaft location optimization process based on the dung beetle algorithm provided in an embodiment of this application; Figure 7 A top view of the resources provided in an embodiment of this application is shown; Figure 8 This illustration shows a schematic diagram of the specific search process based on the dung beetle algorithm provided in an embodiment of this application; Figure 9 This paper illustrates a schematic diagram of candidate locations for a hydrometallurgical plant based on a weighted distance algorithm, as provided in an embodiment of this application. Figure 10 This illustration shows a schematic diagram of the hydrometallurgical plant site selection optimization process provided in an embodiment of this application; Figure 11 A partial schematic diagram of the hydrometallurgical plant site selection optimization process provided in an embodiment of this application is shown; Figure 12 This paper illustrates a schematic diagram of the ore convergence point selection process provided in an embodiment of this application. Figure 13 This illustration shows a schematic diagram of ore convergence point selection provided in an embodiment of this application; Figure 14 This application provides a schematic diagram of the transportation route for a uranium mining base. Figure 15 A schematic diagram illustrating the optimization results of the pioneering system provided in an embodiment of this application is shown. Detailed Implementation

[0010] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0011] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0012] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0013] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0014] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0015] In the fields of engineering optimization, path planning, and parameter tuning of complex mining systems, intelligent algorithms such as clustering algorithms, site selection algorithms, and path optimization algorithms each have their applicable scenarios and advantages and disadvantages. In particular, biomimetic algorithms, represented by the dung beetle algorithm, whale algorithm, and gray wolf algorithm, have shown strong competitiveness in related research due to their unique biomimetic mechanisms and good optimization performance. It should be noted that each type of intelligent optimization algorithm model has its own advantages and focus, and all of them have shown a certain degree of adaptability and practicality in problems such as spatial clustering, site selection optimization, and path optimization. However, existing research mostly focuses on a single link (such as only optimizing transportation routes) and has not yet achieved full-chain collaborative optimization from deposit zoning to the site selection of important facilities and then to transportation routes.

[0016] Since different algorithms have their own advantages and disadvantages in solving specific problems, in the development of special minerals such as uranium mines, the appropriate method should be selected in close combination with the characteristics of the problem, and a hybrid optimization strategy should be adopted to break through the limitations of a single algorithm and achieve the global optimal solution.

[0017] To address the problems in existing technologies, this invention constructs an intensive development optimization scheme for uranium ore fields. Breaking away from the traditional single-deposit development model, it comprehensively utilizes technologies such as spatial clustering and network flow optimization. Through intelligent resource zoning, optimized site selection for shafts and hydrometallurgical plants, and the construction of development and transportation networks, it achieves intensive development of uranium ore field bases, ensuring large-scale mining and construction of mineral resources in the region. This invention is applicable to the upgrading and transformation of sandstone-type uranium ore fields in my country, and its modular design can also be extended to other mineral resource development scenarios.

[0018] This application provides an optimized method for the intensive development of uranium ore fields.

[0019] For details, see Figure 1 The optimization method for intensive development of uranium ore fields includes: Step S101: The DBSCAN algorithm is used to perform preliminary clustering of the ore bodies in each deposit to obtain multiple ore resource-intensive areas. The HAC algorithm is then used to perform partitioning and aggregation of the multiple ore resource-intensive areas within the overall mining field to obtain multiple mining field partition areas.

[0020] In this embodiment, a spatial clustering algorithm is employed to perform clustering analysis on the spatial correlation of mineral resources, intelligently identifying concentrated areas of mineral resources and providing a basis for intensive development. The intelligent zoning model for uranium ore fields consists of two clustering algorithms: DBSCAN and HAC. In the first stage, the DBSCAN algorithm is used for preliminary clustering of ore bodies within each deposit. In the second stage, the HAC algorithm is used for zoning and aggregating the ore deposits within the ore field. Through these two sequential clustering processes, the regional clustering of uranium ore field resources is ultimately achieved, facilitating the regional division for intensive development.

[0021] The exemplary, first stage: preliminary aggregation of ore bodies within the deposit. Based on the uranium resource occurrence morphology, dispersed ore bodies are aggregated into dense areas. First, mining software is used to extract relevant attribute information from the ore deposit block model as raw data. Then, the DBSCAN clustering algorithm is used to aggregate a large number of ore body blocks within the deposit area into dense areas, resulting in resource-dense areas of varying shapes and sizes, thus achieving preliminary resource aggregation within the deposit. See also... Figure 2 , Figure 2 The diagram shows a comparison of DBSCAN clustering before and after. The left side is the diagram before DBSCAN clustering, where there are N1 blocks. The right side is the diagram after DBSCAN clustering, where there are N2 dense clusters, where N2 < N1.

[0022] The second stage involves zoning and clustering ore deposits within the mining area. The preliminary aggregation results from the previous stage are compiled as initial data for spatial clustering of the ore deposit clusters. The HAC clustering algorithm is used to cluster the initially aggregated ore resources across the entire mining area. Corresponding parameters are set according to actual conditions to merge and integrate densely populated ore deposit areas within the mining area, resulting in the final zoning results. (See also...) Figure 3 , Figure 3 The diagram shows a tree structure of the clustering results from the HAC clustering algorithm. Figure 3 The horizontal axis represents the mineral deposit clusters participating in this clustering (i.e., the first-stage clustering results), and the vertical axis represents the distance between cluster mergers.

[0023] The following example illustrates the specific processing steps of the intelligent zoning model for uranium ore fields: The following settings are made when establishing the resource space clustering model: (1) The smallest unit of resources for each mineral deposit is a single block model of the mineral deposit; (2) Use the center coordinates of the block model as the coordinate position of the smallest unit of resources; (3) The clustering process mainly considers the spatial location, grade, quantity and volume information of mineral resources in order to find the density concentration area; (4) Clustering only considers the original data of blocks with a grade higher than industrial grade (0.05%); (5) In the cluster information of the first-stage clustering results, the weight value is the sum of the products of the volume of each block model contained in the cluster and the corresponding block grade; (6) During the second stage of clustering, resource clusters belonging to the same deposit may be clustered into different partitions. At this time, the final selection is made according to the proportion of resource points in the partition.

[0024] For example, based on the DBSCAN clustering algorithm, spatial coordinates, grade, and size data of each deposit block model are extracted as model input indicators. Model parameters (MinPts, Eps) are determined based on the actual data of the deposit block models. Based on N block model data from 21 deposits, attribute indicators such as the central spatial coordinates (x, y, z), average grade (u), number of contained blocks (count), and resource weight (weight) of each deposit cluster are obtained. These are then processed to obtain a total of 4076 clusters of varying sizes. See [link to relevant documentation] Figure 4 , Figure 4 This is a schematic diagram illustrating the effect of the DBSCAN clustering algorithm on a certain mineral deposit.

[0025] Based on the HAC clustering algorithm, the results of the first-stage clustering are used as input indicators. Model parameters (method, metric) are determined according to actual data from the ore deposit block model. Resource spatial clustering is then performed within the mining area. After model solving, the output includes cluster names, the names of the ore deposits contained in each cluster, and a result tree diagram. This tree diagram reveals all the results of hierarchical agglomerative clustering and the order in which ore deposits are merged into the same cluster. Adhering to the intensive approach of "large mines supporting small mines," and considering production scale and ore deposit management efficiency within the partition, the clustering results with appropriate inter-cluster distances are determined as the optimal partitioning. See also... Figure 5 This is a schematic diagram illustrating the effect of the HAC clustering algorithm. According to... Figure 5It can be seen that the mineral resources are roughly divided into two parts. Considering the requirements of subsequent production scale and intensive development, selecting four clustering regions is the optimal partitioning scheme. For example, with several large mineral deposits as the core, the entire mineral field is divided into two relatively concentrated blocks according to geographical proximity. Then, considering the degree of resource aggregation, spatial clustering is performed using an algorithm model. Based on the clustering results of the first stage, each of the two large blocks is further divided into two sub-regions, for a total of four sub-regions.

[0026] Step S102: Determine the search area according to the respective mining area zones, divide the search area into grids to obtain multiple search units; randomly select one unit from the multiple search units as the initial shaft candidate position, calculate the transportation work from each ore block to the initial shaft candidate position, optimize the shaft candidate position based on the dung beetle algorithm, and continuously adjust the shaft candidate position through spatial iterative search to obtain the target shaft candidate position with the lowest total transportation work, based on the principle of minimizing transportation work, and determine the final shaft position based on the target shaft candidate position and the existing shaft position.

[0027] It's worth noting that the dung beetle algorithm, by simulating the rolling behavior of dung beetles, can balance exploration (finding new areas) and development (optimizing known areas) during the search process. In shaft site selection, this means the algorithm can quickly locate promising areas and perform detailed optimization within those areas.

[0028] In this embodiment, the optimization of mine shaft location considers the spatial distribution of the ore body, surface topography, and other characteristics. The dung beetle algorithm is used to select shaft locations, ensuring smooth mining operations and minimizing costs. Shaft location problems typically involve determining the optimal location to achieve a specific objective. Since ore from each intermediate section is transported to the shaft via transport roadways, the resources (i.e., ore resources) are projected onto a horizontal plane, and the horizontal distance from the ore resources to the shaft is calculated. The optimization objective is to minimize the total transport effort of ore resources, i.e., to minimize the horizontal transport effort of all ore to the shaft.

[0029] In one embodiment, the search area is divided into grids to obtain multiple search units, including: In the search area, based on the spatial coordinates of mineral resources and the amount of ore, the spatial coordinates of all mineral resources in the mining field are projected onto the ground surface, and the ore amounts are superimposed to form a resource top view.

[0030] The minimum total transport work for each candidate shaft location is determined using the following formula: ; in, The total transport work for each of the candidate shaft locations is expressed in tons-meters; minZ represents the minimum total transport work. Numbering each ore block; The total number of ore blocks; Number each candidate location for a vertical shaft; For each ore block The amount of ore, in tons; For each ore block To the alternative location of the shaft The straight-line distance between them, in meters.

[0031] The following provides supplementary explanations of the dung beetle algorithm. By applying the dung beetle algorithm, the selection of shaft sites is optimized to improve the scientific rigor of site selection decisions. The study area is defined based on the spatial distribution of the mining area and divided into a series of grid units (i.e., search units). Grid units are selected through random sampling as initial candidate locations for shafts, and the transportation work from each zone of ore to the candidate locations is calculated.

[0032] The dung beetle algorithm, an emerging nature-inspired algorithm, draws inspiration from the navigation and foraging behaviors of dung beetles in nature. With its simple structure, minimal parameter settings, and ease of implementation and adjustment, the algorithm is widely used in research on shaft site selection optimization problems. The core of the algorithm lies in finding the grid cell with the minimum transport work through global search (rolling ball) and local search (foraging) strategies. Based on this, the optimal shaft location is determined after comprehensive evaluation, taking into account the existing shaft's location, geological conditions, and environmental factors.

[0033] In one embodiment, the optimization of candidate shaft locations based on the dung beetle algorithm, using the principle of minimizing transportation work, involves iterative spatial search to continuously adjust candidate shaft locations and obtain the target candidate shaft location with the lowest total transportation work. This includes: A group of dung beetles is randomly generated, and each dung beetle represents a candidate location for a vertical shaft; Determine the first distance between the mineral resource point and each shaft candidate location, and use the cumulative value of the product of the first distance and the ore quantity as the total transportation work for evaluating each shaft candidate location; The search direction for alternative shaft locations is updated by referencing navigation signals; Adjust the position information of each shaft candidate position according to the updated direction and step size to achieve the search of the solution space; A local search mechanism is introduced, and obstacle avoidance strategies are set to prevent solutions from entering infeasible regions; The search strategy for alternative shaft locations is dynamically adjusted in light of changes in the external environment. Calculate the total transport work of the updated shaft candidate locations, and determine the target shaft candidate location with the lowest total transport work based on all calculated total transport work.

[0034] Please see Figure 6 In this embodiment, the shaft location optimization process based on the dung beetle algorithm is as follows: Step 1: Select the search area.

[0035] Specifically, the geographical scope of the study area is determined based on the distribution of mining fields.

[0036] Step 2: Grid division of the search area.

[0037] Specifically, guided by the intelligent zoning results of mineral resources, and based on resource spatial coordinates and ore quantity, all resources within the mineral field are projected onto the surface, and the ore quantities are overlaid to form a resource top-down view. The selected search area is then divided into several grid units (i.e., search elements), such as... Figure 7 As shown, X and Y represent the horizontal and vertical axes of the resource top view, respectively. The selected search area is divided into several grid cells, and each coordinate point contains the coordinates (x, y) and the amount of ore at that location.

[0038] Step 3: Randomly select a grid as a candidate location.

[0039] Specifically, a grid cell is randomly selected from the divided grid as the initial candidate location for the vertical shaft.

[0040] Step 4: Calculate the transportation work of ore from the zone to the candidate location.

[0041] Specifically, for each zone of ore, the transport work to the current candidate location is calculated, including transport distance and transport cost. The goal of shaft location selection is to minimize the total transport distance of all ore to the shaft. Shaft location selection relies on spatial search, treating each feasible location as a feasible solution, calculating the distance from the feasible location to each resource point (representing the ore's horizontal transport distance), and then calculating the product of this distance and the amount of ore at that location (transport work). The calculation results for all resource points are summed to obtain the cumulative value.

[0042] Step 5: Spatial search strategy based on the dung beetle algorithm.

[0043] Specifically, the dung beetle algorithm is applied to simulate the dung beetle's search behavior (including reproduction, foraging, and rolling a ball) to optimize candidate locations. Based on the principle of minimizing transportation work, the candidate locations of the shaft are continuously adjusted through iterative search.

[0044] In this embodiment, the dung beetle algorithm can find a better alternative point, namely the shaft location with the lowest total transport power. It should be noted that the specific search process based on the dung beetle algorithm is combined with... Figure 8 Explanation will be provided. In Figure 8 In mathematical programming, global search refers to a broad search across the entire solution space to find potentially better solutions. Its goal is to explore different regions and avoid getting trapped in local optima. Global search typically covers a larger solution space, attempting to explore all possible solutions to ensure no potential global optima are missed. Local search, on the other hand, involves a detailed search in the vicinity of the current optimal solution to find better solutions. Its goal is to improve the current solution through optimization within a small area. Fast convergence: When close to the optimal solution, local search can quickly find better solutions and is suitable for fine-tuning. To avoid ineffective local searches in the early stages and to speed up computation, a global search is generally used initially to quickly identify the range of potential optimal solutions. After identifying the potential optimal solutions, a local search is then performed within a smaller area to find better results.

[0045] It should be added that the alternative location is to randomly select a point in space and consider that point as a feasible location for a vertical shaft. The distance from that point to each block is calculated by multiplying it by the weight of the ore in the block, and then summing the results. After randomly selecting a large number of alternative locations in space, the location with the smallest sum is selected as the optimal location.

[0046] For example, let's further illustrate the previous example of "based on the clustering results of the first stage, each of the two large blocks is further divided into two sub-partitions, for a total of four sub-partitions." Based on the resource aggregation results data within each deposit in the intelligent partition, the X, Y, and ore quantity attributes are used as input data for shaft site selection. This input data reflects the horizontal distribution characteristics of resources and the ore quantity. This data is used to construct the search range and objective function for shaft site selection. Based on the geological resource model of the four sub-partitions, the resource quantity is projected onto the surface. The dung beetle algorithm is used to search within the sub-partition range, mimicking the behavior of dung beetles rolling balls and foraging. Global and local search strategies are set to promote the global optimum of the optimal solution. See [link to details]. Figure 8 The process includes the following steps: Step S1, set the population size P and the number of iterations M.

[0047] It should be noted that step S1 is to perform initialization, randomly generating a group of dung beetle individuals (feasible solutions), with each dung beetle individual representing a possible shaft location.

[0048] Step S2: Randomly generate the coordinates of the initially selected position within the search space.

[0049] Step S3: Calculate the fitness of each individual, calculate the distance between each location and the resource × the amount of ore, and sum them up.

[0050] In this embodiment, a fitness evaluation mechanism is introduced. Based on the objective function of the site selection problem, the cumulative value of the product of the distance between the resource and the candidate shaft location and the ore quantity is used as the fitness of each individual, and the fitness of each candidate shaft location is used as the evaluation criterion for each candidate shaft location.

[0051] Step S4: Compare individual fitness and select the optimal position.

[0052] Step S5: Terminate?

[0053] In this embodiment, if yes, then step S6 is executed; otherwise, steps S7 and S8 are executed.

[0054] Step S6: Output the optimal shaft location.

[0055] Step S7, global search.

[0056] Step S8, local search.

[0057] It's worth noting that introducing local search can help avoid obstacles. Specifically, a local search mechanism improves the local optimization capability of the solution and sets up obstacle avoidance strategies to prevent the solution from entering infeasible regions. Furthermore, in the early stages of the algorithm, the algorithm typically hasn't found a good solution yet. At this point, a global search should be performed to explore a wider solution space and discover potential high-quality regions. Once the algorithm has found a relatively good solution, but the improvement is small, local search can be used to further optimize the current solution. In this case, local search can find a better solution more quickly.

[0058] Step S9, location update.

[0059] In this embodiment, position updates can be based on direction updates, drawing inspiration from the dung beetle's orientation-maintaining mechanism. The individual's search direction is updated by referencing "navigation signals" (such as the current optimal solution, historical experience, etc.). Specifically, when updating its search position, each individual can determine a new search direction by calculating the difference between its current position and the current optimal solution, which can be a choice between two points. Historical experience can determine a new search direction by calculating the difference between the current position and the optimal solutions of past generations of individuals or the individual's historical best position, which can also be a choice between two points.

[0060] Please see again Figure 6 Step 6: Find the grid with the least transport power.

[0061] Specifically, by combining the spatial search strategy based on the dung beetle algorithm in step five, the grid with the minimum transport work is finally determined.

[0062] Step 7: Alternative locations for the vertical shaft.

[0063] Specifically, after multiple iterations, the grid cell with the minimum transport work is found. This grid cell is the new candidate location for the shaft, and the new candidate location for the shaft is the target candidate location for the shaft.

[0064] Step 8: Final location determined.

[0065] Considering the existing shafts in the mining area and their locations, the advantages and disadvantages of potential new shaft locations compared to existing shafts are evaluated. A comprehensive evaluation of each potential shaft location is conducted, primarily considering the following aspects: First, the distance between each potential location and each ore block is calculated and multiplied by the ore quantity of each block to obtain a cumulative value. This cumulative value is used to compare the advantages and disadvantages of different locations; a smaller value indicates less transportation effort and lower transportation costs, thus selecting the location with the lower transportation effort. Second, the construction difficulty of the potential location, the flatness of the ground, and whether to avoid locations with unfavorable terrain such as mountaintops or villages are also considered. By comprehensively considering these factors, the feasibility of each potential location can be more accurately assessed, thereby selecting the optimal shaft location.

[0066] Specifically, the final location of the shaft is determined by comprehensively considering factors such as transportation efficiency, existing shaft locations, geological conditions, and environmental impact. For example, if a candidate location for the new shaft is on a mountaintop, the increased shaft height will significantly increase construction costs, as the previous analysis primarily considered the surface level and neglected elevation factors. Furthermore, constructing a road from the mountaintop to the shaft will also incur additional costs. Additionally, if candidate locations are situated near waterways, roads, or villages, these locations will be excluded due to feasibility considerations. In such cases, existing shafts may need to be prioritized to ensure project feasibility and economic viability. Specifically, firstly, the distance between each candidate location and each ore block is calculated and multiplied by the ore quantity of each block to obtain a cumulative value. This cumulative value is used to compare the advantages and disadvantages of different locations; a smaller cumulative value indicates lower transportation efficiency and lower transportation costs, thus selecting the location with lower transportation efficiency. Secondly, the construction difficulty of the candidate locations, ground flatness, and whether to avoid locations with unfavorable terrain such as mountaintops or villages must also be considered. By taking into account these factors, the feasibility of each alternative location can be assessed more accurately, thus allowing the selection of the optimal shaft alternative location.

[0067] In this embodiment, during the selection of shaft locations, the distribution of ore quantity within the region is considered, and the transportation work between resources and shaft locations is quantified. The goal is to minimize the transportation work of all resources within the region to the shafts. The main approach is spatial search within the resource distribution area. The terrain is considered to select relatively flat and stable ground to reduce construction difficulty and risk. The optimal construction location coordinates of shafts in each zone are obtained using the dung beetle algorithm optimization search strategy. Based on this, in order to reduce construction costs, the existing shaft projects in each mining area are fully utilized to finally determine the shaft location coordinates of each zone.

[0068] Step S103: The weighted distance algorithm, dung beetle optimization algorithm, and centroid method are used to initially select the locations of the hydrometallurgical plant, resulting in multiple initial selection points. A hydrometallurgical plant location evaluation model is constructed based on the key factors for hydrometallurgical plant site selection. The entropy weight method and the preset evaluation method are used to comprehensively evaluate each of the initial selection points to obtain candidate locations. The transportation routes of the mineral resources from each ore extraction point to each candidate hydrometallurgical plant location are planned to obtain the ore transportation scheme corresponding to each candidate hydrometallurgical plant location.

[0069] In this embodiment, factors such as geological conditions, water supply, transportation convenience, and environmental impact are comprehensively considered, and the location of the hydrometallurgical plant is optimized using a combination of qualitative and quantitative methods. A multi-dimensional site selection analysis model ensures the scientific validity and rationality of the site selection. Based on the site selection principles, qualitative and quantitative analyses are combined, and appropriate site selection methods are adopted for different factors. The advantages of multiple site selection methods are integrated, and the site selection for the hydrometallurgical plant is first initially selected, then screened, and finally the final scheme is determined. The selection scale gradually narrows down, improving site selection efficiency, optimizing decision-making, and ensuring the scientific validity and rationality of the site selection. The main task of the initial selection stage is to define potential areas suitable for the construction of the hydrometallurgical plant from a quantitative analysis perspective, narrowing the scope of subsequent work. The main task of the screening stage is to consider more comprehensive factors, conduct a comprehensive qualitative evaluation of the initially selected locations, and derive alternative schemes. Finally, after more precise road design and cost accounting for each scheme, and in conjunction with underground factors, the final scheme is determined, resulting in a scientifically sound and reasonable site selection plan.

[0070] To further clarify, see [link / reference] Figure 10 The site selection optimization process for hydrometallurgical plants includes the following steps: Step 1: Initial selection of the location for the hydrometallurgical plant.

[0071] Specifically, this includes establishing site selection principles, analyzing factors influencing site selection, site optimization based on the dung beetle algorithm, site optimization based on the weighted distance algorithm, site optimization based on the centroid method, and the initial location selection results for the hydrometallurgical plant.

[0072] This study comprehensively analyzes the key factors influencing the site selection of hydrometallurgical plants, including ore sources, transportation costs, water access, and environmental protection. Multiple methods, including a weighted distance algorithm, a dung beetle optimization algorithm, and a centroid method, are employed for initial site selection. The weighted distance algorithm determines suitable plant locations by weighting the distances from water sources, roads, villages, and mining areas to potential sites. The dung beetle algorithm uses the distribution of mineral resources in each area as basic data and minimizes the total ore transportation effort as the optimization objective, simulating dung beetle foraging behavior to find the optimal solution and obtain the plant location. The centroid method uses the centroid coordinates of each ore body, ore quantity, and unit transportation cost as inputs to initially select the hydrometallurgical plant location from the perspective of minimizing transportation costs. The initial locations obtained from these methods are integrated, and combined with past engineering experience, to optimize and derive multiple initial hydrometallurgical plant locations. These initial locations will serve as the final preliminary selection results for multiple hydrometallurgical plant sites. The principle of the dung beetle algorithm is similar to that of shaft site selection, but the search objects differ. The goal of shaft site selection is to find an optimal location within a mineral deposit, while the search for the location of a hydrometallurgical plant aims to determine an optimal location among multiple mineral deposits.

[0073] In one embodiment, a weighted distance algorithm is used to initially select the location of the hydrometallurgical plant, resulting in at least one initially selected location point, including: Obtain a dataset on the impact of hydrometallurgical plant site selection, which includes ore source data, transportation cost data, traffic condition data, environmental protection data, and water source data. Collect local planar maps, which include major road data, water source data, village data, and geographic information of mining areas. Clean and preprocess the local planar maps to obtain processed planar maps. Each type of data affecting the site selection of a hydrometallurgical plant is assigned an influence weight. The processed planar map is divided into grids according to a preset interval density to obtain multiple map grids. Each map grid is considered as a candidate location for a hydrometallurgical plant. The weighted distance of each candidate location is calculated using the following formula: ; In the formula, For the first Weighted distance of candidate locations for hydrometallurgical plants; For the first The weights of factors influencing the site selection of a hydrometallurgical plant; For the first The candidate positions for the hydrometallurgical plant were up to the first... Distance of each hydrometallurgical plant site selection factor; The objective function is used to calculate the distance score from each candidate location of a hydrometallurgical plant to a preset important point based on the weighted distance of the candidate locations. Based on the distance scores, at least one initial selection point for the location of the hydrometallurgical plant is obtained from multiple candidate locations.

[0074] In this embodiment, see Figure 9 , Figure 9 The image shows a schematic diagram of candidate locations for a hydrometallurgical plant based on a weighted distance algorithm. Figure 9 In the diagram, the areas are distinguished by color, with higher scores leaning towards darker blue. Areas with high scores are selected as candidate locations for the hydrometallurgical plant.

[0075] In one embodiment, the preset key points include villages, roads, water sources, and mining areas. The objective function includes the following formula: ; In the formula: Score is the distance score from each candidate location of the hydrometallurgical plant to the preset important point. The distance from each candidate hydrometallurgical plant location to the village; The distance from each candidate location of the hydrometallurgical plant to the road; The distance from each candidate hydrometallurgical plant location to the water source; The distance from each candidate hydrometallurgical plant location to the mining area; The weights corresponding to the distances from each candidate hydrometallurgical plant location to the village; The weights corresponding to the distances from each candidate location of the hydrometallurgical plant to the road; The weights corresponding to the distances from each candidate hydrometallurgical plant location to the water source; The weights are the distances from each candidate hydrometallurgical plant location to the mining area.

[0076] In one embodiment, the initial selection of the site location for the hydrometallurgical plant is performed using the center of gravity method to obtain at least one initial selection point for the hydrometallurgical plant location, including: Assume the coordinates of each resource point are (x k y k ), k=1,2,3,...,n, the coordinates of the center location are Construct the following formula: ; In the formula, Total transportation cost, in yuan; k The unit transportation cost from the mineral resource point to the alternative location of the hydrometallurgical plant is expressed in yuan / (ton·km); k The quantity of ore at a mineral resource point, in tons; d k The straight-line distance from the mineral resource point to the candidate location of the hydrometallurgical plant is expressed in kilometers. According to the above formula , Find the partial derivative, according to and The principle that the first partial derivative is zero can be used to find the target coordinates that minimize the total transportation cost, and at least one initial selection point for the location of the hydrometallurgical plant can be determined based on the target coordinates.

[0077] According to the above formula , Find the partial derivative, according to and The principle that the first-order partial derivatives are zero can be used to find that... The smallest ,Right now: ; ; Calculate the centroid of each ore body, substitute the centroid coordinates, ore quantity, and unit transportation cost into the above formula, and perform repeated decreasing calculations until the transportation cost is minimized. The coordinate values ​​obtained at this point are the ideal coordinates of the desired site selection center. See Table 1 for a schematic diagram of the preliminary site selection results.

[0078] Table 1. Schematic diagram of preliminary site selection results.

[0079]

[0080] It should be noted that this embodiment provides a location screening based on a comprehensive evaluation method. Specifically, through a comprehensive analysis of the key factors affecting the site selection of a hydrometallurgical plant, a comprehensive and complete evaluation index system is constructed. The entropy weight method is combined with the preset evaluation method to comprehensively evaluate the initial site selection and obtain the candidate site locations suitable for the construction of a hydrometallurgical plant under complex factor conditions.

[0081] Please see again Figure 10 The site selection optimization process for hydrometallurgical plants also includes: The second step is location selection based on a comprehensive evaluation method.

[0082] Specifically, this includes a comprehensive consideration of factors influencing site selection, the construction of an evaluation index system, the evaluation of site selection schemes based on the entropy weight method and the pre-set evaluation method, and the optimization results of alternative hydrometallurgical plant schemes. The evaluation index system is constructed based on data related to topography, resource conditions, transportation, environmental protection, and other factors. Through a comprehensive analysis of key factors influencing the site selection of the hydrometallurgical plant, a comprehensive and complete evaluation index system is constructed. The entropy weight method and the pre-set evaluation method are combined to comprehensively evaluate the initially selected locations, resulting in suitable alternative sites for the construction of the hydrometallurgical plant under complex conditions.

[0083] In one embodiment, the entropy weight method and the preset evaluation method are used to comprehensively evaluate the preliminary locations of each of the hydrometallurgical plant sites to obtain candidate locations for the hydrometallurgical plant sites, including: The evaluation criteria are determined, and an indicator evaluation matrix is ​​constructed based on the evaluation criteria. The indicator evaluation matrix contains positive indicator data and negative indicator data. The positive indicator data includes data such as slope, geological hazards, construction area, impact of exhaust gas emissions, land use type, water source, power source, manpower, transportation conditions, raw material supply and tailings transportation, etc. The negative indicator data includes data such as radiation impact and construction cost.

[0084] The index evaluation matrix is ​​forward-oriented and normalized to obtain the processed index evaluation matrix; The evaluation index weights are obtained using the entropy weight method. The optimal and worst solutions are determined based on the evaluation index weights. The ideal solution distance and comprehensive score are calculated based on the evaluation index weights and the processed index evaluation matrix. The initial selection points for each hydrometallurgical plant location are ranked based on the ideal solution distance and comprehensive score. The candidate locations for the hydrometallurgical plant are determined based on the ranking results.

[0085] Please see again Figure 10 The site selection optimization process for hydrometallurgical plants also includes: The third step is to plan the surface transportation route for ore.

[0086] Specifically, this includes surface road design, engineering quantity calculation, and cost accounting, combined with underground analysis to determine the final optimized site selection. The transportation route for ore from the ore extraction point to the hydrometallurgical plant is planned, resulting in ore transportation schemes corresponding to each alternative hydrometallurgical plant. Ore transportation costs, road construction costs, and hydrometallurgical plant construction costs are calculated, providing fundamental data for the subsequent final site selection based on the underground development system.

[0087] It should be noted that the calculation method for ore transportation costs is as follows: multiply the length of the transportation route by the corresponding transportation cost coefficient, which is the value coefficient of that transportation route. For ore transportation costs in operating costs, the transportation costs from each ore extraction point to the hydrometallurgical plant in the area are mainly considered. For the sake of convenience in the study, surface transportation methods need to be converted into horizontal tunnel types. Relevant standards should be consulted to obtain the corresponding transportation cost coefficients. The formula for calculating ore transportation costs is as follows: Unit transportation cost = Transportation route length × Transportation cost coefficient.

[0088] The formula for calculating road construction costs is as follows: Road construction cost = Length of newly constructed road × Unit road construction cost. The unit road construction cost can be determined by referring to the standards for land acquisition costs and road construction costs for the project, as well as the document No. 307 of 2006 issued by the Ministry of Land and Resources, "Notice on Issuing and Implementing the <National Minimum Price Standard for Industrial Land Transfer>". Other standard documents can also be used to determine the unit road construction cost; no specific restrictions are imposed here.

[0089] The construction cost of the hydrometallurgical plant includes earthwork and investment costs. It is provided by the general layout plan based on the engineering volume and the standards for the construction of the hydrometallurgical plant, and is calculated according to the actual situation.

[0090] In this embodiment, based on the calculation results of the preset evaluation method, the higher the comprehensive score of the initial selection point, the more suitable the area where the point is located is for the construction of a hydrometallurgical plant under the consideration of comprehensive factors. The top-ranked initial selection points are selected as candidate points for the hydrometallurgical plant. For example, a total of 10 candidate points are determined in the western area and a total of 9 candidate points are determined in the northern area, providing optional destinations for ore transportation for subsequent optimization of the overall development system.

[0091] Step S104: Before mining, the intermediate roadways are divided according to the distribution of mineral resources and mining technology. The whale optimization algorithm is used to find the location with the minimum transportation work from the mineral resource point to the intermediate roadway, which is taken as the optimal ore extraction point in the intermediate section. The gray wolf algorithm is used to optimize the ore transportation path of all mineral deposits in each mining area to obtain the optimization results. The optimization results include the construction location of the regional transportation platform and the ore transportation path that minimizes the total transportation cost of ore in the region.

[0092] In this embodiment, intelligent optimization algorithms such as the whale optimization algorithm and the gray wolf algorithm are used to perform mathematical modeling and integrated optimization of the underground development and transportation network, forming a transportation system that meets the geological conditions and mining needs of the mine, thereby reducing costs.

[0093] It should be further explained that the intensive optimization of the transportation system specifically includes the following steps: Step 1: Before mining, the deposit needs to be divided into intermediate sections based on resource distribution and mining technology. Independent development projects and transportation systems need to be constructed. After the development roadways are excavated, the basic ore transportation routes for each intermediate section are established, but the ore flow direction is still unclear. In this situation, by optimizing the optimal convergence and extraction location of resources in the intermediate section, the total transportation work for ore convergence within the intermediate section is minimized. This can indicate the ore flow direction and provide a reference for selecting transportation routes in subsequent multi-deposit joint production. By extracting ore block data, the centroid coordinates of ore body resource points are obtained. Using roadway engineering, the core transportation roadways for centralized transportation of ore resources are identified. The location where resource points converge to the core transportation roadway with the minimum transportation work for ore extraction is found, achieving optimal ore extraction in the intermediate section of the deposit. The mining sections are divided based on the selected intermediate elevation. The intermediate roadways established on this basis are responsible for transporting the ore mined in each section. The ore is transported through these intermediate roadways to the ore convergence point, where it is then transferred. The location of the ore convergence point affects the ore transport efficiency, which in turn affects the transport cost. Therefore, this study establishes an optimization model and uses the whale optimization algorithm to find the location with the minimum transport efficiency from the ore resource point to the aforementioned intermediate roadways. This location is the optimal ore extraction point in the intermediate section, reducing the ore transport cost in the intermediate roadways.

[0094] See Figure 11 As shown, firstly, ore block data, including the center coordinates of the blocks, is extracted from the ore resource model. This data is used as the centroid coordinates of the ore resource points to accurately determine their spatial locations. Subsequently, the intermediate section engineering is constructed based on the actual distribution characteristics of the ore resources, and the layout scheme of the core transport roadway is defined. Finally, based on the determined ore resource points and core transport roadways, the whale optimization algorithm is used to calculate and determine the optimal ore extraction location in the intermediate section.

[0095] Step 2: Based on the existing development project design of the uranium mine base, the Grey Wolf algorithm is used to optimize and select the location of the transportation platform, thereby obtaining the best route for transporting all ore deposits to the hydrometallurgical plant.

[0096] By applying the gray wolf algorithm and simulating gray wolf population mechanisms and predation behavior, the optimal paths from each transportation section to the hydrometallurgical plant can be effectively found. Each individual gray wolf corresponds to one possible path from the underground middle section to the ore extraction point for each deposit. These paths consist of a series of nodes, which can be specific locations underground, such as middle roadways or shafts.

[0097] In this embodiment, the Grey Wolf algorithm is used to optimize all possible transportation routes for each ore deposit. Simultaneously, to further determine the optimal layout of the hydrometallurgical plant, it is necessary to calculate and estimate the engineering quantities and costs of road and hydrometallurgical plant construction, clarifying the costs of road and hydrometallurgical plant construction and transportation costs in different schemes, so as to compare different schemes from an economic perspective, considering underground transportation. During this process, the transportation costs of ore to different alternative hydrometallurgical plants and the construction costs of the alternative hydrometallurgical plants are comprehensively considered. Based on the optimization results of the Grey Wolf algorithm, the optimal hydrometallurgical plant location is selected, thereby deriving the optimal connection location of the transportation platform for each zone and the optimal transportation route for transporting ore from each ore deposit to the hydrometallurgical plant.

[0098] In this embodiment, the process of optimizing ore extraction in the middle section is exemplified as follows: The following assumptions are made for the ore extraction optimization model in the middle section of the deposit: (1) The unit of mineral resource is a single block in the mineral deposit block model; (2) Use the centroid coordinates of the block model as the coordinate position of the smallest unit of resources; (3) The middle section tunnel project is planned according to the established middle section elevation, and the shape of each middle section tunnel is basically the same; (4) Treat the core transport roadway through which the ore is centrally transferred as a straight roadway and ignore any possible bends; Since the work done during ore transportation is not affected by grade, the grade value is not considered. Only the planar coordinates (x, y), size (size_x, size_y, size_z), and extension direction (start_x, start_y, end_x, end_y) of the ore block model and the middle section roadway extension direction (start_x, start_y, end_x, end_y) of the ore deposit block model are used as model input indicators. The model calculates the middle section convergence position with the minimum transportation work, and obtains the optimal ore extraction point coordinates (x, y) of the middle section roadway of the ore deposit.

[0099] The process of selecting ore accumulation points, such as Figure 12 As shown, using the ore deposit block model as the data source and the elevation of the intermediate roadway as the constraint, the ore quantity within each intermediate section is calculated. A random ore convergence point location is generated, such as candidate location 1. The distance between the ore and the convergence point in each intermediate section is calculated, and then the transportation work is calculated (the product of the distance between the ore and the convergence point in each intermediate section and the ore quantity is used as the transportation work). The cumulative value calculated using this method for the ore in each intermediate section is used as the objective function. When the convergence point location moves from location 1 to location 2, the objective function is recalculated, and the transportation work at locations 1 and 2 is compared. This process is repeated for a large number of locations. By comparing a large number of locations, the ore convergence point location that minimizes the total transportation work is selected. The ore convergence point location is the optimal ore extraction point.

[0100] The optimization objective is to minimize the total ore transportation work, specifically the sum of the horizontal transportation work required to transport all ore to the ore convergence point. For example, using the whale optimization algorithm, the optimal mid-section extraction point is optimized for 18 ore deposits across four zones. After solving the model, the optimal mid-section extraction point for each ore deposit can be obtained. This point's coordinates can be referenced when determining the locations for ore convergence and extraction in shafts, ore passes, and adits for engineering layout. See [link to relevant documentation]. Figure 13 This is a schematic diagram showing the selection of ore accumulation points.

[0101] In this embodiment, the process of optimizing the location of the transportation platform is illustrated as follows: To facilitate the use of the Grey Wolf algorithm to optimize the optimal location of the transportation platform, the following model assumptions are made: (1) Only one transportation platform is needed between the two deposits; (2) The location of the centralized transportation platform is selected within a single area at the same elevation between two mineral deposits; (3) The transportation platform is a straight line, that is, the influence of terrain on the shape of the transportation platform is not considered; (4) Centralized transportation platforms formed by connecting different sections of the same deposit have the same weight value, that is, the difference in transportation distance of the centralized transportation platform due to different elevations is not considered; (5) The unit transportation cost of ore is fixed and is only proportional to the transportation distance, that is, the fluctuation of the unit transportation price caused by the scale effect due to the change of transportation volume is not considered. (6) It is assumed that the positions of nodes such as the middle section of the ore deposit, transportation platform, shaft and adit entrance in the transportation network are fixed and unchanging, and the changes in the position or connection relationship of nodes due to mining progress or other factors are not considered. (7) The cost of transporting the ore directly to the main hoisting shaft or adit after the intermediate mining is not considered.

[0102] Within each zone, the hoisting and transportation methods for all ore deposits were analyzed. All ore deposit development methods fall into the following three categories: (1) Deposits developed by regional shafts. In such deposits, the ore in the middle section can be transported directly to the shaft by rail or by establishing a ramp-passway for trackless transport to the shaft.

[0103] (2) Developed by independent shafts, but no ore is produced on the surface of the shafts. Such deposits need to be connected to the middle section of other ore bodies by a transport platform, and transported to the shaft or adit entrance through the middle section roadway. The transport platform can also be connected to the shaft or adit entrance.

[0104] (3) Development via adits or inclined shafts. For this type of deposit, it is also necessary to consider building a transportation platform to transport the minerals to the entrance of the vertical shaft or adit for hoisting.

[0105] Because the transport distance of the ore body connected to the shaft is not fixed, it is impossible to quantify the transportation cost of transporting the ore from the middle section to the shaft or hoisting facility. Therefore, the transportation method for this type of ore is not the focus of this optimization, and its transportation cost is not considered for the time being. Therefore, all possible transportation paths from the middle section ore to the shaft or adit entrance of the uranium mine base are as follows: Figure 14 As shown.

[0106] Each zone contains multiple mineral deposits, requiring the use of the Grey Wolf algorithm to optimize the ore transportation routes for all deposits within each zone. The optimization results reveal the optimal locations for the zone's transportation platforms and the mineral transport routes that minimize the total ore transportation cost for each zone. A schematic diagram of the optimization results for a specific zone is shown below. Figure 15 As shown.

[0107] In one embodiment, the method further includes: calculating transportation cost data corresponding to each of the ore transportation schemes; and determining the final location of the hydrometallurgical plant based on the transportation cost data of each candidate hydrometallurgical plant location and the construction cost data of the hydrometallurgical plant.

[0108] Based on the different candidate hydrometallurgical plant sites obtained from the above-mentioned site selection optimization section, a comparative selection of hydrometallurgical plants was conducted. The Grey Wolf algorithm was used to optimize the transportation routes of all ore deposits within the large block, revealing that regardless of changes in the hydrometallurgical plant site selection, the underground ore transportation route and extraction point remain constant. By comparing the transportation costs of transporting ore to multiple candidate hydrometallurgical plants in different zones with the construction costs of the hydrometallurgical plants, the hydrometallurgical plant with the lowest total cost was selected as the final location.

[0109] Example 2 Furthermore, embodiments of this application provide an intensive development optimization system for uranium ore fields.

[0110] This intensive development optimization system for uranium ore fields includes: The partitioning module is used to perform preliminary clustering of ore bodies in each ore deposit using the DBSCAN algorithm to obtain multiple ore resource-intensive areas. The HAC algorithm is then used to perform partitioning and aggregation of the multiple ore resource-intensive areas within the overall mining field to obtain multiple mining field partitioning areas. The shaft location processing module is used to determine the search area according to the various mining area zones, divide the search area into grids to obtain multiple search units, randomly select one unit from the multiple search units as the initial shaft candidate location, calculate the total transport work from the ore in each ore block to the initial shaft candidate location, optimize the shaft candidate location based on the dung beetle algorithm, and continuously adjust the shaft candidate location through spatial iterative search to obtain the target shaft candidate location with the lowest total transport work, based on the principle of minimizing transport work. The final shaft location is determined based on the target shaft candidate location and the existing shaft location. The hydrometallurgical plant location processing module is used to perform initial site selection for hydrometallurgical plants using weighted distance algorithm, dung beetle optimization algorithm, and centroid method, respectively, to obtain multiple initial site selection points. Based on key factors for hydrometallurgical plant site selection, a hydrometallurgical plant location evaluation model is constructed. Using entropy weight method and preset evaluation method, the initial site selection points are comprehensively evaluated to obtain candidate hydrometallurgical plant locations. Finally, the module plans the transportation routes of mineral resources from each ore extraction point to each candidate hydrometallurgical plant location, obtaining ore transportation schemes corresponding to each candidate hydrometallurgical plant location. The transportation route optimization module is used to divide the intermediate roadways according to the distribution of mineral resources and mining technology before mining begins. The whale optimization algorithm is used to find the location with the minimum transportation effort from the mineral resource point to the intermediate roadway, which is then taken as the optimal ore extraction point. The grey wolf algorithm is then used to optimize the ore transportation routes of all mineral deposits within each mining area, yielding optimization results. These results include the construction location of the regional transportation platform and the ore transportation route that minimizes the total ore transportation cost for each region.

[0111] The intensive development optimization system for uranium ore fields provided in this embodiment can implement the intensive development optimization method for uranium ore fields provided in Embodiment 1. To avoid repetition, it will not be described again here.

[0112] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0113] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0114] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other modifications under the guidance of this application without departing from its spirit, and all of these modifications are within the scope of protection of this application.

Claims

1. A method for intensive and optimized development of uranium ore fields, characterized in that, include: The DBSCAN algorithm is used to perform preliminary clustering of the ore bodies in each ore deposit to obtain multiple ore resource-intensive areas. The HAC algorithm is then used to perform partitioning and aggregation of the multiple ore resource-intensive areas within the overall mining field to obtain multiple mining field partition areas. The search area is determined according to the respective mining area zones. The search area is then divided into grids to obtain multiple search units. One of the multiple search units is randomly selected as the initial shaft candidate location. The total transport work from each ore block to the initial shaft candidate location is calculated. The shaft candidate location is optimized based on the dung beetle algorithm. With the principle of minimizing transport work, the shaft candidate location is continuously adjusted through spatial iterative search to obtain the target shaft candidate location with the lowest total transport work. The final shaft location is determined based on the target shaft candidate location and the existing shaft location. The weighted distance algorithm, dung beetle optimization algorithm, and centroid method were used to initially select the locations of the hydrometallurgical plant, resulting in multiple preliminary selection points. Based on key factors for hydrometallurgical plant site selection, a hydrometallurgical plant location evaluation model was constructed. The entropy weight method and a pre-defined evaluation method were used to comprehensively evaluate each of the preliminary selection points, resulting in candidate locations for the hydrometallurgical plant. Finally, transportation routes for mineral resources from each ore extraction point to each candidate hydrometallurgical plant location were planned, yielding ore transportation schemes corresponding to each candidate hydrometallurgical plant location. Before mining begins, intermediate roadways are divided according to the distribution of mineral resources and mining technology. The whale optimization algorithm is used to find the location with the minimum transportation work from the mineral resource point to the intermediate roadway, which is taken as the optimal ore extraction point in the intermediate section. The gray wolf algorithm is used to optimize the ore transportation path of all mineral deposits in each mining area, and the optimization results are obtained. The optimization results include the construction location of the regional transportation platform and the ore transportation path that minimizes the total ore transportation cost of the region.

2. The method according to claim 1, characterized in that, The search area is divided into grids to obtain multiple search units, including: In the search area, based on the spatial coordinates of mineral resources and the amount of ore, the spatial coordinates of all mineral resources in the mining field are projected onto the ground surface, and the ore amounts are superimposed to form a resource top view.

3. The method according to claim 2, characterized in that, The minimum total transport work for each candidate shaft location is determined using the following formula: ； in, The total transport work for each of the candidate shaft locations is expressed in tons-meters; minZ represents the minimum total transport work. Numbering each ore block; The total number of ore blocks; Number each candidate location for a vertical shaft; For each ore block The amount of ore, in tons; For each ore block To the alternative location of the shaft The straight-line distance between them, in meters.

4. The method according to claim 3, characterized in that, A weighted distance algorithm is used to initially select the location of the hydrometallurgical plant, resulting in at least one initially selected location point, including: Obtain a dataset on the impact of hydrometallurgical plant site selection, which includes ore source data, transportation cost data, traffic condition data, environmental protection data, and water source data. Collect local planar maps, which include major road data, water source data, village data, and geographic information of mining areas. Clean and preprocess the local planar maps to obtain processed planar maps. Each type of data affecting the site selection of a hydrometallurgical plant is assigned an influence weight. The processed planar map is divided into grids according to a preset interval density to obtain multiple map grids. Each map grid is considered as a candidate location for a hydrometallurgical plant. The weighted distance of each candidate location is calculated using the following formula: In the formula, For the first Weighted distance of candidate locations for hydrometallurgical plants; For the first The weights of factors influencing the site selection of a hydrometallurgical plant; For the first The candidate positions for the hydrometallurgical plant were up to the first... Distance of each hydrometallurgical plant site selection factor; The objective function is used to calculate the distance score from each candidate location of a hydrometallurgical plant to a preset important point based on the weighted distance of the candidate locations. Based on the distance scores, at least one initial selection point for the location of the hydrometallurgical plant is obtained from multiple candidate locations.

5. The method according to claim 4, characterized in that, The preset key points include villages, roads, water sources, and mining areas. The objective function includes the following formula: In the formula: Score is the distance score from each candidate location of the hydrometallurgical plant to the preset important point. The distance from each candidate hydrometallurgical plant location to the village; The distance from each candidate location of the hydrometallurgical plant to the road; The distance from each candidate hydrometallurgical plant location to the water source; The distance from each candidate hydrometallurgical plant location to the mining area; The weights corresponding to the distances from each candidate hydrometallurgical plant location to the village; The weights corresponding to the distances from each candidate location of the hydrometallurgical plant to the road; The weights corresponding to the distances from each candidate hydrometallurgical plant location to the water source; The weights are the distances from each candidate hydrometallurgical plant location to the mining area.

6. The method according to claim 3, characterized in that, The initial site selection for the hydrometallurgical plant is performed using the center-of-gravity method, resulting in at least one initial site selection point, including: Assume the coordinates of each resource point are (x k y k ), k=1,2,3,...,n, the coordinates of the center location are Construct the following formula: In the formula, Total transportation cost, in yuan; k The unit transportation cost from the mineral resource point to the alternative location of the hydrometallurgical plant is expressed in yuan / (ton·km); k The quantity of ore at a mineral resource point, in tons; d k The straight-line distance from the mineral resource point to the candidate location of the hydrometallurgical plant is expressed in kilometers. According to the above formula , Find the partial derivative, according to and The principle that the first partial derivative is zero can be used to find the target coordinates that minimize the total transportation cost, and at least one initial selection point for the location of the hydrometallurgical plant can be determined based on the target coordinates.

7. The method according to claim 4, characterized in that, The entropy weight method and the preset evaluation method are used to comprehensively evaluate the initial locations of the hydrometallurgical plants, resulting in candidate locations for the hydrometallurgical plants, including: Determine the evaluation criteria, and construct an indicator evaluation matrix based on the evaluation criteria. The indicator evaluation matrix contains positive indicator data and negative indicator data. The index evaluation matrix is ​​forward-oriented and normalized to obtain the processed index evaluation matrix; The evaluation index weights are obtained using the entropy weight method. The optimal and worst solutions are determined based on the evaluation index weights. The ideal solution distance and comprehensive score are calculated based on the evaluation index weights and the processed index evaluation matrix. The initial selection points for each hydrometallurgical plant location are ranked based on the ideal solution distance and comprehensive score. The candidate locations for the hydrometallurgical plant are determined based on the ranking results.

8. The method according to claim 4, characterized in that, The optimization of candidate shaft locations based on the dung beetle algorithm, using the principle of minimizing transportation work, involves iterative spatial search to continuously adjust candidate shaft locations and obtain the target candidate shaft location with the lowest total transportation work. This includes: A group of dung beetles is randomly generated, and each dung beetle represents a candidate location for a vertical shaft; Determine the first distance between the mineral resource point and each shaft candidate location, and use the cumulative value of the product of the first distance and the ore quantity as the total transportation work for evaluating each shaft candidate location; The search direction for alternative shaft locations is updated by referencing navigation signals; Adjust the position information of each shaft candidate position according to the updated direction and step size to achieve the search of the solution space; A local search mechanism is introduced, and obstacle avoidance strategies are set to prevent solutions from entering infeasible regions; The search strategy for alternative shaft locations is dynamically adjusted in light of changes in the external environment. Calculate the total transport work of the updated shaft candidate locations, and determine the target shaft candidate location with the lowest total transport work based on all calculated total transport work.

9. The method according to claim 1, characterized in that, The method further includes: Calculate the transportation cost data corresponding to each of the ore transportation schemes; determine the final location of the hydrometallurgical plant based on the transportation cost data of each candidate location and the construction cost data of the hydrometallurgical plant.

10. An intensive development optimization system for uranium ore fields, characterized in that, The system includes: The partitioning module is used to perform preliminary clustering of ore bodies in each ore deposit using the DBSCAN algorithm to obtain multiple ore resource-intensive areas. The HAC algorithm is then used to perform partitioning and aggregation of the multiple ore resource-intensive areas within the overall mining field to obtain multiple mining field partitioning areas. The shaft location processing module is used to determine the search area according to the respective mining area zones, divide the search area into grids to obtain multiple search units, randomly select one unit from the multiple search units as the initial shaft candidate location, calculate the total transportation work from each ore block to the initial shaft candidate location, optimize the shaft candidate location based on the dung beetle algorithm, and continuously adjust the shaft candidate location through spatial iterative search to obtain the target shaft candidate location with the lowest total transportation work, based on the principle of minimizing transportation work. The final shaft location is determined based on the target shaft candidate location and the existing shaft location. The hydrometallurgical plant location processing module is used to perform initial site selection for hydrometallurgical plants using weighted distance algorithm, dung beetle optimization algorithm, and centroid method, respectively, to obtain multiple initial site selection points. Based on key factors for hydrometallurgical plant site selection, a hydrometallurgical plant location evaluation model is constructed. Using entropy weight method and preset evaluation method, the initial site selection points are comprehensively evaluated to obtain candidate hydrometallurgical plant locations. Finally, the module plans the transportation routes of mineral resources from each ore extraction point to each candidate hydrometallurgical plant location, obtaining ore transportation schemes corresponding to each candidate hydrometallurgical plant location. The transportation route optimization module is used to divide the intermediate roadways according to the distribution of mineral resources and mining technology before mining begins. The whale optimization algorithm is used to find the location with the minimum transportation effort from the mineral resource point to the intermediate roadway, which is then taken as the optimal ore extraction point. The grey wolf algorithm is then used to optimize the ore transportation routes of all mineral deposits within each mining area, yielding optimization results. These results include the construction location of the regional transportation platform and the ore transportation route that minimizes the total ore transportation cost for each region.

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