Mineral resource investment risk evaluation method and system

Abstract

The invention provides a mineral resource investment risk evaluation method and system, and the method comprises the steps: obtaining the original data of a target region, the original data comprises five types of first-class data: infrastructure data, mineral data, investment enterprise data, region stability data and geographic data, and each type of data comprises at least one type of second-class data, all types of second-class data are divided into three types of element data, namely point element data, line element data and surface element data; performing data gridding division on the target region based on the surface elements by utilizing an ArcGIS technology, and screening out target grids which can be invested; calculating an investment score of each target grid of the target region based on the point elements, the line elements and the corresponding weights; and calculating a density evaluation index of each sub-region of the target region based on the investment score of each target grid, wherein the sub-region with the maximum density evaluation index is the region with the minimum required mineral resource investment risk. By using the method of the invention, the accuracy of risk evaluation can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mineral resource investment risk assessment, and in particular to a mineral resource investment risk assessment method and system. Background Art

[0002] The prosperity of the global economy has promoted the process of economic globalization, making it the main trend of the current world economy. This period happens to be a critical stage of my country's rapid economic growth and accelerated consumption of mineral resources. However, in the process of developing mineral resources overseas, my country's mining companies face many uncertainties and potential risks, which has become an important factor restricting enterprises from "going out". Therefore, it is of great significance to effectively evaluate the risks of overseas mineral resource development, investment and operation, and formulate scientific development strategies and risk prevention and control measures accordingly. Overseas mineral resource investment has now become a core component of the national strategy of various economies. It can not only meet domestic production needs, strengthen energy security, promote industrial structure upgrading, but also bring considerable economic benefits to investors. However, the complexity of the international environment and the volatility of the market have made investment decisions more and more complicated. There is an urgent need for a scientific and accurate evaluation index and evaluation model to quantify and comprehensively judge and manage these risks. Its importance is reflected in: first, integrating information and quickly identifying investment opportunities; second, improving the scientificity and accuracy of decision-making; third, adjusting strategies in real time and reducing risks through detailed analysis and expert guidance. However, there are also challenges in the implementation process, such as the limitations of data acquisition, the rapid changes in market dynamics, and the complexity of international cooperation, all of which need to be fully considered in the system design. At the same time, how to ensure the accuracy and practicality of the model, as well as how to find a balance between complexity and user-friendliness, is a major challenge at the technical level. Domestic and foreign scholars are paying more and more attention to this field, but how to transform theory into a practical investment support system is still a difficult problem to be solved.

[0003] In general, the actual application of overseas mineral resource investment decision support systems and the effectiveness of risk assessment models still need to be further studied and explored. Effective risk assessment can help decision makers identify problems, optimize resource allocation, reduce investment risks, and ensure the dual realization of project economic benefits and social responsibility.

[0004] Existing risk assessment solutions mainly use computer software to collect and integrate multi-source data, and use traditional rule-based methods to standardize, analyze and classify data into risk levels. However, existing methods rely on manual input and fixed rules, are inefficient in processing large-scale and high-dimensional spatial data, and are difficult to cope with changing geological conditions; these limitations make existing risk warning and management systems in the geological industry inaccurate and inefficient. Summary of the invention

[0005] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0006] To this end, the first purpose of the present invention is to propose a mineral resources investment risk assessment method to improve the accuracy of risk assessment.

[0007] The second object of the present invention is to provide a mineral resources investment risk assessment system.

[0008] A third objective of the present invention is to provide an electronic device.

[0009] To achieve the above-mentioned purpose, the first aspect of the present invention provides a method for evaluating the risk of mineral resource investment, comprising:

[0010] Obtaining the original data of the target area, the original data includes five first-class data, namely, infrastructure data, mineral mining data, investment enterprise data, regional stability data, and geographic data. Each first-class data includes at least one second-class data, and all second-class data are divided into three types of element data, namely, point element, line element, and surface element;

[0011] Using ArcGIS technology to divide the target area into data grids based on the surface elements and screen out target grids that can be invested;

[0012] Calculate the investment score of each target grid in the target area based on the point elements, the line elements and the corresponding weights;

[0013] The density evaluation index of each sub-region of the target area is calculated based on the investment score of each target grid, wherein the sub-region with the largest density evaluation index is the region with the minimum investment risk of the required mineral resources.

[0014] In the method of the first aspect of the present invention, the mineral mining data includes mining rights surface elements, the geographic data includes protection area surface elements and national boundary and provincial interface elements, and the use of ArcGIS technology to grid the target area based on the surface elements and screen out target grids that can be invested in includes: using ArcGIS technology to determine the target area based on the national boundary and provincial interface elements, and dividing the target area into multiple grids; determining grids that cannot be invested in based on the mining rights surface elements and the protection area surface elements; eliminating grids that cannot be invested in from all grids in the target area, and the remaining grids are target grids that can be invested in.

[0015] In the method of the first aspect of the present invention, the investment score of each target grid in the target area is calculated based on the point elements, the line elements and the corresponding weights, including: for any target grid, determining all point elements and line elements in the target grid, and the corresponding type of data; determining the scores of all point elements and the scores of all line elements in the target grid; calculating the scores of each type of data in the target grid based on the scores of all point elements, the scores of all line elements and the corresponding weights; calculating the investment score of the target grid based on the scores of each type of data and the corresponding weights, and then obtaining the investment scores of all target grids.

[0016] In the method of the first aspect of the present invention, the infrastructure data includes power plant point elements, port point elements, airport point elements, highway line elements, railway line elements and water system line elements; the mineral mining data includes mine point elements; the investment enterprise data includes Chinese and foreign-funded enterprise point elements; the regional stability data includes terrorist activity information point elements; among them, power plant point elements, port point elements, airport point elements, Chinese and foreign-funded enterprise point elements are all first point elements, mine point elements are second point elements, and terrorist activity information point elements are third point elements.

[0017] In the method of the first aspect of the present invention, the determining of the scores of all point elements and the scores of all line elements in the target grid includes: if the target grid contains the first point element, the score of each first point element is determined based on the distance from each first point element to the center point of the target grid; if the target grid contains the second point element, the score of each second point element is determined based on the distance from each second point element to the center point of the target grid and the number of mineral types; if the target grid contains the third point element, the score of the third point element is a preset score.

[0018] In the method of the first aspect of the present invention, the score of each second point element is determined based on the distance from each second point element to the center point of the target grid and the number of mineral types, including: for any second point element, obtaining the initial score of the second point element based on the distance from the second point element to the center point of the target grid and a given distance range; obtaining the number of mineral types and the type of mineral types of the second point element, and optimizing the initial score based on the number of mineral types and the type of mineral types to obtain the final score of the second point element.

[0019] In the method of the first aspect of the present invention, the determining of the scores of all point elements and the scores of all line elements in the target grid also includes: determining the length of each line element in the target grid, the score of each line element is based on the ratio of the length of each line element to the area of ​​the target grid, or the length of each line element is determined.

[0020] The method of the first aspect of the present invention further includes: generating a risk zoning map based on the density evaluation index of each sub-area.

[0021] To achieve the above-mentioned purpose, the second aspect of the present invention provides a mineral resources investment risk assessment system, comprising:

[0022] A data acquisition module is used to acquire the original data of the target area, wherein the original data includes five first-class data, namely, infrastructure data, mineral mining data, investment enterprise data, regional stability data, and geographic data. Each first-class data includes at least one second-class data, and all second-class data are divided into three types of element data, namely, point element, line element, and surface element;

[0023] A grid division module is used to use ArcGIS technology to perform data grid division on the target area based on the surface elements and screen out target grids that can be invested;

[0024] An investment score calculation module, used for calculating the investment score of each target grid in the target area based on the point elements, the line elements and the corresponding weights;

[0025] The selection module is used to calculate the density evaluation index of each sub-region of the target area based on the investment score of each target grid, wherein the sub-region with the largest density evaluation index is the region with the minimum investment risk of the required mineral resources.

[0026] To achieve the above-mentioned purpose, the third aspect of the present invention proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method proposed in the first aspect of the present invention.

[0027] The mineral resource investment risk assessment method, system and electronic equipment provided by the present invention obtain original data of a target area, wherein the original data includes five first-class data, namely, infrastructure data, mineral mining data, investment enterprise data, regional stability data and geographical data, each first-class data includes at least one second-class data, and all the second-class data are divided into three element data, namely, point elements, line elements and surface elements; ArcGIS technology is used to grid the target area based on surface elements and select target grids that can be invested; the investment score of each target grid in the target area is calculated based on the point elements, line elements and corresponding weights; the density evaluation index of each sub-area of ​​the target area is calculated based on the investment score of each target grid, wherein the sub-area with the largest density evaluation index is the required mineral resource investment risk minimum area. In this case, by integrating multiple data such as infrastructure data, mineral mining data, investment enterprise data, regional stability data, and geographic data, ArcGIS technology is used to grid the target area based on surface elements and screen out target grids that can be invested in. The investment score of each target grid in the target area is calculated based on point elements, line elements and corresponding weights, and then the density evaluation index of each sub-area is obtained. This solves the problem of existing technologies ignoring the fusion of multiple data, which is conducive to improving the accuracy of subsequent investment scores. The target grids are calculated in a refined manner, which further improves the accuracy of risk assessment.

[0028] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0030] Figure 1 A schematic diagram of a process flow of a mineral resource investment risk assessment method provided by an embodiment of the present invention;

[0031] Figure 2 A schematic diagram of a calculation process of an investment score of a target grid provided by an embodiment of the present invention;

[0032] Figure 3 A block diagram of a mineral resource investment risk assessment system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0034] The following describes the mineral resource investment risk assessment method and system according to an embodiment of the present invention with reference to the accompanying drawings.

[0035] The embodiment of the present invention provides a mineral resource investment risk assessment method to improve the accuracy of risk assessment.

[0036] Figure 1 A schematic flow chart of a mineral resource investment risk assessment method provided in an embodiment of the present invention.

[0037] like Figure 1 As shown, the mineral resources investment risk assessment method includes the following steps:

[0038] Step S101, obtaining the original data of the target area, the original data includes five types of first-class data: infrastructure data, mineral mining data, investment enterprise data, regional stability data, and geographic data. Each type of first-class data includes at least one type of second-class data, and all types of second-class data are divided into three types of element data: point elements, line elements, and surface elements.

[0039] In step S101 , the target region to be evaluated may be, but is not limited to, a country, a continent, or the like.

[0040] In step S101, after the target region to be evaluated is determined, the original data of the target region can be obtained from a database storing original data of the world.

[0041] In step S101, the original data includes five types of data: infrastructure data, mineral mining data, investment enterprise data, regional stability data, and geographic data.

[0042] In step S101, each type of first-category data includes at least one type of second-category data.

[0043] For example, the two types of data included in infrastructure data are: power plant point elements, port point elements, airport point elements, highway line elements, railway line elements and water system line elements.

[0044] Mineral and mining data include two types of data: mineral point elements and mining rights area elements. Mineral point elements include single mineral point elements and multiple mineral point elements.

[0045] The investment enterprise data include two types of data: Chinese and foreign-funded enterprise point elements. Chinese and foreign-funded enterprise point elements include Chinese-funded enterprise point elements and foreign-funded enterprise point elements.

[0046] The two types of data included in regional stability data are: terrorist activity information point elements.

[0047] Geographic data includes two types of data: protected area features and national and provincial boundary interface features. National and provincial boundary features can be national and provincial boundary data of countries around the world at the national level.

[0048] In step S101, all the two types of data are divided into three types of element data: point elements, line elements and surface elements. Specifically, power plant point elements, port point elements, airport point elements, mine point elements, Chinese and foreign-funded enterprise point elements, and terrorist activity information point elements are all point element data; highway line elements, railway line elements and water system line elements are all line element data; mining rights surface elements, protected area surface elements and national and provincial boundary interface elements are all surface element data.

[0049] Point factors are divided into first point factors, second point factors and third point factors, and the scoring process of different point factors is different. Among them, power plant point factors, port point factors, airport point factors and Chinese and foreign-funded enterprise point factors are all first point factors, mine point factors are second point factors, and terrorist activity information point factors are third point factors.

[0050] In step S101, three types of element data, namely point elements, line elements and surface elements, are saved in the form of point files, line files and surface files respectively.

[0051] In step S101, after the original data of the target area is obtained, the original data needs to be normalized (ie, standardized) and formatted to ensure data quality and consistency.

[0052] Step S102, using ArcGIS technology to divide the target area into data grids based on surface elements and screen out target grids that can be invested.

[0053] It is easy to understand that ArcGIS is a comprehensive Geographic Information System (GIS) platform. ArcGIS has data gridding technology.

[0054] In step S102, ArcGIS technology is used to grid the target area based on surface elements and select target grids that can be invested in, including: using ArcGIS technology to determine the target area based on national boundary and provincial interface elements, and dividing the target area into multiple grids; determining grids that cannot be invested in based on mining rights surface elements and protected area surface elements; eliminating grids that cannot be invested in from all grids in the target area, and the remaining grids are target grids that can be invested in.

[0055] Among them, it is considered that the basis of grid setting is in line with objective actual laws and level conditions. Too fine grid intervals will increase the workload and cause many null value phenomena; too coarse grid intervals will lead to unsatisfactory calculation results and unobvious effects. Therefore, the present invention determines the preset value of the grid interval based on experiments and actual needs. In the present invention, the grid interval is, for example, 30 minutes, where minutes are geographic coordinate units.

[0056] In addition, since mining rights indicate that the corresponding mining industry has been owned and protected areas are prohibited mining areas, grids that include mining rights surface elements and / or protected area surface elements are grids that cannot be invested in. Grids that cannot be invested in are eliminated from all grids in the target area, and the remaining grids are target grids that can be invested in. Subsequently, risk assessment is performed on target grids that can be invested in to obtain the area with the lowest investment risk.

[0057] Step S103, calculating the investment score of each target grid in the target area based on the point elements, line elements and corresponding weights.

[0058] Figure 2 Schematic diagram of the calculation process of the investment score of the target grid provided by the embodiment of the present invention. Figure 2 As shown, in step S103, the investment score of each target grid in the target area is calculated based on the point elements, line elements and corresponding weights, including: for any target grid, determining all the point elements and line elements in the target grid, and the corresponding first-class data (step S1031); determining the scores of all the point elements and the scores of all the line elements in the target grid (step S1032); calculating the scores of each first-class data in the target grid based on the scores of all the point elements, the scores of all the line elements and the corresponding weights (step S1033); calculating the investment score of the target grid based on the scores of each first-class data and the corresponding weights, and then obtaining the investment scores of all the target grids (step S1034).

[0059] For example, data classification and weight grading are shown in Table 1.

[0060] Table 1 Data classification and weight classification table

[0061]

[0062]

[0063] In Table 1, infrastructure, Chinese and foreign-funded enterprises, mineral resources and national stability are the abbreviations of infrastructure data, investment enterprise data, mineral data and regional stability data. Highways, railways, airports, ports, power plants and water systems are the abbreviations of highway line elements, railway line elements, airport point elements, port point elements, power plant point elements and water system line elements. Chinese-funded enterprises and foreign-funded enterprises are the abbreviations of Chinese-funded enterprise point elements and foreign-funded enterprise point elements. Single mineral species and multiple mineral species are the abbreviations of single mineral species point elements and multiple mineral species point elements. Terrorist activities are the abbreviations of terrorist activity information point elements.

[0064] Taking the weights shown in Table 1 as an example, for each target grid, after determining all the point elements and line elements in the target grid, as well as the corresponding first-class data, the scores of each first-class data in the target grid are calculated based on the scores of all point elements, the scores of all line elements and the corresponding second-class weights in Table 1; the investment score of the target grid is calculated based on the scores of each first-class data and the corresponding first-class weights in Table 1, and then the investment scores of all target grids are obtained.

[0065] In the embodiment of the present invention, the purpose of setting weights (i.e., first-class weights and second-class weights) is to make risk assessment more comparable and standardized, and to facilitate understanding and processing. The weight grading process is based on expert opinions, historical data, industry standards, and previous overseas cooperation experience. Each risk factor will be assigned a corresponding grade based on its possible consequences and the likelihood of occurrence.

[0066] In an embodiment of the present invention, the first type of weight and the second type of weight are dynamically adjustable. Specifically, weight adjustment is to update the existing weights according to new information, environmental changes or changes in risk conditions during the assessment process. Weight adjustment is a dynamic process that ensures that risk assessment always remains timely and accurate. In actual operation, regular review and adjustment of weights are an important part of risk management. The adjustment of weights has a significant impact on risk assessment. Different weight allocations reflect the relative importance of different risk factors to decision-making and project implementation.

[0067] The impacts of weight grading and weight adjustment include: 1) Priority: Changes in weights will re-prioritize risk factors. If the weight of a certain type of risk increases, then the management of this type of risk may be given more attention when making decisions. 2) Decision-making basis: Weight adjustment directly affects the results of risk assessment, thereby affecting investment decisions. A higher weight means that the risk has a greater influence on investment decisions. 3) Resource allocation: According to the degree of resource demand, the direction of resource investment can be adjusted according to the weight. 4) Risk management strategy: The adjustment of weights may prompt adjustments to risk management and response strategies. For example, if the weight of environmental risks increases, companies may strengthen environmental protection measures and community relationship management. 5) Dynamic adjustment: During the implementation of the project, if it is found that the actual impact of certain risks exceeds expectations, the original weights may need to be adjusted to reflect the actual situation. In summary, the adjustment of weights is dynamic and changes according to the different characteristics of different resource-rich areas, which fully reflects the refinement of risk analysis, aims to ensure the accuracy and practicality of risk assessment, serve companies to make the best mining investment decisions, discover risk exposure, reduce potential profit losses, and ensure the smooth progress of the project.

[0068] In step S1032, the process of determining the score of the point element and the score of the line element in each target grid (also called the scoring process) is different.

[0069] Specifically, in step S1032, the scores of all point elements and the scores of all line elements in the target grid are determined, including: if the target grid contains a first point element, the score of each first point element is determined based on the distance from each first point element to the center point of the target grid; if the target grid contains a second point element, the score of each second point element is determined based on the distance from each second point element to the center point of the target grid and the number of mineral types; if the target grid contains a third point element, the score of the third point element is a preset score.

[0070] In the present invention, power plant point elements, port point elements, airport point elements, and Chinese-funded enterprise point elements are all first point elements, and each power plant, port, airport, Chinese-funded enterprise or foreign-funded enterprise can be regarded as a first point element.

[0071] The score of each first point element is determined based on the distance from each first point element to the center point of the target grid, including: for any first point element, the score of the first point element is obtained according to the corresponding scoring rule based on the distance from the first point element to the center point of the target grid and the preset distance range. The scoring rule of the first point element defines different preset distance ranges and corresponding different scores.

[0072] Take the target grid M including a power plant point element as an example. If the scoring rule of the power plant point element defines the distance range from the power plant to the center point of the target grid including 5 preset distance ranges of within 100km, 100-200km, 200-300km, 300-500km, and above 500km, the scores corresponding to the 5 preset distance ranges are 10, 8, 5, 2, and 0 respectively. Determine the distance from the power plant point element to the center point of the target grid, and obtain the score of the power plant point element according to the scoring rule of the power plant point element. If the target grid includes multiple power plant point elements, the scores of each power plant point element are determined according to the scoring rule of the power plant point element. In addition, corresponding scoring rules are set for port point elements, airport point elements, and Chinese and foreign-funded enterprise point elements.

[0073] In the present invention, the mineral point element is a second point element. Each mineral point can be regarded as a second point element. Considering that the mineral points are divided into single mineral species and multiple mineral species, the score of the mineral point element is determined from two directions: distance and quantity.

[0074] Specifically, the score of each second point element is determined based on the distance from each second point element to the center point of the target grid and the number of mineral types, including: for any second point element, obtaining an initial score of the second point element based on the distance from the second point element to the center point of the target grid and a given distance range; obtaining the number of mineral types and the type of minerals of the second point element, and optimizing the initial score based on the number of mineral types and the type of minerals to obtain the final score of the second point element.

[0075] For example, when obtaining the initial score of a mineral point element, the initial score of the second point element is obtained according to the scoring rules of the mineral point element based on the distance from the mineral point element to the center point of the target grid and the given distance range. The scoring rules of the mineral point element define the given distance range from the mineral point to the center point of the target grid, and the scores corresponding to each given distance range. When obtaining the final score of a mineral point element, the optimization coefficient is determined based on the scoring optimization rules of the mineral point element, and the initial score is multiplied by the optimization coefficient to obtain the final score. Among them, the scoring optimization rules of the mineral point element define the different types of minerals and the corresponding optimization coefficients of the mineral types when a single mineral point is multi-ore.

[0076] In the present invention, the terrorist activity information point element is the third point element.

[0077] For example, if the target grid M contains a terrorist activity information point element, the score of the terrorist activity information point element is a preset score. The preset score is related to the number of times the terrorist attack point falls in the target grid M and the number of casualties in the target grid M.

[0078] Determining the scores of all point elements and all line elements in the target grid in step S1032 also includes: determining the length of each line element in the target grid, the score of each line element is based on the ratio of the length of each line element to the area of ​​the target grid, or the length of each line element.

[0079] In the present invention, the highway line elements, railway line elements and water system line elements are all line element data, and each highway, railway or water system can be regarded as a line element.

[0080] There are two ways to determine the score of line elements. One is to determine it based on the length of each line element and the set length range according to the line element length scoring rule; the other is to determine it based on the ratio of the length of each line element to the area of ​​the target grid, combined with the set ratio range, according to the line element ratio scoring rule. Among them, the line element length scoring rule defines different set length ranges and the scores corresponding to different set length ranges. The line element ratio scoring rule defines different set ratio ranges and the scores corresponding to different set ratio ranges.

[0081] Step S104, calculating the density evaluation index of each sub-region of the target area based on the investment score of each target grid, wherein the sub-region with the largest density evaluation index is the region with the minimum investment risk of the required mineral resources.

[0082] In step S104, if the target region is a country, the sub-region may be a provincial region, and if the target region is a continental region, the sub-region may be a country.

[0083] In step S104, after obtaining the investment score of each target grid obtained in step S103, the density evaluation index of each sub-region of the target area is calculated. The specific calculation process includes:

[0084] Count the number of target grids contained in each sub-region and the corresponding investment score;

[0085] The area of ​​the corresponding sub-region is calculated based on the area of ​​a single target grid and the number of target grids;

[0086] The investment scores of the target grids contained in each sub-region are summed to obtain the total investment score of the corresponding sub-region;

[0087] By calculating the ratio of the total investment score of each sub-region to the area of ​​the corresponding sub-region, the density evaluation index of the corresponding sub-region can be obtained. The larger the density evaluation index, the smaller the investment risk of the sub-region.

[0088] In step S104, the sub-region with the largest density evaluation index is the required region with the lowest mineral resource investment risk.

[0089] In step S104, risk levels may also be set to classify the calculated density evaluation index of each sub-region into different risk levels.

[0090] In this embodiment, the mineral resource investment risk assessment method further includes: generating a risk zoning map based on the density assessment index of each sub-region, wherein sub-regions of the same risk level can be displayed in the same color.

[0091] In order to implement the above embodiment, the present invention also proposes a mineral resource investment risk assessment system.

[0092] Figure 3 A block diagram of a mineral resource investment risk assessment system provided by an embodiment of the present invention.

[0093] like Figure 3 As shown, the mineral resource investment risk assessment system includes a data acquisition module 11, a grid division module 12, an investment score calculation module 13 and a selection module 14, wherein:

[0094] The data acquisition module 11 is used to acquire the original data of the target area, which includes five first-class data: infrastructure data, mineral mining data, investment enterprise data, regional stability data, and geographic data. Each first-class data includes at least one second-class data, and all the second-class data are divided into three types of element data: point element, line element, and surface element;

[0095] The grid division module 12 is used to use ArcGIS technology to divide the target area into data grids based on surface elements and screen out target grids that can be invested;

[0096] An investment score calculation module 13, used to calculate the investment score of each target grid in the target area based on the point elements, line elements and corresponding weights;

[0097] The selection module 14 is used to calculate the density evaluation index of each sub-region of the target area based on the investment score of each target grid, wherein the sub-region with the largest density evaluation index is the region with the minimum investment risk of the required mineral resources.

[0098] Furthermore, in a possible implementation of the embodiment of the present invention, in the data acquisition module 11, the mineral mining data includes mining rights surface elements, and the geographic data includes protected area surface elements and national boundary and provincial interface elements.

[0099] Furthermore, in a possible implementation of an embodiment of the present invention, in the data acquisition module 11, the infrastructure data includes power plant point elements, port point elements, airport point elements, highway line elements, railway line elements and water system line elements; the mineral mining data includes mine point elements; the investment enterprise data includes Chinese and foreign-funded enterprise point elements; the regional stability data includes terrorist activity information point elements; among them, the power plant point elements, port point elements, airport point elements and Chinese and foreign-funded enterprise point elements are all first point elements, the mine point elements are second point elements, and the terrorist activity information point elements are third point elements.

[0100] Furthermore, in a possible implementation of an embodiment of the present invention, the grid division module 12 is specifically used to: use ArcGIS technology to determine the target area based on national boundary and provincial interface elements, and divide the target area into multiple grids; determine grids that cannot be invested in based on mining rights surface elements and protected area surface elements; eliminate grids that cannot be invested in from all grids in the target area, and the remaining grids are target grids that can be invested in.

[0101] Furthermore, in a possible implementation of the embodiment of the present invention, the investment score calculation module 13 is specifically used to: for any target grid, determine all point elements and line elements in the target grid, and the corresponding class of data; determine the scores of all point elements and the scores of all line elements in the target grid; calculate the scores of each class of data in the target grid based on the scores of all point elements, the scores of all line elements and the corresponding weights; calculate the investment score of the target grid based on the scores of each class of data and the corresponding weights, and then obtain the investment scores of all target grids.

[0102] Furthermore, in a possible implementation of an embodiment of the present invention, in the investment score calculation module 13, the scores of all point elements and the scores of all line elements in the target grid are determined, including: if the target grid contains a first point element, the score of each first point element is determined based on the distance from each first point element to the center point of the target grid; if the target grid contains a second point element, the score of each second point element is determined based on the distance from each second point element to the center point of the target grid and the number of mineral types; if the target grid contains a third point element, the score of the third point element is a preset score.

[0103] Furthermore, in a possible implementation of an embodiment of the present invention, in the investment score calculation module 13, the score of each second point element is determined based on the distance from each second point element to the center point of the target grid and the number of mineral types, including: for any second point element, obtaining an initial score for the second point element based on the distance from the second point element to the center point of the target grid and a given distance range; obtaining the number of mineral types and the type of mineral types of the second point element, and optimizing the initial score based on the number of mineral types and the type of mineral types to obtain the final score of the second point element.

[0104] Furthermore, in a possible implementation of an embodiment of the present invention, in the investment score calculation module 13, the scores of all point elements and the scores of all line elements in the target grid are determined, and it also includes: determining the length of each line element in the target grid, and the score of each line element is based on the ratio of the length of each line element to the area of ​​the target grid, or the length of each line element.

[0105] Furthermore, in a possible implementation of the embodiment of the present invention, the mineral resource investment risk assessment system also includes a display module, and the display module is used to generate a risk zoning map based on the density assessment index of each sub-area.

[0106] It should be noted that the above explanation of the embodiment of the mineral resource investment risk assessment method is also applicable to the mineral resource investment risk assessment system of this embodiment, and will not be repeated here.

[0107] In an embodiment of the present invention, by obtaining original data of a target area, the original data includes five types of first-class data, namely, infrastructure data, mineral and mining data, investment enterprise data, regional stability data, and geographic data, each type of first-class data includes at least one type of second-class data, and all types of second-class data are divided into three types of element data, namely, point elements, line elements, and surface elements; ArcGIS technology is used to grid the target area based on surface elements and select target grids that can be invested; the investment score of each target grid in the target area is calculated based on the point elements, line elements, and corresponding weights; the density evaluation index of each sub-region of the target area is calculated based on the investment score of each target grid, wherein the sub-region with the largest density evaluation index is the region with the minimum investment risk of the required mineral resources. In this case, by integrating multiple data such as infrastructure data, mineral mining data, investment enterprise data, regional stability data, and geographic data, ArcGIS technology is used to grid the target area based on surface elements and screen out target grids that can be invested in. The investment score of each target grid in the target area is calculated based on point elements, line elements and corresponding weights, and then the density evaluation index of each sub-area is obtained. This solves the problem of existing technologies ignoring the fusion of multiple data, which is conducive to improving the accuracy of subsequent investment scores. The target grids are calculated in a refined manner, which further improves the accuracy of risk assessment.

[0108] The method and system of the present invention efficiently integrate the multivariate information of geology, environment and economy through ArcGIS data gridding technology, score determination and weight design. Through data grid technology, fine spatial management of risk factors is achieved, and the accuracy of risk prediction is significantly improved. In the weight design, expert knowledge and multi-criteria decision-making strategies are integrated to ensure the comprehensiveness and objectivity of risk assessment. The accuracy of mineral risk area division is greatly improved, providing a strong basis for actual decision-making. It not only optimizes the mining risk management process, but also provides valuable reference cases for other geographic information system application fields. It not only improves the accuracy and efficiency of risk assessment and promotes the scientific process of resource development decision-making, but also effectively optimizes mineral exploration investment, reduces the environmental and social costs caused by blind development, and helps the construction of green mines. At the same time, it also provides a powerful tool for government supervision, which helps to formulate more reasonable mineral resource management policies.

[0109] The method and system of the present invention break through many limitations of the prior art and improve the accuracy and comprehensiveness of mineral risk zoning. It solves the problems of low accuracy, neglect of multivariate data fusion, and subjectivity in weight setting in the current mineral risk zoning. It makes the subsequent risk assessment more accurate. The advantages of the method and system of the present invention include but are not limited to: the weights are dynamically adjustable, and the adjustment of the weights is combined with expert knowledge and quantitative analysis to ensure the objectivity and accuracy of the risk assessment; through data gridding technology, complex geographic spatial information is converted into actionable numerical values, which facilitates the quantitative evaluation of risk analysis and the interpretation of results, and provides a scientific basis for risk decision-making in the mining field.

[0110] Compared with the traditional methods that rely on manual input and fixed rules, the processing of large-scale and high-dimensional spatial data is inefficient and difficult to cope with changing geological conditions; these limitations make the existing risk warning and management systems in the geological industry appear to be not accurate and efficient enough. The data processing efficiency is low, the accuracy is greatly affected by human factors, and it is impossible to accurately analyze the characteristics of specific minerals. The method and system of the present invention can dynamically update the analysis results based on the original data obtained in real time, and provide personalized risk warning services. This will greatly improve the accuracy of the warning and reduce misjudgment. In addition, when evaluating risk zoning, the setting of weights takes into account the authoritative opinions of experts. By increasing the weight of experts, the weights of various types of data are made more accurate. This not only increases the comprehensiveness of the evaluation, but also improves the scientific nature of decision-making, improves the accuracy of risk identification, and provides decision makers with insights and suggestions from senior experts in the industry. It provides a more comprehensive and objective perspective for the evaluation of the mining investment environment. It not only helps to optimize investment strategies, but also helps to promote the rational development and utilization of my country's mineral resources, as well as maintain the country's economic security and environmental sustainability.

[0111] In order to implement the above embodiments, the present invention also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0112] In order to implement the above embodiments, the present invention further proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0113] In order to implement the above embodiments, the present invention further provides a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0114] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0115] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0116] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0118] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0119] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0120] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0121] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the risk of mineral resource investment, characterized in that: include: Obtaining the original data of the target area, the original data includes five first-class data, namely, infrastructure data, mineral mining data, investment enterprise data, regional stability data, and geographic data. Each first-class data includes at least one second-class data, and all second-class data are divided into three types of element data, namely, point element, line element, and surface element; Using ArcGIS technology to divide the target area into data grids based on the surface elements and screen out target grids that can be invested; Calculate the investment score of each target grid in the target area based on the point elements, the line elements and the corresponding weights; The density evaluation index of each sub-region of the target area is calculated based on the investment score of each target grid, wherein the sub-region with the largest density evaluation index is the region with the minimum investment risk of the required mineral resources.

2. The method for evaluating mineral resource investment risk according to claim 1, characterized in that: The mineral mining data includes mining rights surface elements, the geographic data includes protected area surface elements and national and provincial boundary interface elements, and the use of ArcGIS technology to grid the target area based on the surface elements and screen out target grids that can be invested includes: Using ArcGIS technology to determine the target area based on the national and provincial interface elements, the target area is divided into a plurality of grids; Determine the grid that cannot be invested based on the mining rights surface element and the protection area surface element; Grids that cannot be invested in are eliminated from all grids in the target area, and the remaining grids are target grids that can be invested in.

3. The method for evaluating mineral resource investment risk according to claim 1, characterized in that: The calculating the investment score of each target grid in the target area based on the point elements, the line elements and the corresponding weights includes: For any target grid, determine all point elements and line elements in the target grid, as well as the corresponding type of data; Determine the scores of all point elements and all line elements in the target grid; The scores of each type of data in the target grid are calculated based on the scores of all point elements, the scores of all line elements and the corresponding weights; The investment score of the target grid is calculated based on the score of each type of data and the corresponding weight, and then the investment scores of all target grids are obtained.

4. The method for evaluating mineral resource investment risk according to claim 3, characterized in that: The infrastructure data includes power plant point elements, port point elements, airport point elements, highway line elements, railway line elements and water system line elements; the mineral mining data includes mine point elements; the investment enterprise data includes Chinese and foreign-funded enterprise point elements; the regional stability data includes terrorist activity information point elements; among them, power plant point elements, port point elements, airport point elements, Chinese and foreign-funded enterprise point elements are all first point elements, mine point elements are second point elements, and terrorist activity information point elements are third point elements.

5. The method for evaluating mineral resource investment risk according to claim 4, characterized in that: Determining the scores of all point elements and all line elements in the target grid includes: If the target grid includes the first point element, the score of each first point element is determined based on the distance from each first point element to the center point of the target grid; If the target grid contains the second point element, the score of each second point element is determined based on the distance from each second point element to the center point of the target grid and the number of mineral types; If the target grid includes the third point element, the score of the third point element is a preset score.

6. The method for evaluating mineral resource investment risk according to claim 5, characterized in that: The score of each second point element is determined based on the distance from each second point element to the center point of the target grid and the number of mineral types, including: For any second point element, an initial score of the second point element is obtained based on the distance from the second point element to the center point of the target grid where the second point element is located and a given distance range; The number and type of mineral species of the second point element are obtained, and the initial score is optimized based on the number and type of mineral species to obtain a final score of the second point element.

7. The method for evaluating mineral resource investment risk according to claim 5, characterized in that: The step of determining the scores of all point elements and all line elements in the target grid further includes: The length of each line element in the target grid is determined, and the score of each line element is based on the ratio of the length of each line element to the area of ​​the target grid, or the length of each line element.

8. The method for evaluating mineral resource investment risk according to claim 1, characterized in that: Also includes: Generate a risk zoning map based on the density evaluation index of each sub-area.

9. A mineral resources investment risk assessment system, characterized in that: include: A data acquisition module is used to acquire the original data of the target area, wherein the original data includes five first-class data, namely, infrastructure data, mineral mining data, investment enterprise data, regional stability data, and geographic data. Each first-class data includes at least one second-class data, and all second-class data are divided into three types of element data, namely, point element, line element, and surface element; A grid division module is used to use ArcGIS technology to perform data grid division on the target area based on the surface elements and screen out target grids that can be invested; An investment score calculation module, used for calculating the investment score of each target grid in the target area based on the point elements, the line elements and the corresponding weights; The selection module is used to calculate the density evaluation index of each sub-region of the target area based on the investment score of each target grid, wherein the sub-region with the largest density evaluation index is the region with the minimum investment risk of the required mineral resources.

10. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 8.

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