Mine resource mining design system based on big data

Abstract

The invention relates to the technical field of big data, and discloses a mine resource mining design system based on big data. According to the system, geology, environment and resource data of a mine are acquired and stored in real time by integrating the data acquisition module and the big data platform, and the data processing and analysis module performs denoising and missing value filling on the data to form a unified data set, so that the credibility of the data is improved, design errors caused by manual operation are reduced, and the design efficiency is improved. The resource assessment module generates a detailed resource assessment report according to comprehensive data analysis and provides a scientific basis for mining design, a comprehensive plan formulated by the mining design module on the basis can identify and control potential risks through verification of the risk assessment report module, and when it is found that the risks do not conform to operation regulations, the risk assessment report module gives an alarm to the mining design module. The system can timely feed back to the data acquisition module, deeper data collection and analysis are carried out, and the comprehensive decision support system can better cope with complex geological conditions and market demand changes.

Description

Technical Field

[0001] The present invention relates to the technical field of big data, and in particular to a mining resource exploitation design system based on big data. Background Art

[0002] Mining resource exploitation is an important pillar of modern industrial and economic development, and plays an irreplaceable role in national and regional economic growth, energy supply and raw material security. With the continuous growth of the global population and the acceleration of industrialization, the demand for mineral resources has become more urgent. Effective mining of mineral resources can not only meet market demand, but also promote local economic development and create a large number of employment opportunities. However, the mining process of mineral resources is complex and involves many factors, including geological conditions, environmental protection, economic benefits, etc. Scientific and reasonable planning and design must be carried out to ensure the sustainable use of resources and the protection of the ecological environment.

[0003] Traditional mining resource exploitation design methods often rely on designer experience and a single data source, lacking a comprehensive analysis of the mine environment and resource conditions, leading to limitations in resource exploitation decisions. In addition, the data processing and analysis process is often manual, inefficient, and prone to design errors, making it difficult to cope with complex geological conditions and changing market demands. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the shortcomings of the prior art, the present invention provides a mining resource exploitation design system based on big data. By integrating a data acquisition module and a big data platform, it is not only possible to acquire and store the geological, environmental and resource data of the mine in real time, but also to denoise and fill missing values ​​of these data through a data processing and analysis module to form an accurate and unified data set. This method significantly improves the credibility of the data, reduces the design errors caused by manual operations, and improves efficiency. The resource evaluation module generates a detailed resource evaluation report based on comprehensive data analysis to provide a scientific basis for mining design and ensure the feasibility and rationality of the plan. The comprehensive plan formulated by the mining design module on this basis can identify and control potential risks after verification by a risk assessment report module. When it is found that the risk does not meet the operating regulations, the system will promptly feedback to the data acquisition module for deeper data collection and analysis to promote continuous optimization and improvement. This comprehensive decision support system can better cope with complex geological conditions and changes in market demand, effectively improve the scientificity and sustainability of mining resources, and solve the above problems.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a mining resource exploitation design system based on big data, comprising a data acquisition module, a data processing and analysis module, a resource assessment module, a mining design module and a risk assessment report module;

[0008] The data acquisition module acquires the geological data, mine environment data and mineral resource data of the mine through geological exploration equipment, and stores the data in the big data platform;

[0009] The data processing and analysis module removes noise and fills missing values ​​for the geological data, mining environment data and mineral resource data of the mine stored in the big data platform, and after forming a unified data set, calculates the ore grade, predicted ore reserves, effective mining depth of equipment, ore body volume, unit ore mining cost and mining environment risk index, and sends them to the resource assessment module;

[0010] The resource evaluation module calculates the feasibility of mining resources according to the values ​​transmitted by the data processing and analysis module, and performs feasibility evaluation of mining resources, generates a resource evaluation report and sends it to the mining design module;

[0011] The mining design module formulates a comprehensive mining plan based on the resource assessment report and sends it to the risk assessment report module;

[0012] The risk assessment report module conducts a risk assessment on the comprehensive mining plan for mining resources. When it is identified that the risk assessment for mining resources does not meet the actual operating regulations, the information is fed back to the data collection module for more detailed data collection until the risk assessment meets the actual mining resource mining specifications.

[0013] Preferably, the formula for data denoising is as follows:

[0014]

[0015] In the formula, MY i represents the moving average of the geological data points, mining environment data points, or mineral resource data points of the i-th mine, N represents the window size for data denoising, and x j It represents the kth original data point in the original mine geological data, mine environment data or mineral resource data, and i and j represent counting subscripts.

[0016] Preferably, the formula for filling missing values ​​with data is as follows:

[0017]

[0018] In the formula, Tb represents the geological data points, mine environment data points or mineral resource data points of the filled mine, m represents the number of valid geological data points, mine environment data points or mineral resource data points of the mine, and X represents the set of geological data points, mine environment data points or mineral resource data points of all valid mines.

[0019] Preferably, the calculation formula for the ore grade is as follows:

[0020]

[0021] In the formula, Grap represents the ore grade, that is, the proportion of useful minerals in the ore, Ykzl represents the mass of useful minerals, and Kzzl represents the total mass of the ore.

[0022] Preferably, the ore predicted reserves calculation formula is as follows:

[0023]

[0024] In the formula, Yccl represents the predicted reserves of ore, Kctj represents the volume of the ore body, Indicates the average grade of the ore.

[0025] Preferably, the calculation formula for the effective mining depth of the equipment is as follows:

[0026] Efft=Dpth-Opls

[0027] In the formula, Efft represents the effective mining depth of the equipment, Dpth represents the maximum safe depth, and Opls represents the operating loss caused by equipment limitations or geological conditions.

[0028] Preferably, the ore body volume calculation formula is as follows:

[0029] Kctj=Jdmj*Efft

[0030] In the formula, Kctj represents the volume of the ore body, Jdmj represents the base area of ​​the ore body, which is obtained based on geological exploration, and Efft represents the effective mining depth of the equipment.

[0031] Preferably, the unit ore mining cost calculation formula is as follows:

[0032]

[0033] In the formula, Unit Cost represents the unit ore mining cost, Zkcc represents the total mining cost, including manpower, equipment and operating expenses, and Kzkl represents the total amount of ore mined.

[0034] Preferably, the calculation formula of the mining environment risk index is as follows:

[0035]

[0036] In the formula, Hkzs represents the mining environment risk index, Risk t It represents the numerical assessment of the t-th environmental risk factor. Each risk factor is quantified through investigation and monitoring data, including soil erosion risk, ecological impact risk, noise pollution risk and waste disposal risk. t represents the circular variable and p represents the number of total risk factors.

[0037] Preferably, the feasibility calculation formula for mining resources is as follows:

[0038]

[0039] In the formula, Fspt represents the feasibility of mining resources, Xmjx represents the net present value of the project, and Psym represents the total initial investment of the mining project.

[0040] Compared with the prior art, the present invention provides a mining resource mining design system based on big data, which has the following beneficial effects:

[0041] By integrating the data acquisition module and the big data platform, the present invention can not only acquire and store the geological, environmental and resource data of the mine in real time, but also denoise and fill in missing values ​​of these data through the data processing and analysis module to form an accurate and unified data set. This method significantly improves the credibility of the data, reduces the design errors caused by manual operation, and improves efficiency. The resource assessment module generates a detailed resource assessment report based on comprehensive data analysis to provide a scientific basis for mining design and ensure the feasibility and rationality of the plan. The comprehensive plan formulated by the mining design module on this basis can identify and control potential risks after verification by the risk assessment report module. When it is found that the risk does not meet the operating regulations, the system will promptly feedback to the data acquisition module for deeper data collection and analysis to promote continuous optimization and improvement. This comprehensive decision support system can better cope with complex geological conditions and changes in market demand, and effectively improve the scientificity and sustainability of mine resource exploitation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Traditional mining resource mining design methods often rely on designer experience and a single data source, lack of comprehensive analysis of the mining environment and resource conditions, resulting in limitations in resource mining decisions. In addition, data processing and analysis processes are often manual operations, which are inefficient and prone to design errors. It is difficult to cope with complex geological conditions and changing market demands. Therefore, a mining resource mining design system based on big data is proposed. Please refer to Figure 1 ,The system includes data acquisition module, data processing and analysis module, resource assessment module, mining design module and risk assessment report module;

[0045] The data acquisition module uses high-precision geological radar, seismic wave exploration, soil sampling and environmental monitoring stations. These devices can go deep underground to monitor and collect various key parameters in real time, such as stratum structure, rock properties, ore deposit distribution and surrounding environmental influencing factors. At the same time, the use of drone remote sensing technology can efficiently collect surface data and provide a comprehensive perspective of the mining area. These data are quickly uploaded to the big data platform through wireless networks to ensure the real-time and integrity of the data. The platform uses data sorting and storage technology to store massive information in structured and unstructured forms to form a comprehensive data foundation. This integration and real-time update capabilities not only improve the efficiency and accuracy of data collection, but also lay a solid foundation for subsequent data processing and analysis, and provide reliable data support for the scientific management and decision-making of mining resources;

[0046] The data processing and analysis module removes noise and fills missing values ​​for the geological data, mining environment data and mineral resource data of the mine stored in the big data platform, and calculates the ore grade, predicted ore reserves, effective mining depth of equipment, ore body volume, unit ore mining cost and mining environment risk index after forming a unified data set, among which:

[0047] The formula for data denoising is as follows:

[0048]

[0049] De-noising technology can effectively eliminate random errors and inaccurate values ​​in data, making the data purer, which provides a reliable basis for subsequent data analysis and decision-making. In the formula, MY irepresents the moving average of the geological data points, mining environment data points, or mineral resource data points of the i-th mine, N represents the window size for data denoising, and x j It represents the jth original data point in the original mine geological data, mine environment data or mineral resource data. i and j represent counting subscripts. The denoised data set is usually smaller, which reduces the complexity of calculation and storage, making subsequent data processing more efficient and saving time and resources.

[0050] The formula for filling missing values ​​is as follows:

[0051]

[0052] In the actual mining environment, data collection may be affected by various factors. A perfect filling mechanism can maintain the continuity of data and ensure that real-time analysis is not interrupted. In the formula, Tb represents the geological data points, mining environment data points or mineral resource data points of the mine after filling, m represents the number of valid geological data points, mining environment data points or mineral resource data points of the mine, and X represents the set of all valid geological data points, mining environment data points or mineral resource data points of the mine. By filling in missing values, analysis bias caused by incomplete data can be avoided, so that each data point can participate in subsequent analysis and improve data integrity.

[0053] The formula for calculating ore grade is as follows:

[0054]

[0055] The calculation of ore grade helps determine the proportion of useful components in the ore, thereby optimizing the production plan, ensuring the priority mining of high-grade ore, and improving resource utilization efficiency. In the formula, Grap represents the ore grade, that is, the proportion of useful minerals in the ore, Tkzl represents the mass of useful minerals, and Kzzl represents the total mass of the ore. By understanding the grade of the ore, it can be decided whether to continue mining a specific area to avoid waste of resources caused by low-grade ore;

[0056] The formula for calculating the predicted ore reserves is as follows:

[0057]

[0058] Accurate predicted reserves enable mines to formulate reasonable mining plans and ensure that mines have sufficient ore supply at different stages. In the formula, Yccl represents the predicted ore reserves, and Kctj represents the volume of the ore body. Indicates the average grade of ore. By accurately predicting ore reserves, market risks can be better managed and timely responses to market changes can be made;

[0059] The calculation formula of the effective mining depth of the equipment is as follows:

[0060] Efft=Dpth-Opls

[0061] Understanding the effective mining depth of equipment can help companies better configure and use mining equipment and maximize their mining efficiency. In the formula, Efft represents the effective mining depth of equipment, Dpth represents the maximum safe depth, and Opls represents the operating loss caused by equipment limitations or geological conditions. By scientifically determining the mining depth, safety risks in operations can be reduced and potential dangers caused by ultra-deep mining can be avoided.

[0062] The formula for calculating the ore body volume is as follows:

[0063] Kctj=Jdmj*Efft

[0064] The calculation of ore body volume can help evaluate the resource potential of the entire ore deposit and set goals for long-term mining and management of mineral resources. In the formula, Kctj represents the ore body volume, Jdmj represents the base area of ​​the ore body, which is obtained based on geological exploration, and Efft represents the effective mining depth of the equipment;

[0065] The unit ore mining cost calculation formula is as follows:

[0066]

[0067] The calculation of unit mining cost is an important basis for making budgets and controlling mining costs, which helps to optimize mining management and improve economic benefits. In the formula, Unit Cost represents the unit ore mining cost, Zkcc represents the total mining cost, including manpower, equipment and operating expenses, and Kzkl represents the total amount of ore mined. Through effective cost control, we can maintain competitive advantages in the market and achieve higher profit margins.

[0068] The calculation formula of mining environment risk index is as follows:

[0069]

[0070] By calculating the environmental risk index, we can identify the potential threats to the environment from mining, and thus formulate effective preventive measures to reduce environmental damage. In the formula, Hkzs represents the mining environmental risk index, and Risk t It represents the numerical evaluation of the t-th environmental risk factor. Each risk factor is quantified through survey and monitoring data, including soil erosion risk, ecological impact risk, noise pollution risk and waste disposal risk. t represents the cycle variable and p represents the number of total risk factors. This indicator provides a scientific basis for the sustainable development of mines, taking into account environmental and social responsibilities while pursuing economic benefits.

[0071] The resource assessment module calculates the feasibility of mining resources according to the values ​​transmitted by the data processing and analysis module, and conducts a feasibility assessment of mining resources. The feasibility calculation formula for mining resources is as follows:

[0072]

[0073] By calculating the feasibility of resource mining, it can provide decision makers with a scientific basis based on data and reduce the risk of wrong decisions. In the formula, Fspt represents the feasibility of mining resources, Xmjx represents the net present value of the project, and Psym represents the total initial investment of the mining project. Comprehensive consideration of economic, environmental and social factors makes the assessment more comprehensive and promotes sustainable mining. The assessment results will generate a detailed resource assessment report, which includes statistical data, charts and suggestions for feasibility analysis to ensure the readability and transparency of information. In addition, the module also combines multidisciplinary principles such as geological engineering, environmental science and economics to conduct risk assessment and sensitivity analysis to identify potential uncertainties and risk factors. Finally, the reviewed resource assessment report will be sent to the mining design module to provide a scientific basis and guidance for the subsequent mining plan formulation to ensure the efficient, safe and sustainable development of mining resources.

[0074] After receiving the resource assessment report, the mining design module uses planning tools to develop a detailed comprehensive mining plan for mine resources. This plan takes into account resource allocation, mining methods, equipment selection, human resources and other aspects, and uses geological models, production scheduling optimization and simulation technology to ensure the effectiveness and feasibility of the mining strategy. During the development process, the module will also refer to the data and experience of previous mining projects, and continuously optimize and adjust the design plan to ensure the scientificity and practicality of the plan;

[0075] Once the comprehensive plan is completed, it will be sent to the risk assessment report module. Relying on risk assessment tools, a combination of quantitative and qualitative methods will be used to comprehensively evaluate the comprehensive plan. This module uses a risk analysis platform to assess potential environmental risks, economic risks and operational risks, and uses probability models and scenario analysis techniques to quantify the potential impact of various risk factors. If certain risk indicators are identified in the assessment that do not meet actual operating procedures or safety standards, the module will automatically generate feedback information, indicating the need to re-collect more detailed data to correct the risk assessment. This feedback will be sent back to the data collection module in a timely manner, and necessary data updates and supplements will be made by adding on-site monitoring, remote sensing technology and other data collection methods. This cycle continues until the risk assessment results are consistent with the mining resource exploitation specifications, ensuring that each link meets the requirements of safety, efficiency and sustainable development.

[0076] Through the comprehensive application of the above systems, we can better cope with complex geological conditions and changes in market demand, and effectively improve the scientificity and sustainability of mining resources exploitation.

[0077] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A mining resource mining design system based on big data, characterized by: It includes data collection module, data processing and analysis module, resource assessment module, mining design module and risk assessment report module; The data acquisition module acquires the geological data, mine environment data and mineral resource data of the mine through geological exploration equipment, and stores the data in the big data platform; The data processing and analysis module removes noise and fills missing values ​​for the geological data, mining environment data and mineral resource data of the mine stored in the big data platform, and after forming a unified data set, calculates the ore grade, predicted ore reserves, effective mining depth of equipment, ore body volume, unit ore mining cost and mining environment risk index, and sends them to the resource assessment module; The resource evaluation module calculates the feasibility of mining resources according to the values ​​transmitted by the data processing and analysis module, and performs feasibility evaluation of mining resources, generates a resource evaluation report and sends it to the mining design module; The mining design module formulates a comprehensive mining plan based on the resource assessment report and sends it to the risk assessment report module; The risk assessment report module conducts a risk assessment on the comprehensive mining plan for mining resources. When it is identified that the risk assessment for mining resources does not meet the actual operating regulations, the information is fed back to the data collection module for more detailed data collection until the risk assessment meets the actual mining resource mining specifications.

2. The mining resource exploitation design system based on big data according to claim 1, characterized in that: The formula for data denoising is as follows: In the formula, MY i represents the moving average of the geological data points, mining environment data points, or mineral resource data points of the i-th mine, N represents the window size for data denoising, and x j It represents the jth original data point in the original mine geological data, mine environment data or mineral resource data, and i and j represent counting subscripts.

3. The mining resource exploitation design system based on big data according to claim 2 is characterized by: The formula for filling missing values ​​in the data is as follows: In the formula, Tb represents the geological data points, mine environment data points or mineral resource data points of the filled mine, m represents the number of valid geological data points, mine environment data points or mineral resource data points of the mine, and X represents the set of geological data points, mine environment data points or mineral resource data points of all valid mines.

4. The mining resource exploitation design system based on big data according to claim 3 is characterized by: The calculation formula of the ore grade is as follows: In the formula, Grap represents the ore grade, that is, the proportion of useful minerals in the ore, Ykzl represents the mass of useful minerals, and Kzzl represents the total mass of the ore.

5. The mining resource exploitation design system based on big data according to claim 4 is characterized by: The calculation formula for the predicted ore reserves is as follows: In the formula, Yccl represents the predicted reserves of ore, Kctj represents the volume of the ore body, Indicates the average grade of the ore.

6. The mining resource exploitation design system based on big data according to claim 5 is characterized by: The calculation formula for the effective mining depth of the equipment is as follows: Efft=Dpth-Opls In the formula, Efft represents the effective mining depth of the equipment, Dpth represents the maximum safe depth, and Opls represents the operating loss caused by equipment limitations or geological conditions.

7. The mining resource exploitation design system based on big data according to claim 6 is characterized by: The ore body volume calculation formula is as follows: Kctj=Jdmj*Efft In the formula, Kctj represents the volume of the ore body, Jdmj represents the base area of ​​the ore body, which is obtained based on geological exploration, and Efft represents the effective mining depth of the equipment.

8. The mining resource exploitation design system based on big data according to claim 7 is characterized by: The unit ore mining cost calculation formula is as follows: In the formula, Unit Cost represents the unit ore mining cost, Zkcc represents the total mining cost, including manpower, equipment and operating expenses, and Kzkl represents the total amount of ore mined.

9. The mining resource exploitation design system based on big data according to claim 8, characterized in that: The calculation formula of the mining environment risk index is as follows: In the formula, Hkzs represents the mining environment risk index, Risk t It represents the numerical assessment of the t-th environmental risk factor. Each risk factor is quantified through investigation and monitoring data, including soil erosion risk, ecological impact risk, noise pollution risk and waste disposal risk. t represents the circular variable and p represents the number of total risk factors.

10. The mining resource exploitation design system based on big data according to claim 9, characterized in that: The feasibility calculation formula for mining resources is as follows: In the formula, Fspt represents the feasibility of mining resources, Xmjx represents the net present value of the project, and Psym represents the total initial investment of the mining project.

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