677 results about "Mineral Sources" patented technology

The invention discloses a prospecting target area prediction method and system based on geological three-dimensional modeling, and relates to the technical field of mineral resource exploration, and the method comprises the steps: obtaining geological, geophysical, geochemical and remote sensing data of a target area, carrying out the preprocessing, generating a modeling data set, and carrying out the geological modeling; constructing a lithology classification model by combining the three-dimensional geologic model with deep learning, and extracting shallow lithology features to predict the deep lithology of the target area; screening metallogenic factors according to the prediction result of the deep lithology of the target area, the deep geophysical data and the three-dimensional geologic model, and generating a metallogenic prediction factor set; and establishing a prospecting prediction model based on the graph convolutional network, importing the metallogenic prediction factor set to obtain a metallogenic distribution prediction result, and carrying out three-dimensional prospecting target area delineation. According to the method, deep lithologic stratum prediction is carried out through multi-source geological data and geological three-dimensional modeling, and target region delineation is carried out by using the graph convolutional neural network, so that the efficiency and precision of prospecting target region prediction are improved, and the exploration cost is reduced.

The invention relates to the technical field of mineral resource reserve simulation, in particular to a geological mineral resource reserve simulation method. The method comprises the following steps: carrying out multi-source heterogeneous data acquisition on a target geological area, and carrying out comprehensive feature extraction to obtain a comprehensive data set; constructing an initial static geologic model according to the comprehensive data set to obtain the initial static geologic model; building a multi-physics field coupling model according to the initial static geologic model to obtain a multi-physics field coupling model grid; performing metallogenic fluid migration and enrichment simulation according to the multi-physics field coupling model grid to obtain a metallogenic fluid migration simulation result; carrying out geologic body geometrical morphology updating on the initial static geologic model according to a metallogenic fluid migration simulation result to obtain a dynamic geologic model; and performing reserve prediction model training according to the dynamic geologic model to obtain a reserve prediction model. According to the method, the mineral resource reserves are evaluated more accurately and dynamically through a mineral resource reserve simulation technology.

The invention discloses a mineral resource intelligent prediction method and system based on multi-source heterogeneous data fusion and deep learning, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data, and carrying out the standardization processing to form a structured data set; multi-source heterogeneous data fusion: realizing data layer space registration and feature layer weight dynamic allocation through an attention mechanism multi-modal fusion module, and outputting a high-dimensional metallogenic feature vector; constructing a CNN-LSTM mixed deep learning model and completing initialization training, and outputting an initial mineralization probability graph; and establishing a dynamic updating engine, performing model increment training based on transfer learning, correcting the mineralization probability through positive and negative sample reinforcement learning in combination with a newly added data type, and outputting a time sequence dynamic mineralization probability graph. According to the method, mineralization probability dynamic evaluation and risk quantitative updating are realized, the prediction precision and the model updating efficiency are improved, the method is adaptive to a multi-stage exploration scene, and accurate real-time support is provided for exploration decision making.

The invention relates to the technical field of mineral resource management, in particular to a mineral resource dynamic prediction and mining management system which comprises a data perception and fusion layer, a unified digital twinborn model, a dynamic prediction and decision intelligent agent and a visualization and interaction control layer. The data perception and fusion layer collects structured data such as geological exploration and mining environment and market unstructured data, and generates a unified space-time tensor through processing; the unified digital twinborn model generates a dynamic comprehensive mining area situation map containing resource reserve risk economic indicators through a three-dimensional convolutional neural network embedded with an attention mechanism; the dynamic prediction and decision-making agent predicts future reserves and geological risks, and constructs a dual-objective optimization model to generate an optimal mining path equipment scheduling and resource allocation scheme; and the visualization and interaction control layer presents the mining area state and the decision scheme in a three-dimensional manner and provides an interaction interface. According to the invention, the data utilization rate and decision scientificity are improved, the safety risk is reduced, and mine management intellectualization is promoted.

The embodiment of the invention provides a BIM-GIS three-dimensional twin geological modeling method and system based on cognitive enhancement. The method is applied to the technical field of geological modeling, and comprises the following steps: acquiring different data sources to carry out geological data acquisition, and carrying out preprocessing to obtain preprocessed geological data; converting the geologic body into a voxel three-dimensional model according to the preprocessed geologic data; performing multi-level optimization processing on the voxel three-dimensional model, constructing an optimized model, and performing three-dimensional display; and performing mineral resource space distribution prediction according to the voxel three-dimensional model, predicting the position and the number of mineral resources in combination with historical exploration data and a convolutional neural network, and performing underground water flow simulation analysis in combination with an underground water flow model. In this way, by combining the BIM and GIS technologies, the three-dimensional geological data can be efficiently and accurately managed and displayed, and the method can be widely applied to the fields of geological exploration, mining development, urban construction and the like.

The embodiment of the invention provides a mineral target area intelligent delineation method and system based on multi-source heterogeneous geoscience data, and relates to the technical field of mineral resource exploration, and the method comprises the steps: dividing a multi-source heterogeneous geoscience data set comprising geological structure, geochemistry, geophysics and remote sensing image data into a plurality of data modules according to a geological action influence range; each data module records spatial distribution and attribute information, then a dynamic mapping relation between the data modules and a mineral product mineralization mechanism is constructed, a mineralization association chain between the data modules is generated based on the dynamic mapping relation, a cooperative change rule of nodes and connection paths is defined, then a target region feature set conforming to the mineralization association chain is screened, and a target region feature set is obtained; and finally, according to a collaborative change rule, judging the integrating degree of a target region feature set space unit and a mineralization condition, dynamically adjusting a space boundary range, and generating a mineral target region delineation result, thereby comprehensively utilizing multi-source data, deeply mining a relationship with a mineralization mechanism, and improving the accuracy and reliability of mineral target region delineation.

The invention discloses a metal ore body identification method based on remote sensing interpretation, relates to the technical field of mineral resource exploration, and aims to solve the problems of low identification precision, neglect of geological laws and poor generalization caused by dependence on single remote sensing data in the prior art. After standardization processing, spectrum and texture features are fused to generate a composite vector; extracting a multi-scale mineralization signal by using a convolutional neural network, and interpreting and constructing an ore control area in combination with a terrain gradient and a curvature; alteration zoning is identified through spectrum angle matching, and a favorable mineralization area is determined by combining lithology combination analysis; and dynamically weighting geological elements by adopting a deep learning fusion network, outputting an ore body position according to a comprehensive score, and finally optimizing a boundary through spatial clustering and morphological filtering. The method significantly improves the automation degree and reliability of ore body identification in a complex environment, and is suitable for metal mineral exploration target area delineation.

The invention belongs to the technical field of geophysical methods, and particularly discloses a geophysical electromagnetic method data inversion method and system based on entropy constraint, and the method comprises the steps: dividing an inversion space, setting a conductivity value, and constructing an initial inversion model; a magnetotelluric regularization inversion objective function based on entropy constraint is constructed; and adopting a Gaussian-Newton method to minimize the magnetotelluric regularization inversion objective function, updating the initial inversion model, obtaining a conductivity model, and realizing magnetotelluric three-dimensional inversion. The method can achieve the effect of focusing inversion so as to obtain a high-resolution conductivity model with a clear boundary, and serves the fields of petroleum, mineral resource exploration and the like.

The invention relates to the technical field of mineral resource exploration. The prospecting method, device and equipment based on the multi-source geological relation data and the medium are provided, the method comprises the steps that space-time alignment and standardization processing are conducted on obtained geological historical evolution data, geophysical field data, geochemical migration data and remote sensing alteration information, and a dynamic knowledge graph is generated; performing mineralization stage division processing on the geological historical evolution data to obtain a mineralization stage division result; integrating the fluid migration space-time path network, performing three-dimensional dynamic modeling processing, and generating a staged three-dimensional metallogenic evolution model; extracting a stage-specific geological mark combination from the staged three-dimensional metallogenic evolution model, performing knowledge reasoning processing based on a preset historical ore deposit rule base, and outputting an ore prospecting target region and a metallogenic process constraint basis which meet a preset confidence coefficient condition, so as to improve the precision of a structural model, generate a staged fluid dynamic field, and improve the precision of the metallogenic process. And the target region prediction confidence is enhanced.

The invention provides a geological survey optimization method and system based on big data analysis, and relates to the technical field of data processing, and the method comprises the steps: distributing different cost weights for different regions; constructing an initial cost matrix based on geological characteristics, continuously finely adjusting node cost and calculating an evaluation function value, gradually cooling until a final temperature is reached, and outputting an optimized inter-node cost matrix; by initializing the distance and the access array, unaccessed nodes are continuously selected, the distance corresponding to the adjacent nodes is updated until all the nodes are accessed, and finally the corresponding path from the starting point to the target area is output; a mineral resource evaluation model is established based on multi-source geological data, and geological resource distribution of a whole research area is finally predicted by calculating index contribution, screening key areas, evaluating investigation priorities, selecting sampling points and establishing and optimizing a resource prediction model. The geological survey efficiency and accuracy can be improved.

The invention relates to the technical field of mineral resource prediction and analysis, and discloses a regional mineral resource prediction and analysis method and system, and the method comprises the steps: carrying out the collection and standardization, and constructing a standardized physical data set; organizing the physical parameters in a spatial dimension, and constructing a multi-dimensional tensor structure; tensor evolution modeling is carried out to generate tensor time sequence data; obtaining an optimal weight combination according to geological unit division; calculating the mineralization probability of each space unit; evaluating the credibility of the mineralization probability result, and outputting a mineralization probability graph and a credibility scoring graph; the system comprises a data acquisition and standardization module, a tensor modeling module, an evolution modeling module, a feature optimization module, a probability prediction module, a credibility evaluation module and a visual output module. According to the method, a space-parameter joint expression mode based on a four-order tensor structure is introduced, various kinds of physical field data are subjected to integrated processing in a unified space framework, and a data model reflecting multi-dimensional geological response characteristics is effectively constructed.

The invention relates to a geological mineral exploration data extraction method which comprises the following steps: S1, data acquisition: acquiring multi-source geological data through scientific drilling, remote sensing images and a geological radar; according to the method, through scientific drilling, remote sensing image and geological radar multi-source data collection, in combination with data preprocessing, feature extraction and three-dimensional model construction, massive geological data can be rapidly obtained and processed, and accurate positioning of mineral resources is achieved; the remote sensing image is combined to identify surface mineralization abnormity, the problem of surface and deep data separation in a traditional exploration method is effectively solved, meanwhile, the data preprocessing module ensures consistency and high-precision fusion of data from different sources through cleaning, format unification and geographical registration, and the data preprocessing module analyzes abundance and distribution rules of main metallogenic elements to obtain the data of different sources. And identifying combinations of elements closely related to mineralization.

The invention relates to the technical field of mineral resource estimation, and discloses a mineral reserve estimation method based on artificial intelligence, comprising the following steps: step 1, collecting and preprocessing data, acquiring multi-source data, and performing data standardization and feature extraction; 2, carrying out ore body modeling through a 3D convolutional neural network, constructing the 3D convolutional neural network, and carrying out ore body prediction through three-dimensional convolution calculation; step 3, optimizing ore body space prediction in combination with Kriging interpolation, preliminarily predicting ore body space distribution through Kriging interpolation, and further optimizing by using a deep learning model; and step 4, estimating reserves based on Bayesian optimization. The technical scheme of combining multi-source data fusion, a 3D convolutional neural network and Kriging interpolation is adopted, the effect of accurately capturing ore body space distribution under the complex geological condition is achieved, and compared with an existing scheme depending on a traditional geological statistical method, the problem that the fault zone and heterogeneous region modeling capacity is insufficient is solved.

The invention relates to the technical field of unmanned aerial vehicle geological survey, and discloses a path planning method and system for unmanned aerial vehicle geological survey, and the method comprises the steps: obtaining the image, spectrum and topographic data of a remote mountainous area through a multispectral sensor and a laser radar, and recognizing potential vein abnormal points through data fusion and geological feature analysis; and constructing a geological interestingness weight map and planning a flight path. Dense sampling flights are performed in a high interestingness region, high resolution data are processed in real-time to identify key reconnaissance targets, a weight map is updated, and a flight trajectory is dynamically adjusted. Through the closed-loop self-adaptive exploration process, accurate positioning and feature description of the hidden vein are realized, the mineral exploration efficiency is improved, the labor cost and the safety risk are reduced, and an innovative solution is provided for mineral resource exploration in remote areas.

The invention discloses an intelligent planning method of a geological mineral exploration analysis model, and relates to the field of mineral exploration, and the intelligent planning method comprises the following specific steps: S101, obtaining regional geological mineral data through satellite remote sensing, ground survey and historical data collection, and preprocessing the collected geological mineral data; according to the method, powerful prior knowledge support can be provided for simulation learning, exploration planning is more scientific and reliable, mineral resource distribution can be predicted more comprehensively, model generation efficiency and calculation performance are improved, multi-level reasoning and prediction can be carried out on different geological levels, limitation of traditional exploration can be broken through, and the method is suitable for popularization and application. The overall effect of the intelligent planning method is improved; according to the method, global search can be carried out in a large range, local optimum is avoided, meanwhile, the accuracy and reliability of path planning are improved, the diversity and adaptability of path selection are ensured, and a model can more intelligently select a path with high exploration value when facing an unknown area.

The invention belongs to the technical field of mineral resource exploration, and provides a multi-source remote sensing data and AI combined copper ore exploration method and system, and the method comprises the following steps: obtaining multi-source remote sensing data of a target region through a plurality of remote sensing devices; preprocessing the multi-source remote sensing data to obtain fusion data; the fusion data are analyzed through an AI algorithm, and mineralization alteration areas and geological structure information are obtained through recognition; performing overlay analysis on the mineralization alteration area and the known geochemical anomaly area, analyzing the spatial relationship between the mineralization area and the structure in combination with the geological structure information, and judging the favorable mineralization part of the copper ore to obtain an analysis result; performing field investigation on the potential copper ore area determined by the analysis result, collecting rock samples for laboratory analysis, and verifying the accuracy of the analysis result; according to the method, the multi-source remote sensing data and the AI technology are comprehensively utilized, the target area is rapidly analyzed, the potential copper ore area is positioned, the workload of blind exploration is reduced, and the exploration efficiency is improved.

The embodiment of the invention discloses a mineral resource exploration data analysis method and device based on three-dimensional space modeling. The method comprises the steps that back-flushing pushing-covering structure feature information is extracted through structure interface occurrence measurement, lithology combination analysis and structure movement trace recognition and converted into geologic structure feature parameters; stratigraphic age data and lithology distribution information are integrated, and a three-dimensional stratigraphic model is constructed with a stratigraphic superposition relation as a constraint; geophysical data are imported to extract geophysical response characteristics of stratums with different depths, and lithologic boundary correction is combined to obtain deep fracture characteristic parameters; sedimentary system division is carried out on lithofacies paleogeographic information, and deep fracture characteristic parameters are fused to establish a fracture system three-dimensional model; the multi-feature association relationship is identified, the ore body prediction model is established, the spatial data is input to obtain the three-dimensional visual prediction result of the hidden ore body spatial distribution, and the exploration efficiency and accuracy in the deep phosphorite exploration process are improved.

The invention discloses a deep learning prospecting prediction method and system for multi-modal geological data, and relates to the technical field of mineral resource exploration and prediction. The method comprises the following steps: acquiring multi-modal geological data, and preprocessing the multi-modal geological data to obtain preprocessed geological sensing data; constructing a prospecting prediction model based on a hybrid model architecture; the hybrid model architecture comprises a CNN branch, an RNN branch and a GNN branch, and each branch is connected with the full connection layer through splicing operation; inputting the multi-modal geological data into a trained prospecting prediction model for processing to obtain a mineralization potential prediction result; the mineralization potential prediction result comprises a mineralization type, an ore body scale and an ore body grade; and based on the mineralization potential prediction result, constructing a three-dimensional mineralization potential model of the target area by using a depth generation model for visual display. According to the invention, the precision and generalization ability of prospecting prediction can be improved.

The invention relates to a gold mine air-ground-in-well rapid collaborative exploration method, belongs to the field of geological exploration, and aims to solve the technical problems of data splitting, low target area positioning precision, insufficient deep prediction capability and the like in traditional gold mine exploration. By integrating air remote sensing, surface geological survey and underground detection data, a multi-source data standardization processing flow is established, and a progressive exploration system from wide-area screening to accurate positioning is realized by combining geological, geophysical and geochemical comprehensive analysis methods. The system adopts a dynamic feedback mechanism to optimize an exploration process, forms closed-loop analysis and prediction, improves exploration efficiency and accuracy, and reduces resource waste and exploration cost. The method is suitable for gold ore resource exploration under complex geological conditions, has remarkable economic benefits and application value, and can be widely applied to the field of mineral resource development.

The invention discloses a satellite-air-ground-well multi-method collaborative shallow coverage area copper-gold ore exploration method, and relates to the technical field of mineral resource exploration, regional alteration mineral information is extracted in a shallow coverage area, and a porphyry type copper-gold ore prospecting prospective area is determined according to alteration mineral space distribution and zoning characteristics thereof; in the prospecting prospective area, a key exploration area is selected according to metallogenic geological conditions, aviation geophysical prospecting and aviation hyperspectral remote sensing investigation are carried out, and a prospecting favorable area is delineated; performing prospecting prediction on related ore types in the favorable prospecting area, and delineating a prospecting target area; determining a new drilling position in the target area; drilling verification and well logging are carried out, ground-well geophysical prospecting is carried out to describe the spatial form of the metallogenic geologic body, and the prospecting effect is expanded. According to the method, the favorable prospecting area and the prospecting target area can be more accurately delineated, the spatial form of the ore body is finely delineated, a reliable basis is provided for prospecting of the porphyry type copper-gold ore, and the prospecting success rate and efficiency of the porphyry type copper-gold ore in the shallow coverage area are greatly improved.

The invention discloses a brine type potassic salt ore detection method based on a comprehensive geophysical technology, and relates to the field of geophysical detection and strategic mineral resources, the method comprises the following steps: adopting an audio magnetotelluric sounding technology to obtain audio magnetotelluric sounding data; determining a stratum resistivity profile of the audio magnetotelluric sounding line according to the audio magnetotelluric sounding data; acquiring shallow seismic exploration data of the same survey line by adopting a shallow seismic exploration technology; determining a seismic stack section according to the shallow seismic exploration data; the stratum resistivity profile and the seismic stack profile are combined to jointly reveal the stratum structure and construction characteristics of the research area; based on the stratum structure and construction characteristics, according to the low resistance abnormity of the stratum resistivity profile and the strong reflection event characteristics of the seismic stack profile, the sylvite mineralization favorable area and the brine reservoir interval are comprehensively detected, and the brine type sylvite ore identification precision can be improved.

A dynamic adaptive learning method for mineral prediction includes: collecting a dataset including geological data and labels of the geological data; extracting features from the geological data, initializing parameters of a training model and optimizing the parameters to obtain training parameters; performing an associative training on the training model based on the training parameters and the labels in a dynamic adaptive learning framework to obtain a mineral prediction model, algorithms of the associative training including a variational expectation algorithm and a variational maximization algorithm, and the variational expectation algorithm including an unsupervised learning mode, a semi-supervised learning mode, and a fully supervised learning mode; and predicting, by using the mineral prediction model, a mineral to obtain a mineral prediction result. The method can break through limitations of the traditional machine learning technology, offering a more efficient, universal, and stable strategy for geophysical data analysis and mineral resource assessment.

The invention discloses a multi-source remote sensing rare earth ore prospecting method and system fused with pteridophyte ore showing, and belongs to the technical field of mineral resource exploration. Weathering crust type rare earth ore mineralization factor remote sensing features are obtained through multi-source remote sensing data, a potential area is preliminarily delineated, an unmanned aerial vehicle carrying a VNIR camera is deployed to obtain a high-resolution image, and the high-resolution image is subjected to image mapping; and comprehensively extracting fern distribution information according to texture, form and spectral indexes, and down-sampling a fern coverage result to a resolution consistent with that of other features to form a fern coverage layer. And constructing a multi-source remote sensing matrix fusing the fiddlehead coverage map layer and the mineralization factor characteristics, and predicting the rare earth mineralization probability and optimizing the potential region selection through sample training and global reasoning. According to the method, the positioning precision and exploration efficiency of the weathering crust rare earth ore are remarkably improved through coupling of the biological indicator factor (pteridophyte) and the traditional rock-mineral-structure-landform ore showing factor.

The invention provides a layered multi-agent-based full-process intelligent decision-making method and system for mineral development. The method comprises the following steps: constructing a three-level intelligent decision-making main body-based full-process layered collaborative decision-making architecture for mineral development; designing a communication protocol; and establishing a quantitative coupling relationship between the cross-link coupling constraint and the process parameters. And constructing a state space and an action space of the three-level intelligent decision-making main body. Designing a global objective function, a link objective function and a key parameter collaborative optimization model; and establishing an adaptive weight optimization mechanism. And designing a distributed collaborative optimization mechanism based on an alternating direction multiplier method. And implementing an agent collaborative decision-making process of the evolutionary game. The selected decision scheme is converted into a specific production instruction to be issued and executed, the execution effect and feedback data are monitored in real time, and the strategy is dynamically adjusted and optimized. According to the invention, the multi-objective collaborative optimization of the full life cycle of mineral development is realized, and the intelligent, green and sustainable development of mineral resource development is promoted.

The invention relates to a dynamic detection method, system, equipment and medium for a blind gold mine in a coverage area, and belongs to the technical field of mineral resource exploration, the method comprises the following steps: collecting historical geochemical exploration data and geographic coordinate data of a target coverage area, and screening a target test profile based on drilling mine finding position and coverage layer thickness information; collecting soil shallow gas data on the test section and performing anomaly analysis to generate a gas anomaly space coordinate set; taking an area marked by the gas anomaly space coordinate set as a preset sampling point, acquiring soil sample attribute data of the preset sampling point, inputting the soil sample attribute data into a pre-configured granularity-element correlation model, and outputting a coverage type judgment label; and calling a corresponding geochemical method combination from a preset method library, generating an execution instruction list, carrying out element abnormal data acquisition and spatial overlay analysis on the target coverage area, and generating an ore body prediction probability distribution diagram and a drill hole layout coordinate list. The accuracy of ore body positioning can be improved.

The invention relates to the technical field of geological image processing, and particularly discloses a three-dimensional modeling method for a geological section, which is used for solving the problem of how to accurately identify and hide irrelevant geological areas and improve the modeling efficiency in geological three-dimensional modeling. Comprising the steps of data acquisition and preprocessing, geologic model constraint, implicit modeling and machine learning optimization, three-dimensional modeling and graphic rendering optimization, model correction and optimization and dynamic interaction and analysis, and four analysis formulas based on hidden requirements are provided; according to the method, by setting the hiding demand judgment formula for the coal seam, the rock stratum, the mined area and the inactive fault, the geological area irrelevant to target research is screened, the geological three-dimensional modeling process is optimized, the accuracy and efficiency of the model are improved, interference of irrelevant data is reduced, and the method has good application prospects. It is ensured that the mineral resource evaluation result better conforms to the actual geological condition, and the precision and credibility of mineral resource prediction are effectively improved.

The invention provides a design optimization method of a deep sea mineral resource set ore head based on a coanda effect. The method comprises the following steps: determining acquisition target parameters; constructing a particle size stress model; determining initial parameters of a deep sea mineral resource collection head; according to the design optimization method, the deep sea mineral resource collecting head is designed according to actual mining requirements and mineral characteristics, and the collecting effect of the deep sea mineral resource collecting head is improved.

The invention relates to the technical field of metallogenic gene analysis, in particular to a metallogenic gene analysis method and system based on an expert knowledge base. The method comprises the following steps: acquiring authoritative theory information, analyzing the authoritative theory information, and determining a metallogenic gene feature information set; according to the metallogenic gene feature information set, carrying out directivity correlation analysis on the metallogenic genes, and determining a directivity node set and a target node set; constructing a metallogenic gene structure information base according to the pointing node set and the target node set; acquiring a mining area exploration data set, analyzing the mining area exploration data set based on the metallogenic gene structure information base, and determining an effective node information set; and based on the metallogenic gene structure information base, analyzing the effective node information set and the mining area exploration data set, and determining and outputting a metallogenic state analysis report. According to the method, the user can better perform resource management and decision making, sustainable mineral resource development and utilization are ensured, and scientificity and economical efficiency of mineral exploration are greatly improved.

The invention relates to a flotation process automatic control method for intelligently optimizing a reinforcement learning algorithm. In the mineral flotation operation process, a flotation reagent system needs to be precisely controlled, for example, the dosage of reagents such as a collecting agent, an inhibitor and a foaming agent is adjusted, or process reaction parameters such as the pH value of ore pulp, the flotation temperature and the reagent reaction time are adjusted, so that the flotation index reaches the best, and maximum utilization of mineral resources is achieved. Main equipment for adding flotation reagents is a variable frequency pump, the reagent amount is adjusted through PID, meanwhile, process index data of a flotation tank are collected through a sensor, the whole control algorithm is optimized through improved FPA reinforcement learning, flotation process index data parameters serve as environment state observation values of reinforcement learning intelligent agents, and the flotation process index data parameters are calculated. Meanwhile, the design environment is rewarded according to the conditions meeting the flotation requirements, and the intelligent agent is trained through repeated environment interaction. And finally, during operation, the intelligent agent continuously gives an optimal action value parameter of a flotation cell actuator in real time, and unmanned reinforcement learning optimization control of mineral flotation is achieved.

The invention discloses a land sedimentary basin salt water layer carbon dioxide geological storage site selection method comprising the following steps: carrying out geological exploration in a to-be-stored area, and delineating a storage prospective area; geological exploration is carried out in the sequestration distant view area, a trap structure and geological conditions beneficial to carbon dioxide sequestration are found, and a sequestration target area is delineated through pre-exploration well drilling in combination with important mineral resource exploration development conditions and carbon dioxide sequestration and mineral resource development configuration; geological exploration is carried out in a storage target area, supplementary drilling is carried out under the conditions of shaft corrosion prevention and leakage, carbon dioxide storage and mineral resource development are configured to avoid the risk of carbon dioxide leakage caused by mineral resource development and abandoned drilling by combining important mineral resource exploration and development conditions, and a storage site is delineated; determining a carbon dioxide injection layer; and sealing site selection is completed. According to the method, a proper sealing site can be screened for large-scale safe carbon dioxide sealing for the salt water layer of the sedimentary basin, and the sealing effect is ensured.