167 results about "Chemical data" patented technology

The invention discloses an Internet of Things intelligent monitoring alarm method and system for water quality detection, and relates to the technical field of environmental protection monitoring, and the method comprises the steps: respectively sampling water quality electrochemical data for corresponding monitoring water area blocks based on each Internet of Things node; extracting electrochemical time sequence sensing characteristics respectively corresponding to each piece of water quality electrochemical data; extracting mode time sequence dependence features corresponding to the electrochemical time sequence sensing features based on a water quality anomaly identification model to predict water quality anomaly risks; identifying a corresponding water quality abnormity risk event type based on an abnormal event identification model; and according to the block identifier of each risk monitoring water area block and the corresponding water quality abnormity risk event type, executing water area water quality pollution alarm operation. Therefore, based on distributed water quality monitoring, intelligent risk identification and water quality pollution early warning operation of the Internet of Things nodes, water quality monitoring real-time performance, comprehensive coverage and intelligent management capability are remarkably improved.

A computer implemented method may collect a set of known drug compounds from a database, and create augmented datasets with labeled and unlabeled chemical data and biological data. A computer implemented method may train a neural network using the expanded training set to generate a prediction score of bioactivity of candidate drug compounds towards the biological target, wherein the contribution of the second plurality of unlabeled samples in training the neural network is scaled by a parameter. A computer implemented method may output one or more refined neural network models capable of generating candidate drug compounds with predicted activity. A computer implemented method may generate a candidate drug compound by inputting a candidate drug compound with chemical data into the trained neural network model.

The invention relates to the technical field of material determination, and particularly discloses an intelligent screening method and system for traditional Chinese medicine decoction pieces, and the method comprises the steps: S1, obtaining decoction piece standard data of to-be-detected traditional Chinese medicine decoction pieces, collecting decoction piece physical data and decoction piece chemical data of the to-be-detected traditional Chinese medicine decoction pieces, and obtaining historical detection data of the to-be-detected traditional Chinese medicine decoction pieces; s2, calculating a feature deviation value of each feature in the physical feature vector of the to-be-detected traditional Chinese medicine decoction piece and a physical deviation value of the to-be-detected traditional Chinese medicine decoction piece; s3, constructing a chemical detection model, and determining chemical deviation data of the to-be-detected traditional Chinese medicine decoction pieces; and S4, determining the traditional Chinese medicine decoction piece grade of the to-be-detected traditional Chinese medicine decoction pieces and performing intelligent screening. According to the method, a comprehensive and accurate decoction piece standard system can be constructed, multi-dimensional and quantitative evaluation of the quality of the decoction pieces is realized, the grade of the decoction pieces is comprehensively and accurately judged, subjective errors and quality misjudgment are avoided, the screening efficiency and accuracy are improved, and a scientific solution is provided for quality control of traditional Chinese medicines.

The embodiment of the invention discloses a dangerous chemical identification method, and the method comprises the steps: obtaining chemical information data, and constructing a dangerous chemical semantic database according to the chemical information data through employing a deep learning multi-modal data fusion algorithm; according to naming rules and attributes of chemicals, defining text variables and numerical variables, extracting hazardous chemical judgment conditions from the chemical information data, and establishing a hazardous chemical rule base according to the text variables, the numerical variables and the hazardous chemical judgment conditions; according to the chemical information data, establishing a hazardous chemical data model based on a graph neural network technology; constructing a hazardous chemical substance identification system by using the hazardous chemical substance semantic library, the hazardous chemical substance rule library and the hazardous chemical substance data model; and obtaining to-be-classified chemical information, and classifying to-be-classified chemicals in the to-be-classified chemical information by using a hazardous chemical identification system to obtain a hazardous chemical judgment result. According to the invention, more intelligent and accurate dangerous chemical identification is realized.

A computer implemented method may collect a set of known drug compounds from a database, and create augmented datasets with labeled and unlabeled chemical data and biological data. A computer implemented method may train a neural network using the expanded training set to generate a prediction score of bioactivity of candidate drug compounds towards the biological target, wherein the contribution of the second plurality of unlabeled samples in training the neural network is scaled by a parameter. A computer implemented method may output one or more refined neural network models capable of generating candidate drug compounds with predicted activity. A computer implemented method may generate a candidate drug compound by inputting a candidate drug compound with chemical data into the trained neural network model.

The invention discloses a microwave and radio frequency integrated treatment system which comprises a multi-source information acquisition module, a feature extraction module, a feature modeling module and an instruction generation module. The multi-source information acquisition module acquires internal temperature distribution data and contact impedance spectroscopy data of a focus through a sensor array, and forms focus materialization data in combination with focus space information; the feature extraction module performs space-time registration on the focus materialization data by using a space-time registration algorithm to obtain focus comprehensive thermal impedance features; the feature modeling module extracts space-time evolution features of the comprehensive thermal impedance features of the focus by using a lightweight convolutional neural network, and constructs a thermal impedance dynamic expansion model; the instruction generation module generates an initial control instruction sequence used for controlling the microwave and radio frequency integrated treatment equipment based on the thermal impedance dynamic expansion model in combination with the treatment scheme.

The invention discloses a method and a system for estimating the content of key metal elements of a crusting based on a hyperspectrum, and belongs to the technical field of deep sea mineral detection. The method comprises the following steps: selecting a crusting sample, obtaining hyperspectral data and chemical data of the crusting sample, establishing a point selection data set at a corresponding position, deleting abnormal sample points, and calculating the content of the key metal elements of the crusting sample; the method comprises the following steps: selecting a characteristic wave band of a crusting sample through preprocessing and CARS to obtain a characteristic wave band data set; and based on an MLP neural network model, performing model training through the data set, and constructing an estimation model for estimating the grade of the crusting sample. Compared with the prior art, the method has high accuracy and high predictive capacity, by inverting chemical data of the whole crusting section and applying the scheme for estimating the content of the key metal elements of the crusting, nondestructive grade and abundance analysis of the crusting sample can be achieved, and a reliable scientific basis is provided for seabed detection and multi-metal mineral exploitation.

The invention provides a geological surveying and mapping information extraction method and system based on multi-source data fusion, and relates to the technical field of data processing, and the method comprises the steps: collecting multi-source geological data, including satellite remote sensing data, geological exploration data, geophysical data, geochemical data and ground actual measurement data; preprocessing the multi-source geological data to obtain preprocessed multi-source geological data; based on the amount of information provided by each data source, the frequency of data updating and the accuracy of historical data, calculating a dynamic adjustment factor of each data source; fusing the preprocessed multi-source geological data by using a dynamic adjustment factor to obtain fused geological data; based on the fused geological data, extracting target geological surveying and mapping information by using a geological information extraction algorithm; and performing information screening on the extracted target geological surveying and mapping information to form a final geological surveying and mapping information extraction result. According to the invention, the precision and efficiency of data fusion can be improved.

The invention is suitable for the technical field of resource exploration, and provides a gold mine target region optimization method and device based on element geochemical anomalies, and the method comprises the steps: obtaining the geochemical sample data of a plurality of sampling points in a research region; training the converter model by using the geochemical sample data to obtain a trained converter model; initializing model parameters of the trained converter model to obtain a student model; performing self-distillation training on the student model by using the geochemical sample data to obtain a geochemical data reconstruction model; inputting the geochemical sample data of the sampling points into a geochemical data reconstruction model for processing to obtain reconstructed geochemical data of the sampling points; and calculating a geochemical anomaly score of each sampling point based on the reconstructed geochemical data of each sampling point, and determining a gold mine target area in the research area based on the geochemical anomaly scores of the plurality of sampling points. The method can meet the requirement of accurately and optimally selecting the target region in gold mine exploration.

The invention discloses a safety evaluation method, system, equipment and program for a chemical storage place, and relates to the technical field of dangerous chemical management, and the method comprises the steps: carrying out the time-space alignment of multi-source data; wherein the multi-source data comprises stored chemical data, environment data, equipment state data, management data and historical data; extracting spatial features of the multi-source data after space-time alignment, and generating a spatial risk weight map; pre-training a risk evaluation model; and mapping the weight in the spatial risk weight map to the risk evaluation model to obtain a risk level. Through the treatment scheme disclosed by the invention, potential risks of the storage place can be found in time, early warning can be performed in advance, emergency response can be started, the possibility of accidents can be effectively reduced, and the safety of the chemical storage place can be improved.

The invention relates to the technical field of chemical data processing, and provides a retrieval enhancement generation method and system oriented to chemical corpus data. The method comprises the steps that a special lexicon and a general lexicon for chemical knowledge are built, comprehensive and accurate chemical term resources are provided for a large model, and the large model and the RAG technology can better extract and understand the terminologies of the chemical industry; by designing a chemical engineering knowledge weighted graph, all entity words and text blocks in the graph are endowed with different weights, and each knowledge element can be paid attention to different degrees according to the weights in the retrieval process; a dynamic self-adaptive mechanism is introduced, the content of the chemical knowledge special lexicon can be dynamically enriched according to the updating and changing conditions of the knowledge in the chemical field, new chemical terms are contained in the chemical knowledge special lexicon in time, it is ensured that the large model can rapidly learn and master the latest knowledge, and the application performance and effect of the large model in the chemical field are remarkably improved.

The invention relates to an automatic intelligent script system for material simulation calculation. The automatic intelligent script system comprises a main program module; the comparison module is used for reading the configuration file script and determining that the calculation parameter file meeting the operation requirement of the target material calculation software exists in the calculation parameter file set based on the molecular structure file; the input module is used for acquiring calculation parameters and generating a first input parameter file used for geometric structure optimization simulation calculation and a second input parameter file used for thermal activation molecular dynamics simulation calculation on the basis of the input template file; and the calculation module is used for calling target material calculation software to sequentially execute geometric structure optimization simulation calculation and thermal activation molecular dynamics simulation calculation on the molecular structure of the molecular structure file at least based on the first input parameter file and the second input parameter file to obtain a calculation result. The script system can improve the calculation efficiency and reduce the error rate, and can be used for material simulation calculation and provide rich chemical data resources for artificial intelligence.

The invention discloses a grounding grid corrosion rate prediction method, medium and system, and the method comprises the steps: training a neural network model through employing a first grounding grid corrosion rate and first grounding grid soil physical and chemical data, and obtaining a grounding grid corrosion rate prediction inversion model; inputting the second grounding grid corrosion rate into the grounding grid corrosion rate prediction inversion model, and outputting second grounding grid soil physical and chemical data; combining the first grounding grid soil physical and chemical data and the second grounding grid soil physical and chemical data to obtain third grounding grid soil physical and chemical data, and combining the first grounding grid corrosion rate and the second grounding grid corrosion rate to obtain a third grounding grid corrosion rate; training a neural network model by adopting the third grounding grid soil physicochemical data and the third grounding grid corrosion rate, and optimizing a weight and a threshold value through a simulated annealing algorithm to obtain a grounding grid corrosion rate prediction model; and inputting actually acquired soil physical and chemical data into the grounding grid corrosion rate prediction model, and outputting the grounding grid corrosion rate. The method is high in prediction accuracy.

The invention relates to the technical field of drug release control. By providing a multi-modal data fusion nano-drug release kinetics prediction method, device and equipment and a medium, the method comprises the following steps: acquiring permeation path image data of a nano-drug in a target tissue, and acquiring drug release rate chemical data; performing time dimension synchronization processing on the permeation path image data and the drug release rate chemical data to generate a multi-modal space-time correlation data set; performing feature extraction processing on the multi-modal time-space correlation data set to obtain nano-drug permeation behavior features and release dynamics fluctuation features; constructing a space-time diagram neural network prediction model; training the space-time diagram neural network prediction model to obtain a trained prediction model; and generating a personalized drug release scheme through a reverse optimization algorithm on the basis of the drug release kinetics prediction curve and the quantitative index of the degree of variability, so as to achieve the technical effects of improving the prediction precision, enhancing the model adaptability and reducing the curative effect volatility.

The invention discloses a mining area heavy metal monitoring method and system based on spatio-temporal evolution, relates to the technical field of environmental pollution monitoring, and solves the technical problem of inaccurate monitoring result caused by incomplete data and single analysis method in the prior art. According to the method, time sequence hyperspectral data and time sequence geochemical data of a target area are collected, space-time alignment is carried out, and space-time consistency of the data is ensured; the hyperspectral data and the geochemical data are processed through a first model and a second model which are constructed based on an artificial intelligence model, a heavy metal inversion result and a prediction result are obtained, the weight of the heavy metal inversion result and the weight of the prediction result are dynamically adjusted through a weight adjustment function set in a third model, and a heavy metal monitoring result is comprehensively obtained; according to the method, the reliability and correlation of different data sources are fully considered, the actual concentration and distribution condition of the heavy metals in the mining area can be reflected more accurately, and a reliable basis is provided for environmental governance and risk assessment of the mining area.

The invention relates to a material science literature knowledge reconstruction method based on a large language model. At present, research focuses on extraction of specific chemical components from literatures and synthesis of detail or attribute information, and the information is scattered and incomplete. The method for extracting the synthetic program is also lack of flexibility and universality, so that the application and the expansibility of the method are limited. According to the method, the large language model represented by GPT-4 shows excellent ability in extracting information from the literature and understanding and generating chemical data, and is used for accurately extracting a complex synthetic route in the material literature. The method not only can extract information of a precursor, raw materials, a synthesis method, a product, synthesis steps and synthesis conditions in the inorganic material, but also can comprehensively reflect the logic relationship of the synthesis process. And finally, storing and utilizing structured JSON (JavaScript Object Notation) data in a synthetic route, so as to help researchers to obtain valuable information and insights.

A method involving collecting a first geochemical data set for a first plurality of produced water samples; collecting a second plurality of produced water samples; performing geochemical analyses on the second plurality of produced water samples to form a second geochemical data set; and combining the first and second geochemical data sets into a database. The method further includes determining, by a subject matter expert, a water type for each produced water sample in the database and training a machine-learned model with the database to predict the water type of a produced water sample given its geochemical data. The method further includes collecting a third plurality of produced water samples, performing geochemical analysis on the third plurality of produced water samples, and determining, with the trained machine-learned model, the water type for each produced water sample in the third plurality of produced water samples using the third geochemical data set.

The invention discloses a rare metal mineralization potential comprehensive evaluation method based on rock geochemistry, and aims to solve the problems of single evaluation index and disjunction between field observation and indoor analysis in the prior art. The method comprises the following steps: obtaining geological data of an analysis area, and screening a target area based on a favorable structure background and an exposed feature criterion; identifying mineralogical and lithofacies markers and collecting sampling data; the method comprises the following steps: acquiring rock geochemical data of a sample sampled by a previous person, including multi-dimensional data including major elements, trace elements, isotope composition and the like, and calculating a quantitative evaluation index according to the data; and finally, establishing a comprehensive evaluation model by adopting a weighted scoring method to realize mineralization potential grade division and target region accurate delineation in a three-dimensional space. According to the method, organic combination of macroscopic geologic features and microcosmic geochemical data is realized, and a complete technical system from field observation to indoor analysis to engineering decision is formed.

The invention belongs to the technical field of gold mineralization data processing, and particularly relates to a gold mineralization data processing method based on a metric learning enhanced variational auto-encoder, and the method comprises the steps: carrying out the standardization processing of geochemical multi-element original data, and screening out feature vectors of which the contribution degrees to anomaly recognition exceed a set threshold value, and forming a training set; training a metric learning enhanced variational auto-encoder model comprising an encoder, a decoder and a metric constraint module by using the training set; a trained metric learning enhanced variational auto-encoder model is applied to actual geochemical data, geochemical sample points with obvious abnormal features are identified by calculating reconstruction errors of samples in the actual geochemical data and category distances of potential spaces, complex geochemical element distribution can be described more accurately, and the accuracy of geochemical element distribution is improved. And the sensitivity and the recognition capability of the subtle anomaly are improved.

The invention discloses application of biochar in saline-alkali soil improvement and crop quality and yield improvement, and relates to the technical field of biochar soil improvement and crop yield and quality improvement. Comprising a biochar saline-alkali soil improvement and crop quality-improving and yield-increasing system which is used for acquiring degradation weight data of biochar and physical and chemical data of saline-alkali soil, acquiring correlation between saline-alkali soil data and biochar degradation degree by using a Pearson correlation coefficient, calculating the biochar degradation degree, constructing a biochar degradation monitoring model, and monitoring the quality of the saline-alkali soil in the biochar degradation monitoring model. According to the method, the soil bulk density measurement technology, the drying acquisition technology and the modern information technology are combined, the soil bulk density measurement technology and the drying acquisition technology are combined, and the soil bulk density measurement and the drying acquisition technology are combined; the consumption of biochar in saline-alkali soil at the interval of 24 hours and the soil bulk density, the water content, the pH value and the organic carbon content of the saline-alkali soil in a saline-alkali soil test area at the interval of 24 hours are accurately captured, and physical data and chemical data of the saline-alkali soil are monitored in real time.

The invention discloses a chemical data processing analysis method and system for chemical production, and relates to the technical field of data processing. The method comprises the following steps: collecting chemical variables in chemical production in real time; determining a first window step length at the first moment based on the overall change condition of the chemical variables in each data window within a first time period before the first moment; and traversing the chemical variables based on the first window step length and a preset window length to obtain a next data window, and returning to circularly execute the process of determining the first window step length at the first moment based on the overall change condition of the chemical variables in each data window within a first time period before the first moment until the chemical abnormal condition is met, obtaining a plurality of data windows; and on the basis of the local change condition of each chemical variable in each data window in a second time period before the chemical abnormal moment, the abnormal change consistency of each chemical variable and chemical production and the abnormal change control degree of each chemical variable on the chemical production are respectively determined.

The invention relates to a dangerous chemical accident named entity identification method based on deep learning. The method comprises the following steps: firstly, acquiring dangerous chemical accident data, preprocessing the dangerous chemical accident data, and further constructing a dangerous chemical data set; based on the hazardous chemical substance data set, constructing a hazardous chemical substance electronic dictionary and a hazardous chemical substance entity corpus; on the basis, an ERNIE-BiGRU-Multi-HeadAttention-CRF deep network model is constructed, and named entity recognition is carried out on the accident description text; meanwhile, performing entity alignment on the accident description text according to a dangerous chemical entity corpus to obtain an identification result; and finally, a result obtained through deep learning model recognition and an entity corpus alignment result are fused to obtain a final entity recognition result. The dangerous chemical accident named entity recognition method has the advantages that the accuracy of dangerous chemical accident named entity recognition can be improved, researchers are helped to deeply analyze accident occurrence reasons and development laws to attract accident training, and reference is provided for follow-up handling and early warning work of dangerous chemical accidents.

The invention relates to the technical field of textile engineering, and discloses an automatic high-precision collaborative degumming system for mulberry silk fabrics, which comprises the following modules: an automatic control module for monitoring and precisely controlling the degumming process in real time; the collaborative operation module is used for coordinating work between each device and the station and is responsible for collaborative operation of multiple devices; the data acquisition and sensing module is used for acquiring and transmitting various physical and chemical data in the mulberry silk fabric degumming process; the data analysis and optimization module is used for optimizing a degumming process in real time through a big data technology and a machine learning algorithm; the intelligent feedback and early warning module is used for monitoring the running state in real time and giving out early warning and automatically adjusting when the running state is abnormal; and the user management and interface module is used for interaction of operators. According to the method, big data analysis and a machine learning algorithm are combined, the degumming parameters can be automatically adjusted according to different characteristics of the mulberry silk fabric, the temperature and humidity, the pH value and the degumming time are optimized, and the degumming process is more accurate.

The invention relates to the technical field of remote sensing prospecting, and discloses a uranium-copper multi-metal remote sensing prospecting method which comprises the following steps: S1, data acquisition and preprocessing; s2, alteration information is extracted; s3, multi-source information comprehensive analysis and prospecting prediction; s4, alteration abnormity verification is carried out; and S5, delineating the prospecting target area. According to the method, vegetation spectrum interference is stripped by carrying out principal component analysis on visible light-short wave infrared of a remote sensing image, weak iron staining and hydroxyl alteration signals are extracted, a geological and remote sensing coupling technology is carried out, alteration anomaly is superposed with a geological map, geochemical data and a construction interpretation result, a comprehensive remote sensing anomaly map is obtained, and the comprehensive remote sensing anomaly map is obtained. According to the method, the multi-parameter grading model GGS is established, and the target region is graded based on the multi-parameter grading model GGS, so that the probability of missed judgment of the target region is reduced.

The invention discloses an energy engineering digital intelligent platform control method and system based on multi-source data fusion, and particularly relates to the field of data analysis, comprising data entry, data cleaning and preprocessing, comprehensive evaluation, collaborative analysis, trend analysis and control. The coal chemical industry data are comprehensively recorded, cleaned and preprocessed, the comprehensive evaluation model is established, accurate monitoring and management of the production process are achieved, the overall performance and stability of a production system are improved through collaborative analysis, and prospective guidance is provided for future production through trend analysis. Finally, the control system can give out risk early warning according to real-time data, production safety is ensured, the production process can be optimized, production efficiency can be improved, production cost and risks can be reduced, and powerful support is provided for energy engineering digital-intelligent transformation.

The invention relates to the technical field of soil detection, in particular to a soil salinity and alkalinity degree detection method and a soil salinity and alkalinity degree detection system applied to anti-corrosion operation of a grounding grid. Acquiring at least two groups of soil physicochemical data information and at least two groups of environment state data information of the area where the grounding grid is located; performing multi-channel data feature processing on the at least two groups of soil physical and chemical data information to obtain a first soil corrosion influence feature set; performing fluctuation analysis on the at least two groups of environment state data information to obtain a correction factor set; performing feature correction on the first soil corrosion influence feature set based on the correction factor set to obtain a second soil corrosion influence feature set; inputting the second soil corrosion influence feature set into a preset soil corrosion prediction model to obtain a soil corrosion degree index; various factors are comprehensively considered, and the accuracy of corrosion prediction and the effectiveness of anti-corrosion measures are improved.

The embodiment of the invention discloses a hazardous chemical substance data sharing supervision method and system based on an alliance block chain. A specific implementation mode of the method comprises a data uploading step, and the data uploading step comprises the steps that entities of all links in the life cycle of the hazardous chemical substances collect data of the corresponding links through Internet of Things equipment and store the data into a management system of the entities; submitting supervision data in the corresponding link data to an alliance block chain node corresponding to a supervisor by using a management system of the entity and an interface provided by the supervisor; the supervisor audits the received supervision data, and writes the supervision data into a block chain account book if the supervision data pass the auditing; based on the received supervision data of each link, generating life cycle data of the hazardous chemical substance; and based on the life cycle data, carrying out full life cycle monitoring on the hazardous chemical substance.

The invention relates to the technical field of electromagnetic detection, in particular to a fabric chemical residue detection method and system and a medium. The method comprises the following steps: acquiring fabric production sensing data and performing fabric chemical processing information integration to obtain fabric processing chemical data; obtaining electromagnetic detection data of the fabric and screening abnormal electromagnetic modes to obtain abnormal electromagnetic reflection mode data; performing fabric residual fingerprint matching on the abnormal electromagnetic reflection mode data based on fabric processing chemical data to obtain fabric residual fingerprint matching data; constructing a chemical residue prediction model according to the fabric processing chemical data and the fabric residue fingerprint matching data; and acquiring real-time production fabric data, and performing fabric chemical residue risk assessment on the real-time production fabric data through the chemical residue prediction model so as to obtain a real-time production fabric chemical residue risk assessment report. The method can effectively identify low-concentration residues.

The invention belongs to the technical field of soil ecological risk monitoring, and discloses a soil ecological risk assessment method and system based on multi-source data analysis. The system comprises a data acquisition module, a data preprocessing module, a comprehensive data analysis module, a database module, a risk dynamic early warning module and a decision report output module, the data preprocessing module is used for preprocessing a chemical data set to obtain a second chemical data set; the comprehensive data analysis module is used for processing the second chemical data set to obtain a risk prediction value; the risk dynamic early warning module is used for analyzing the risk prediction value and outputting a risk decision report; the decision report output module is used for processing and outputting the risk decision report; in general, the method has the remarkable advantages of being high in data prediction accuracy, good in auxiliary evaluation capability and high in risk decision-making practicability.

This application is applicable to the field of resource exploration technology, and provides a method and device for preferentially selecting gold ore target areas based on geochemical anomalies of elements. The method includes: obtaining geochemical sample data of multiple sampling points in the research area; training a converter model using the geochemical sample data to obtain a trained converter model; initializing the model parameters of the trained converter model to obtain a student model; performing self-distillation training on the student model using the geochemical sample data to obtain a geochemical data reconstruction model; inputting the geochemical sample data of the sampling points into the geochemical data reconstruction model for processing to obtain the reconstructed geochemical data of the sampling points; calculating the geochemical anomaly scores of each sampling point based on the reconstructed geochemical data of each sampling point, and determining the gold ore target areas in the research area based on the geochemical anomaly scores of multiple sampling points. This application can meet the need for accurately preferentially selecting target areas in gold ore exploration.