372 results about "Research data" patented technology

A scheduling method and system for the operation of reservoirs to recharge freshwater for repelling saltwater intrusion under changing conditions. Research data of a research area is collected, the research area is generalized, and the river network topology of the area is constructed; the research data acquire the seawater flow data of each station in the research area for N time periods, analyze the correlation between the flow of a predetermined station and the saltwater intrusion and build a seawater flow spatial-temporal evolution model; rainfall data and freshwater runoff of each station are acquired to analyze the variation trends of freshwater flow and hydrological regime of each station and a first ending point and build a freshwater flow spatial-temporal evolution model; and a scheduling model for recharging freshwater for repelling saltwater intrusion is built for simulation, a scheduling method is provided, and a scheduling scheme set is formed.

The invention provides a photovoltaic active distribution network voltage out-of-limit problem tracing method comprising the following steps: receiving research data and carrying out multi-scale feature analysis to obtain a standardized multi-dimensional feature matrix and a time-space incidence matrix; constructing an initial causal graph based on basic research data and power grid physical constraints, and constructing a multi-layer causal network by calculating causal intensity; extracting a dynamic feature sequence, analyzing propagation features, and generating a propagation path set and a path weight matrix; performing tensor completion and state estimation on the research data to obtain a state estimation matrix and estimation uncertainty; and outputting an out-of-limit source position, an out-of-limit risk score and early warning information based on pattern matching analysis and risk assessment. According to the invention, through combination of multi-dimensional feature analysis and a multi-layer causal network, accurate positioning and risk early warning of a voltage out-of-limit source are realized, and traceability accuracy and analysis efficiency are improved.

The invention relates to the technical field of medical data management, in particular to a medical data document classification and marking system which comprises a document structure analysis module, a semantic information identification module, a classification and marking module and a document intelligent label module. According to the method, document visualization and retrieval efficiency is improved by analyzing document content and extracting titles, texts and appendixes, analytic performance and operability of data are enhanced by identifying key information and semantic relations and accurately extracting and labeling medical terms, medicine research data and medical record records, storage is optimized according to priorities in combination with classification and marking, and the method is high in practicability and easy to popularize and use. Clinical values and application scenes of documents are clarified, document management processes are refined, management compatible with clinical data formats is formed through structured storage of marked data and classification label generation, storage normalization and retrieval efficiency are improved, the requirement for accurate information calling in clinical tests is met, document management capacity is improved, and the document management efficiency is improved. And support is provided for improvement of clinical decision-making and research efficiency.

The invention discloses a multi-agent collaborative data authorization operation management method and system, and the method comprises the steps: carrying out the edge grading, edge verification and desensitization of scientific research data, obtaining desensitized shared data, encrypting and transmitting the desensitized shared data to a cloud sharing platform, carrying out the layering encryption, and generating an uploading log, the method comprises the following steps: generating an encrypted request packet, screening data, providing the data for a research and development institution, generating an intelligent contract, carrying out dynamic authorization to generate an authorization instruction and an authorization log, verifying the authorization instruction, opening a data channel permission to determine visited data, carrying out hierarchical decryption on the visited data to obtain collaborative authorization data, and sending the collaborative authorization data to the research and development institution. And carrying out weight sorting on the collaborative authorization data, accessing and downloading the collaborative authorization data, and updating an access log. The method not only can improve the efficiency and accuracy of scientific research data authorization operation management, but also has good interpretability, and can be directly applied to a scientific research data authorization operation management system.

The invention relates to the technical field of medical data analysis and processing, in particular to a medical scientific research auxiliary method and system based on a large model, a terminal and a medium, and the method comprises the steps: extracting theme meanings based on a natural language processing technology, sending retrieval requests to a plurality of medical literature databases based on the theme meanings, and returning retrieved related literatures; performing feature extraction on the related literatures, classifying the related literatures, and generating a literature review; determining an experiment type based on related literatures and medical knowledge maps in combination with the research problem and an expected target, determining an experiment process and an input demand, obtaining a precise search condition in combination with the input demand, and obtaining corresponding research data based on the precise search condition; a corresponding analysis method is selected for analysis processing, and an analysis result is obtained; and generating a preliminary framework of the thesis based on research data, analysis results and literature review in combination with a standard structure template. The efficiency and quality of medical scientific research are effectively improved, the cost is reduced, and the intelligence and systematicness of scientific research are enhanced.

The invention discloses a project performance evaluation and risk early warning method for multi-source heterogeneous scientific research data, and relates to the technical field of scientific research project management, and the method comprises the steps: collecting the multi-source heterogeneous scientific research data, cleaning, converting, filling missing values, and storing in a unified format; extracting performance and risk features, and fusing to obtain a fused feature set; combining an analytic hierarchy process, an entropy weight method and the like to construct a dynamic weight performance evaluation model; constructing a multi-modal risk early warning model by adopting multiple algorithms and optimizing a threshold value; collecting data in real time to update a feature set, and dynamically adjusting the model; and visually outputting a result, generating an improvement suggestion, and forming closed-loop feedback. According to the method, the multi-source data processing efficiency and evaluation accuracy are improved, the risk early warning timeliness and adaptability are enhanced, a management closed loop is formed, and the problems of difficult data integration, evaluation lagging, insufficient early warning and the like of a traditional method are solved.

The invention provides a data mining method and device, equipment and a storage medium, and the method comprises the steps: carrying out the data preprocessing of multi-source heterogeneous scientific research data, generating a data set in a unified format, and mining a multi-dimensional scientific research portrait feature set from the data set; and based on the feature sets, performing pattern mining on academic output data of the researchers to obtain a research interest pattern set and a research trend index. Through predictive mining, a research interest prediction model and a project matching degree score are generated for historical research data, graph structure mining is performed on a researcher cooperation network by using the model, and a potential cooperation relationship is predicted. And finally, performing knowledge mining on the scientific research project data according to the project matching degree score, the network structure feature and the cooperation relationship prediction result, and generating a personalized project recommendation list and an interpretable recommendation basis thereof. According to the method, interest evolution, project characteristics and cooperation potential of researchers can be comprehensively considered, and recommendation accuracy and interpretability are improved.

The invention discloses a human-in-loop-based medical record data key information extraction and reconstruction method, which is characterized in that a method of introducing a human-in-loop technology into a large language model is adopted, and the large language model and expert priori knowledge are utilized to clean and standardize unstructured medical record data; the semantic retrieval and information understanding ability of the model is enhanced through a medical vector library, efficient extraction of key information is achieved, confidence is generated through self-evaluation, human expert verification is triggered under the condition of low confidence, the model is continuously optimized and updated through expert feedback of a human-in-loop, and the accuracy of human-in-loop verification is improved. And finally, medical record data structured output with higher credibility is realized. Compared with the prior art, the method has the advantages that the accuracy of automatic processing of medical record data is improved, the problem that an automatic processing system is difficult to meet high precision and high credibility is solved, and the method has wide application prospects in clinical decision support, intelligent medical record analysis, personalized diagnosis and treatment scheme making and medical scientific research data mining.

The invention discloses a dynamic response method and device for interactive object elements in an interactive scene, equipment and a medium, and belongs to the technical field of Internet. The method comprises the following steps: collecting research data; analyzing the investigation data, and determining a scene type of an interaction scene needing to be generated; inputting the scene type into an AI model, and generating scene elements in the interaction scene through the AI model; applying the scene elements to an interaction platform for rendering to obtain an interaction scene; in the process of displaying the interaction scene, receiving an interaction operation sent by a user in the interaction scene; and identifying an interaction object element corresponding to the interaction operation, and generating a dynamic effect of the interaction object element in the interaction scene according to the interaction operation. According to the technical scheme, the interaction scene can be generated in a personalized mode according to the investigation and survey data, the corresponding dynamic effect is generated according to the interaction operation of the user, the content of the interaction scene is enriched, and the interactivity and immersion of the interaction scene can be improved.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence-based spleen and liver trauma model establishment method, which comprises the following steps of: S01, acquiring target data; step S02, obtaining research data; s03, inputting research data into the improved U-Net network model, and performing organ segmentation to obtain a target organ contour; s04, performing three-dimensional reconstruction to obtain a trauma model corresponding to the target organ, and performing hemodynamic analogue simulation; through the step S03, construction of the improved U-Net network model is facilitated, compared with a traditional U-Net network model, the improved U-Net network model can significantly improve the accuracy and robustness of organ segmentation, the effectiveness and accuracy of the spleen and liver contour are enhanced, and an effective foundation is laid for subsequent three-dimensional modeling based on the spleen and liver contour.

The invention discloses an ion adsorption type rare earth ore prospecting method based on a multi-element composite mineralization mode, and belongs to the technical field of rare earth ore geological exploration. The method comprises the following steps: firstly, collecting the existing exploration and research data of the known granite weathered crust type rare earth deposit; analyzing the ore-forming basic elements of the known granite weathering crust ion adsorption type rare earth ore deposit; then field geological survey and sample collection are carried out on the known granite weathered crust ion adsorption type rare earth ore deposit; determining each metallogenic mode element by utilizing analysis test data and field survey data; analyzing the formation cause of the ore deposit, establishing a prospecting mark, and constructing a multi-component composite mineralization mode of the granite weathering crust type rare earth ore deposit. And finally, prospecting target area prediction is carried out. For the known high-altitude granite weathering crust ion adsorption type rare earth deposit, multiple exploration technologies are organically fused, the special environment of the high-altitude area is optimized, a multi-element composite mineralization mode is constructed, and brand-new theoretical guidance and technical means are provided for prospecting of ion adsorption type rare earth ore in the high-altitude area.

The invention provides an indoor thermal comfort air conditioner regulation and control method based on old people with different weak levels. The method provides a regulation and control strategy based on the weakness level of the elderly. Regulation and control strategy data comes from research data, and actual requirements of old people with different weak levels are reflected more objectively. According to the research data, the neutral temperature range and the thermal comfort temperature range of old people with different weak levels are counted. The old people do not need to frequently switch on and switch off the air conditioner at night through the regulation and control strategy, an air conditioner switch-on and switch-off program is automatically designed according to habits of the old people, and rest at night is facilitated. The grade of the elderly can be evaluated according to the actual information of the elderly, so that a regulation scheme is recommended. The regulation and control strategy also considers the condition that the old are not good at using the air conditioner, if the old press the air conditioner by mistake, default data are used for regulation and control according to the investigation and survey data of the real society, the default data are from the investigation and survey data of the real society, the situation that the room is too cold due to mistaken use of the old is prevented, and regulation and control are more suitable for aging.

The invention discloses a senile syndrome intelligent scientific research data management method and system, and relates to the technical field of data processing. The method comprises the following steps: integrating structured data of a hospital information system, analyzing an unstructured text, and extracting a key entity and a time sequence relationship to obtain multi-source patient structured data and time sequence feature data; performing cleaning, standardization and term normalization on the data based on the elderly syndrome proprietary domain ontology; a special disease data matrix is generated by dragging variables, and a scientific research queue is rapidly screened in combination with a Boolean logic grouping rule; dynamically adjusting random forest model parameters by adopting a wild horse optimization algorithm, training a high-risk prediction model based on historical data, and outputting a real-time high-risk case; combining the SHAP value quantization variable contribution degree and statistical test to generate a scientific research report; according to the method, the problems of medical scientific research data islands, non-uniform terms and poor model interpretability are solved, and efficient integration, accurate prediction and interpretable analysis of the senile syndrome data are realized.

The invention relates to the technical field of medical treatment, and discloses an intelligent subject evaluation method based on multi-dimensional data, which comprises the following steps: acquiring the multi-dimensional data corresponding to a subject, inputting the multi-dimensional data into a pre-trained evaluation model, and acquiring an analysis report of the subject participating in clinical research, the multi-dimensional data comprises clinical symptoms, traditional Chinese and western medicine diagnosis, medication information and social background of the subject, and the analysis report comprises accuracy, compliance and risk; based on the analysis report, determining a corresponding group-entering suggestion and a treatment research scheme for each subject, the treatment research scheme including drug selection, dose adjustment and a follow-up plan; in the clinical research process, the latest research data are continuously obtained, and the analysis report and the treatment research scheme of the subject are adjusted based on the latest research data. The quality and effectiveness of clinical research can be improved.

The invention relates to a scientific research auxiliary method and device based on a generative model and computer equipment, and is applied to the technical field of computer science. When the scientific research instruction comprises an experimental verification task, obtaining a knowledge base corresponding to the scientific research instruction based on an experimental verification agent, verifying the correctness of an experimental hypothesis in the experimental verification task based on the knowledge base, and generating a verification conclusion; wherein the knowledge base is established based on the pre-stored scientific research data, the problem that the experimental design in the scientific research task is lack of automatic analysis is solved, and the functions of combining the intelligent agent with the scientific research experiment and perfecting the scientific research assistance are realized.

The invention relates to a total primary productivity high-precision downscaling method based on a multi-scale attention mechanism, and belongs to the technical field of remote sensing. According to the method, the technical problems of fuzzy details, fuzzy boundaries and insufficient fitting precision of the existing GPP downscaling technology in a complex terrain region are solved. According to the technical scheme, an Enhanced Multi-scale Residual Attention Network (EMSRAN) model is constructed, multi-source remote sensing data including a medium-resolution imaging spectrometer (MODIS) GPP product and a terrestrial satellite auxiliary factor are integrated, data preprocessing, sample generation and model training and optimization are carried out, and a high-resolution GPP image is output. Technical effects include significant improvement of spatial detail reduction ability, enhancement of heterogeneous ecosystem adaptability, enhancement of boundary definition expression, and improvement of carbon cycle research data reliability.

The invention discloses an office building energy-saving reconstruction method based on neural network optimization, and belongs to the field of office building energy conservation, and the method comprises the steps: carrying out the energy audit of a target building, and obtaining the field investigation data; calculating the field research data based on DeST software, and constructing a building energy consumption model; based on the building energy consumption model, performing energy consumption simulation on various external enclosure structure energy-saving reconstruction scheme combinations to obtain energy consumption data under different enclosure structure combinations; constructing an ANN neural network model, and inputting the historical data set into the ANN neural network model to obtain a prediction model; inputting the energy consumption data under different enclosure structure combinations into the prediction model to obtain an optimal energy-saving scheme; and performing technical economic analysis and environmental economic evaluation on the optimal energy-saving scheme, and generating an energy-saving transformation report.

The invention discloses a scientific research archive data management method for a scientific research institute, and belongs to the technical field of archive management. The system solves the problem that existing archive management mainly depends on manual work, and careless mistakes are prone to occurring in the archive management process, the collected scientific research archives of the scientific research institute are classified, numbered and archived and stored in the archive library, a user can quickly search and call the archives conveniently, and then the archives are stored in the archive library according to the identity of the user. Different permissions are distributed to the user, the user can carry out corresponding retrieval and management on the archive according to the distributed permissions, it is ensured that the user carries out corresponding rights on the premise of following the permissions, and therefore the safety of the archive is ensured, abnormal or suspicious conditions of the user are found through real-time monitoring of user behaviors and operation, and the user experience is improved. And measures are taken in time for intervention, so that the safety of the archives can be further ensured, and the confidentiality and integrity of scientific research data are ensured.

The invention belongs to the technical field of computer software, and particularly relates to a data lake and warehouse management deployment method applied to a domestic operating system, which comprises the following steps: establishing a heterogeneous server cluster, deploying various domestic operating systems in the server cluster, and completing configuration of a network architecture and a basic software environment; the method comprises the following steps: carrying out localization adaptation on a software component source code, synchronously modifying package management logic of a self-research data lake and warehouse management platform, and further implementing intelligent compiling to generate a program package for different instruction set architecture and domestic operating system combination; automatically generating and executing a deployment script by utilizing a self-developed data lake and warehouse management platform to complete installation and configuration of each software component on a target node; and after deployment of each software component is completed, systematic deep tuning and component-level accurate tuning are automatically performed on the deployed system and each software component, and automatic verification is executed. The invention provides an efficient, stable, safe and controllable data lake and warehouse integrated solution for users, wherein the data lake and warehouse integrated solution can be used immediately after being opened.

The invention discloses an investment and research information sharing and distributing method for group business management and control, and relates to the technical field of data sharing, and the method comprises the following specific steps: information collection and integration: collecting an external authority information source and capturing related investment and research information by using a network robot, according to the method, multiple authoritative external information sources are regularly monitored and sorted, investment and research data generated by the internal business departments of the group are collected, the universality and accuracy of the information sources are ensured, meanwhile, intelligent screening is carried out by utilizing a rule-based expert system, and the accuracy of the information sources is improved. According to the method and the system, the pertinence and availability of the information are further improved by combining the social relation data with the user portraits and the project requirements, and the personalized and accurate pushing of the information is realized, so that the group enterprises can quickly obtain the information closely related to the business decision.

The invention belongs to the technical field of ecological restoration evaluation, and provides a geological ecological restoration evaluation method and system for geological disaster prevention and control, electronic equipment and a storage medium. The method comprises the steps of research data acquisition, geological disaster type setting, initial evaluation, data preprocessing, model construction and training, risk determination before restoration, information acquisition after restoration, geological disaster risk prediction and geological ecological restoration evaluation. According to the method, the model is trained by using the remote sensing image, the sound wave data and the initial evaluation data, the evaluation result is predicted through the remote sensing image and the sound wave data, the risk evaluation difficulty and time are reduced, in addition, remote sensing and sound waves are combined, meanwhile, surface features and structural features are extracted, and the prediction effect of the model is optimized; through a multi-mode proximity perception model, the perception capability of the surrounding environment is improved, and the prediction precision of a single block is improved; and through the repair effect evaluation rule, a repair effect evaluation mechanism is optimized.

The invention relates to the technical field of market research data analysis, in particular to a market research data analysis method and system.The method comprises the steps that multi-source data is collected and cleaned through a multi-channel self-adaptive data integration technology, and a comprehensive data set is generated; constructing a graph network structure based on a graph neural network, updating node features through a message passing mechanism, generating market prediction data, and optimizing an investigation path based on a reinforcement learning algorithm; comparing the market feedback data with the prediction data through a feedback self-learning mechanism, and adjusting an investigation strategy to obtain an optimized investigation path; the performance of the market under extreme conditions is simulated through Monte Carlo simulation, the abnormal behaviors of the market are identified through dynamic outlier detection, a coping strategy is generated, and an investigation path is adjusted. The method effectively improves the accuracy of investigation data and the allocation efficiency of investigation resources, and adapts to a complex market environment.

The invention provides a teaching document anti-copy protection method and system based on OCR interference resistance, and belongs to the technical field of information security and digital copyright protection. According to the method, anti-OCR font processing is applied to document characters, invisible disturbance symbols are inserted between characters or paragraphs, watermark information is embedded in document typesetting or font strokes, and finally a standard PDF or DOCX file is compiled. Watermark information is embedded through stroke thickness change, paragraph row spacing indentation perturbation and other modes. The system comprises a font disturbance module, a character disturbance module, a watermark embedding module, a compiling output module and a verification module. According to the method, the human eyes can normally read the document, but messy codes or disorder can occur when the OCR tool extracts the document, and the document cannot be directly reused; meanwhile, watermark information cannot be stripped, so that leakage tracking is facilitated; and the output file keeps a standard format and is compatible with the existing office teaching process. The method is suitable for anti-copying protection of teaching documents, scientific research data, test papers, contract files and the like.

The invention relates to an intelligent hospital management system based on clinical scientific research integration, and the system comprises a clinical scientific research integration platform which is provided with a special disease database data service module which is used for integrating patient clinical data with a patient as a center; the special disease database data service module comprises a special disease scientific research data center which is used for storing high-quality multi-dimensional structured patient data processed by an artificial intelligence technology; the regional decision application module is used for collecting multi-hospital-area data and carrying out hospital diagnosis and treatment and multi-dimensional analysis of medication data; the regional collaborative application module is used for realizing medical resource sharing of multiple hospital areas; and the regional multi-center-line image integration module is used for acquiring image system data of a plurality of courtyards and realizing sharing and mutual recognition. According to the invention, multi-center medical data integration and scientific research collaboration are realized, and the collaborative work and collaborative diagnosis and treatment efficiency is improved.

The invention discloses a survey data processing method, system and device based on AI analysis and a storage medium. According to the method, investigation mode information can be obtained, and a data analysis source is determined according to the investigation mode information. And creating an interactive dialogue to obtain question data input by the user. And inputting the question data into the AI analysis model, so that the AI analysis model extracts survey project data from the question data, and obtains key information from the data analysis source according to the survey project data to output answer data and associated data. And displaying the answer data and the associated data. According to the method, the investigation data can be processed based on artificial intelligence, automation and data analysis technologies, and the processing efficiency and accuracy of the investigation data are effectively improved. And key information is obtained from different data analysis sources according to different investigation modes, so that a personalized data analysis method can be provided, and the pertinence and effectiveness of investigation and research are enhanced.

The invention relates to a scientific information acquisition data analysis method for market research, and belongs to the technical field of market research. The method comprises the following steps: S1, question definition and target setting: determining an investigation purpose, a research object and questions needing to be answered, and determining the range and depth of market investigation; s2, information data acquisition: scientifically acquiring original data of market investigation from a plurality of market investigation channels; s3, data processing: using statistics to clean, sort and summarize the collected data; s4, information data screening: selecting original data corresponding to market investigation according to market investigation requirements, and obtaining various types of original investigation data; s5, extracting data features; s6, data analysis; and S7, data feedback. According to the invention, scientific information acquisition can be carried out on various market research data, the information acquisition time is shortened, the information acquisition speed and accuracy are effectively improved, professionals can conveniently analyze various data more clearly later, and the authenticity of data analysis results is enhanced.

The invention discloses a data fusion method, apparatus and device, and a storage medium. The method comprises the steps of obtaining scale data in each database; by means of a preset semantic analysis model, scale fields in the scale data are transferred into standard names in the CDISC standard, and a field mapping relation and a field value mapping relation are constructed; converting the scale data into target data conforming to the CDISC standard based on the field mapping relation and the field value mapping relation; and performing data fusion on the target data according to the weight of each database by adopting a weighted average method to obtain a data fusion result. The scale field in the scale data is transferred into the standard name by utilizing the preset semantic analysis model, the scale data is converted into the target data meeting the CDISC standard on the basis of the field mapping relation and the field value mapping relation, and finally, the target data is fused by adopting the weighted average method, so that the data fusion efficiency is improved. And the efficiency and reusability of cross-column research data fusion are improved.

The invention relates to the technical field of enterprise management, and discloses an intelligent enterprise management system and method based on big data, and the system comprises a multi-dimensional collection module and an intelligent management module. According to the enterprise intelligent management system and method based on big data, management data of all regional branch companies, working data of employees and market research data are acquired through a multi-dimensional acquisition module and are classified to form a data set, and an intelligent management module analyzes the cost-benefit ratio, the index achievement rate, the net flow rate and the operation score of each regional branch company; the output ratio and the individual score of each employee are analyzed, the intelligent management module analyzes the fluctuation rate of historical product prices, the fluctuation rate of peer product prices, the fluctuation rate of product failure rates, and the fluctuation rate and the fluctuation coefficient of peer product failure rates of each region, and the multi-dimensional analysis integration capability is high; the intelligent management module judges operation states of regional branch companies, working states of employees and market fluctuation trends and performs scheduling measures, so that efficient operation and high emergency response capability are achieved.

The invention discloses a disaster prevention and control refined division method based on map data. The method comprises the following steps: step S10, subdividing an evaluation unit based on map data, landform data and administrative division; step S20, comprehensively selecting prevention and treatment alternative areas through risk assessment, vulnerability assessment and disaster assessment; step S30, constructing an index system from three dimensions of dangerousness, exposure and vulnerability; s40, performing index quantification based on the map data and the investigation and survey data, and calculating the weight of each index in combination with the information entropy; step S50, calculating a comprehensive index of each evaluation unit by adopting a Jenks natural fracture method; and step S60, finely delimiting disaster prevention and control key areas in combination with actual management department experience and comprehensive indexes. The method is convenient for later-period management, avoids a large amount of repeated work, and has relatively high objectivity and reference value.

The invention relates to the technical field of fracture network prediction, and discloses a connectivity fracture network structure intelligent generation method based on a GA-GAN model, and the method comprises the following steps: 1, building a fracture network database; step 2, establishing a GA-GAN model; step 2.1, importing information and setting parameters; step 2.2, constructing a discriminator; step 2.3, establishing a generator based on a genetic algorithm; 2.4, training the discriminator; step 2.5, carrying out iterative training; and step 3, automatically generating a corresponding connectivity fracture network structure by using the GA-GAN model. The fracture network structure intelligent generation method successfully solves the problems of low efficiency, high cost, insufficient authenticity and the like in the traditional fracture network generation, and the fracture network generated by the model not only retains the complex geometric characteristics of a real network, but also can adapt to different geological scenes, and has good application prospects. And research data with higher reference value is provided for practical applications such as underground water flow simulation and oil and gas exploitation.