The rapid development of big data technology has brought new opportunities and challenges to oil reservoir modeling. This paper systematically reviews the key applications of big data in oil reservoir modeling, covering crucial stages such as data acquisition, preprocessing, data fusion, and integration, and focuses on innovative methods for predicting oil reservoir properties (e.g., porosity and permeability) based on machine learning algorithms. By combining the strengths of data-driven models with traditional physical models, strategies to improve the accuracy and reliability of oil reservoir modeling are proposed. Furthermore, the paper delves into the applications and value of big data technology in real-time oil reservoir modeling, oil reservoir evaluation, development decision-making, uncertainty analysis, and risk assessment. Finally, the paper envisions future trends in the deep integration of big data artificial intelligence, and efficient computing technologies in oil reservoir management, especially their potential to support sustainable oilfield development. This work aims to provide practical insights for big data-driven oil reservoir modeling, promoting the intelligence and efficiency of oilfield development.

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With the growing demand for efficient and precise production in the oil industry, oilfield production optimization and intelligent management have become key research areas. This paper systematically reviews the applications of big data and artificial intelligence (AI) algorithms in oilfield production optimization and intelligent management. First, it explores real-time monitoring systems for oilfields based on big data, covering data collection and processing, real-time monitoring technologies, anomaly detection, and early warning mechanisms. Next, it provides an in-depth analysis of the application of AI algorithms in oil extraction parameter optimization, explaining how machine learning and deep learning models enable dynamic optimization of extraction parameters and real-time decision support. Through specific case studies, the paper demonstrates the practical applications of these technologies in oilfields and their impact on improving production efficiency. Finally, it discusses future trends in oilfield production optimization and intelligent management, addressing technical challenges such as big data processing, AI algorithm complexity, and system integration. This paper aims to provide a comprehensive knowledge framework for researchers and engineers in the oilfield industry, promoting the deeper application and development of intelligent management technologies in oilfield production.

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With the rapid development of artificial intelligence (AI) and big data technologies, the application of these advanced technologies in petroleum engineering has progressively deepened, showing significant potential, particularly in oilfield completion design and optimization. This paper systematically reviews the key applications of AI and big data in oilfield completion processes, covering areas such as data-driven completion parameter optimization, formation pressure and temperature prediction, real-time monitoring, and intelligent decision support. It provides a detailed analysis of how these technologies enhance the precision and safety of completion operations. Furthermore, the paper explores innovative applications of AI in completion safety management, focusing on optimizing the balance between operational safety and efficiency. The article concludes by summarizing current technical challenges, including data quality control, limited algorithm adaptability, and the need for interdisciplinary collaboration, while also highlighting future research directions. This study aims to serve as a valuable reference for the further application of AI and big data technologies in oilfield completion.

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In the process of oilfield development, reservoir, and fluid dynamic simulation are critical tools for optimizing resource utilization and enhancing recovery rates. However, traditional methods face significant limitations when addressing complex reservoir characteristics and fluid behaviors. With the rapid development of big data and artificial intelligence (AI) technologies, these advanced tools offer innovative solutions for reservoir characterization and fluid dynamic simulation. This paper provides a comprehensive review of the applications of big data and AI in reservoir characterization, fluid dynamic simulation, and oilfield development optimization, systematically exploring the potential of these technologies to enhance efficiency and sustainability in oilfield development. First, the paper introduces the application of big data in reservoir information extraction, focusing on the advantages of AI algorithms in improving reservoir modeling accuracy. Next, it examines the essential role of big data and AI in fluid dynamic monitoring and prediction, highlighting their significant contributions to optimizing oil recovery strategies, increasing recovery rates, and reducing resource wastage. Finally, the paper discusses the challenges in applying these technologies and envisions the extensive prospects of big data and AI in the oil and gas industry, providing valuable insights for future research and practice.

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With the increasing difficulty of oil and gas resource development, traditional hydraulic fracturing design faces numerous challenges in enhancing production capacity and reducing geological risks. In recent years, big data and artificial intelligence (AI) technologies have provided new opportunities for intelligent optimization in hydraulic fracturing design. This paper systematically reviews the current applications of big data and AI in hydraulic fracturing, focusing on reservoir characterization based on big data, optimization of fracturing parameters, and the central role of AI in fracture performance prediction, real-time operational optimization, and geological risk management. By integrating these advanced technologies, an intelligent fracturing design model has been established, significantly improving operational efficiency and effectively reducing geological risks. The paper also analyzes the main challenges in the current application of these technologies and proposes future research directions to support the continuous advancement of hydraulic fracturing technology in oilfields.

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As the penetration rate of renewable energy sources such as wind and solar power increases, traditional power systems face growing challenges to stability, particularly in frequency fluctuation and voltage control. This paper systematically studies the application of big data and artificial intelligence (AI) technologies in power systems, focusing on their roles in real-time monitoring, fault prediction, and intelligent scheduling to improve grid stability. It also explores the critical role of energy storage in mitigating renewable energy volatility and enhancing the system’s dynamic response capabilities. Additionally, the paper introduces the virtual synchronous generator as an intelligent control strategy that significantly enhances frequency stability by providing virtual inertia. The importance of optimized control of power electronic devices in maintaining voltage stability is further analyzed. Through system simulations, it is verified that the collaborative application of big data, AI, energy storage, and intelligent control technologies can effectively enhance the stability of power systems with high renewable energy penetration. This paper aims to propose an integrated solution and technical roadmap involving big data, AI, energy storage, and power electronics to ensure the safe and stable operation of power systems.

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With the continuous expansion and increasing complexity of modern power systems, traditional local relay protection demonstrates significant limitations in handling large-scale faults, making it challenging to meet the system’s requirements for rapid response and global coordination. As an advanced protection solution, wide area protection (WAP) technology utilizes wide area measurement systems (WAMS) to provide real-time, synchronized data acquisition of the power grid, thereby significantly enhancing fault detection, localization, and response accuracy and efficiency. This paper systematically describes the basic principles of WAP and the architecture and functions of WAMS and analyzes the mechanism of their coordinated operation within power systems. To address the critical challenges WAP faces in practical applications—such as communication delays, reliability, and security—targeted solutions are proposed. Additionally, the potential applications of emerging technologies such as edge computing and artificial intelligence in WAP are explored, providing new technological pathways for further improving the safety and stability of power systems.

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With the rapid advancement of genomics and biotechnology, crop genomics and genome-wide association studies (GWAS) have been widely applied in crop research. This article systematically reviews the fundamentals of crop genomics, covering genome sequencing technologies, gene annotation, functional prediction, and recent advances in comparative genomics. It then delves into the basic principles of GWAS, thoroughly analyzing methods for collecting phenotypic and genotypic data and data processing techniques, emphasizing the significant role of GWAS in elucidating agronomic traits, stress resistance studies, and gene mining. The article also discusses major challenges in crop genomics and GWAS research, such as the genetic analysis of complex traits, handling large datasets, and gene-environment interactions. Finally, future research directions are proposed, including the integrative analysis of multi-omics data and the application prospects of artificial intelligence and machine learning. This article aims to provide researchers with a comprehensive academic perspective and cutting-edge information to promote further innovation and development in crop genomics and GWAS.

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With the growing global population and the increasing challenges of climate change on agricultural production, developing high-yield and resilient crop varieties has become increasingly urgent. Integrating artificial intelligence (AI) and gene editing technologies offers a groundbreaking solution to this issue. This article systematically explores the application of AI and gene editing in high-yield crop breeding, covering areas such as genome big data analysis, precise gene editing, phenotype prediction, resource optimization, and environmental adaptability assessment. Through case studies, the paper demonstrates the successful application of these technologies in key crops like rice and wheat. It provides an in-depth discussion of their continual improvement and future development trends. This study aims to offer new insights into agricultural innovation and proposes potential solutions for global food security and sustainable agricultural development.

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Global climate change and increasingly complex agricultural environments have made enhancing crop resilience and productivity a core challenge in modern agriculture. Integrating genomic data with environmental data offers an innovative solution for crop breeding. Through big data platforms, researchers can combine crop genomic information with ecological data, such as soil, moisture, and climate, to analyze crops’ stress responses at the genetic level. This data-driven approach provides new pathways for breeding drought-tolerant, salt-resistant, and high-yield crop varieties and optimizes crop management strategies, advancing precision agriculture. This paper explores the key technologies and models for integrating genomic and environmental data, discusses relevant breeding practices, and envisions the future direction of intelligent breeding and crop management. This big data-based approach is a crucial reference for sustainable agricultural development and addressing global environmental challenges.

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In modern agriculture, molecular design breeding has gradually become a core technology for enhancing crop yield, quality, and stress resistance. This paper systematically reviews the applications and recent advancements in metabolomics and proteomics within this field. Metabolomics, through precise analysis of metabolite profiles, identifies metabolic markers associated with key agronomic traits and offers insight into crop metabolic responses under varying environmental conditions, supporting breeders in selecting high-quality varieties adapted to specific environments. Proteomics, by examining protein expression profiles and functional proteins, reveals the mechanisms underlying crop disease resistance and the development of complex traits. Integrating these molecular design breeding technologies accelerates breeding processes and promotes the comprehensive improvement of multiple traits. This paper presents successful applications of metabolomics and proteomics in breeding through specific case studies and discusses prospects and challenges in the field. This study aims to offer innovative perspectives for crop molecular breeding by delving into these frontier technologies.

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As a globally important food crop, Maize is significantly affected in yield and growth by drought stress. Due to the complex multigene regulatory networks governing maize drought resistance traits, single-omics data alone are insufficient to reveal its regulatory mechanisms comprehensively. This study integrates multi-omics data, including genomics, transcriptomics, epigenomics, and metabolomics, to systematically construct a maize drought resistance gene regulatory network. Through big data analysis, combined with multidimensional data under various environmental and stress conditions, key gene nodes and core transcription factors within the drought-resistance regulatory network were identified. Utilizing artificial intelligence (AI) algorithms, such as random forest and deep learning, we extensively explored gene interactions and identified gene combinations contributing significantly to drought resistance traits. Subsequently, CRISPR/Cas9 gene editing technology was employed to validate the functions of these key genes, preliminarily exploring their application potential in drought-resistance breeding. The results indicate that integrating multi-omics data with AI algorithms significantly enhances the capacity to analyze complex gene regulatory networks and improves the efficiency of drought-resistance gene selection. This study offers new insights into the molecular regulatory mechanisms of maize drought resistance traits and provides strong candidate genes and strategic support for precision breeding.

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The continuous growth in global food demand poses significant challenges for agriculture. Gene editing technology offers new solutions for crop breeding, yet the complexity of genome and multi-omics data interpretation remains a critical bottleneck. In recent years, the remarkable performance of artificial intelligence (AI) in big data processing and analysis has brought new opportunities to gene editing and crop breeding. This paper systematically explores the multi-level applications of AI in agricultural genomics, including data processing and analysis, gene-editing target prediction, crop phenotype prediction, breeding optimization, and automated high-throughput screening. Through case studies, the paper demonstrates how AI enhances the efficiency and accuracy of gene editing, shortens breeding cycles, and examines future development potential and challenges. This work aims to provide reference and inspiration for research and practical applications in related fields.

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In modern agricultural technology development, precision fertilization based on big data and artificial intelligence (AI) has become a critical strategy for enhancing rice production efficiency and achieving sustainable agriculture. This paper systematically analyzes the dynamic nitrogen demand of rice at various growth stages, proposing a data-driven precision fertilization model through real-time monitoring to optimize nitrogen application strategies and improve nitrogen use efficiency. By integrating soil sensors, the Internet of Things, and remote sensing technology, nitrogen demand can be monitored in real-time and undergo intelligent decision-making via AI algorithms, reducing nitrogen waste and minimizing environmental pollution. Additionally, the paper explores the potential of gene editing technology to enhance rice nitrogen use efficiency. Through multiple case studies, this paper demonstrates the practical applications of big data and AI technologies in rice nitrogen management, aiming to explore how technological innovations can advance intelligent nitrogen management and sustainable agricultural production.

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In recent years, significant progress has been made in big data analysis and intelligent breeding technology for crop molecular breeding, greatly enhancing agricultural productivity. This article systematically reviews the current research status, application examples, technical bottlenecks, future directions in crop molecular breeding, big data analysis, and intelligent breeding technology. First, it introduces the definition, historical evolution, and current applications of crop molecular breeding in modern agriculture. Next, it elaborates on applying big data technology in crop breeding, including methods for the integrated analysis of genomic, phenotypic, and environmental data. Subsequently, it focuses on intelligent breeding technologies, particularly the application of machine learning, artificial intelligence, and gene editing in precision breeding, and discusses the key role of high-throughput phenotyping. The article further proposes a comprehensive breeding strategy that integrates big data and intelligent technologies, leveraging multi-source data integration and analysis to promote breeding precision and showcases successful case studies of practical applications. Finally, it discusses the major challenges currently faced in the field and explores future research directions and innovation potential. This article aims to provide a reference for crop breeding research, supporting the further development of related technologies.

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Wetlands are vital global ecosystems, and their health is crucial for maintaining ecological balance and promoting environmental conservation. Traditional methods for assessing wetland vegetation health primarily rely on ground surveys and limited remote sensing technology, often facing limitations such as insufficient data coverage and low analytical efficiency. With the rapid advancement of artificial intelligence (AI) technologies, AI applications in wetland vegetation health assessment have expanded. This paper systematically reviews the use of AI in remote sensing data analysis, time-series modeling, multi-source data integration, automated monitoring and early warning systems, and ecological model development. Through case studies, we explore how AI can enhance assessment accuracy and efficiency. Additionally, this paper addresses the main challenges in current technology applications and discusses key directions for future development. The study shows that AI provides powerful wetland vegetation health assessment tools, offering promising prospects for advancing ecosystem monitoring and management capabilities. This paper aims to provide researchers and managers with in-depth insights into the latest developments and future applications of AI in wetland conservation.

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Against the backdrop of global climate change, drought has become one of the key challenges limiting agricultural production and food security. To mitigate the negative impacts of drought on crop growth and yield, it is essential to investigate the water utilization mechanisms of drought-resistant crops in depth. This paper, utilizing big data technologies, systematically explores drought-resistant crops' soil water utilization mechanisms under various environmental conditions. Firstly, leveraging the Internet of Things sensors and remote sensing technologies, this study achieved dynamic monitoring of soil moisture and developed a high-precision soil moisture variation model. Secondly, it deeply analyzed the water use efficiency of drought-resistant crops, focusing on adaptive mechanisms in physiological processes, such as root system architecture, stomatal regulation, and transpiration. This paper further elucidated the complex relationships between crop water use efficiency and gene regulatory networks by integrating genomic and environmental data. Finally, based on big data analysis, recommendations are provided for optimizing drought-resistant crop selection and cultivation management strategies, highlighting this approach's potential in drought and irrigated conditions. This paper aims to offer practical insights for the selection, cultivation strategy optimization, and sustainable development of precision agriculture in drought-resistant crops.

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With the rapid development of global agriculture, big data technology plays a crucial role in analyzing soil health and crop adaptability. This paper systematically examines the application of big data in the integrated assessment of soil chemical, physical, and biological properties, revealing the complex relationships between soil types and crop growth. It focuses on the impacts of soil degradation on crop growth, yield, and quality and explores the feasibility of optimizing soil management strategies using big data, covering areas such as precision fertilization, irrigation control, and soil improvement. Additionally, this paper delves into applying big data-driven predictive models for selecting highly adaptable crop varieties, considering environmental factors such as climate change, and presenting innovative solutions for optimizing agricultural cultivation strategies. The practical effectiveness of big data technology in soil health management and crop adaptability analysis is validated through case studies. This paper aims to provide a scientific basis for academia and agricultural practitioners, promoting sustainable agricultural production and accelerating the application of data-driven intelligent agricultural management.

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Soil salinization is a major challenge in global agricultural production, severely affecting crop growth and soil health. With the intensification of climate change and human activities, the issue of salinization is becoming increasingly prominent, and traditional remediation methods are sometimes limited in effectiveness. Big data technology offers new opportunities for the precise management of saline-alkali soils. This study integrates data on environment, climate, soil, and crop growth to analyze the mechanisms underlying salinization and to reveal the impacts of climate conditions, groundwater levels, and land use on soil salinization. Using big data technology, we aim to achieve real-time monitoring, accurate prediction, and intelligent management of soil salinization to optimize irrigation and improvement measures. Additionally, the study explores physical, chemical, and biological improvement strategies and proposes a data-driven integrated management approach. Through the analysis of global and Chinese case studies, we validate the effectiveness and feasibility of data-driven improvements in saline soils. This study aims to explore how big data technology can provide scientific support and innovative solutions for soil salinization management, offering insights into the sustainable development of future agriculture.

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Amid global climate change and increasing water scarcity, optimizing agricultural irrigation is crucial for ensuring food security and sustainable agricultural development. This paper proposes a big data-driven approach to optimize agricultural irrigation, integrating climate conditions, water resources, soil types, and crop demands to provide precise irrigation solutions for various crops. A data-driven irrigation model is developed by analyzing the dynamic impacts of climate change on crop water requirements, the water retention capacity of different soil types, and the water demand characteristics of diverse crops. IoT and sensor technology application in smart irrigation systems is further explored, with proposed design and implementation strategies. Through case studies and field validation, this paper demonstrates the method’s significant impact on enhancing the efficiency of agricultural water management. This paper aims to guide the selection of optimal irrigation solutions for different crops, improving irrigation efficiency and optimizing water resource management.

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Under the dual pressures of global climate change and population growth, water resource risk management faces increasingly severe challenges. The rapid development of big data and artificial intelligence (AI) offers unprecedented water resource risk assessment and response opportunities. This paper systematically explores the applications of big data in water resource management, including multi-source integration of hydrological data, dynamic analysis of supply-demand balance, and optimization of water quality management. With the support of AI models, the predictive capabilities for extreme hydrological events, such as floods and droughts, have significantly improved, playing a critical role, especially in real-time decision-making and emergency response. Additionally, integrating intelligent water resource management systems demonstrates how big data and AI technologies can optimize water resource scheduling, allocation, and long-term planning. However, current technologies still face challenges in data quality, model accuracy, and interdisciplinary collaboration. This paper aims to summarize the latest advancements in big data and AI for water resource risk assessment, analyze existing technical bottlenecks, and propose future research directions to promote intelligent and sustainable water resource management.

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Water stress is a critical limiting factor for winter wheat’s growth, development, and yield in arid regions. In this study, conducted over two growing seasons (2020-2021 and 2021-2022), different water stress treatments were applied to systematically analyze the impact of water stress at various growth stages of winter wheat. The experimental design included water stress treatments during the overwintering, green-up, jointing, heading, and grain-filling stages to examine the effects of different irrigation quotas on wheat growth and yield. Results showed that water stress during the overwintering and green-up stages had the most pronounced inhibitory effects on wheat growth, as indicated by significant reductions in plant height and leaf area index. In contrast, wheat at the jointing and heading stages exhibited stronger adaptability, with the number of effective spikes and grains per spike remaining relatively stable. Yield analysis revealed that water stress during the overwintering and green-up stages had the most significant negative impact on final yield, whereas moderate water management adjustments during the jointing and grain-filling stages contributed to maintaining higher yields. Further analysis indicated significant interactions between different irrigation quotas and water stress timing. Appropriate irrigation practices can effectively enhance water use efficiency and ensure stable, high wheat yields. This study reveals the mechanisms of water stress effects at various growth stages of winter wheat in arid regions, providing practical insights for optimizing water management in winter wheat cultivation in arid areas, enhancing water use efficiency, and supporting sustainable agriculture in arid regions.

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The intensification of global climate change has made soil moisture management and plant drought resistance critical topics in agricultural and ecosystem management. This paper systematically explores the complex interactions among soil, plants, and climate, focusing on the relationships between soil moisture dynamics, climatic conditions (such as temperature and precipitation), and plant drought tolerance. First, it elaborates on the differences among soil types in water retention and drainage capacity and their impact on plant growth. Then, it delves into the multidimensional effects of climate change on the soil-plant system, particularly the adaptive mechanisms of plants in arid environments. The study also presents how predictive models can be developed using big data analysis and artificial intelligence techniques to address water management and crop drought resilience under various climate scenarios. Through case studies, the paper demonstrates the application of these strategies across different regions and crops and concludes with future research directions and associated challenges. The findings provide a scientific basis for optimizing soil moisture management and enhancing crop drought tolerance, supporting sustainable agriculture under the backdrop of climate change.

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