Key Technologies Research Articles

Preparation and thermal performance study of a novel hydrated salt composite PCM for space heating

In order to effectively address the mismatch between thermal energy supply and demand in buildings, latent heat storage (LHS) based on phase change material (PCM) stands out as a key technology. While hydrated salt has emerged as a promising PCM, it encounters common issues such as substantial supercooling, phase separation, and low thermal conductivity, hindering its development. To mitigate these limitations, this study focused on modifying Ba(OH)2·8H2O by incorporating various nucleating agents and thickening agents to reduce supercooling, enhancing the composite PCM's thermal properties by introducing expanded graphite (EG) to elevate thermal conductivity. Subsequently, the thermal stability of the PCM was evaluated through accelerated thermal cycling tests, encompassing the assessment of critical thermal parameters, including supercooling degree, enthalpy, and thermal conductivity of the composite PCM. Experimental findings unveiled the effectiveness of incorporating 2 wt% BaCl2·2H2O and 1 wt% CMC to Ba(OH)2·8H2O, effectively diminishing the supercooling degree from 14 °C to 1.42 °C. Further integration of 3.5 wt% EG led to a substantial decrease in the supercooling degree to 0.24 °C. Simultaneously, the addition of EG resulted in an improvement in thermal conductivity, elevating it from 0.62 W/(m·K) to 1.03 W/(m·K), while the enthalpy decreased from 283.68 J/g to 252.81 J/g. Notably, the composite PCM exhibited notable thermal reliability with a latent heat drop of 15.9 % following 1000 accelerated cycles of testing. Moreover, the composite PCM demonstrated superior thermal storage density, measuring at 2.36 times that of traditional paraffin wax, yet with a thermal storage cost merely a quarter of paraffin wax, rendering it more favorable for extensive deployment in building energy systems.

BrepMFR: Enhancing machining feature recognition in B-rep models through deep learning and domain adaptation

Feature Recognition (FR) plays a crucial role in modern digital manufacturing, serving as a key technology for integrating Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP) and Computer-Aided Manufacturing (CAM) systems. The emergence of deep learning methods in recent years offers a new approach to address challenges in recognizing highly intersecting features with complex geometric shapes. However, due to the high cost of labeling real CAD models, neural networks are usually trained on computer-synthesized datasets, resulting in noticeable performance degradation when applied to real-world CAD models. Therefore, we propose a novel deep learning network, BrepMFR, designed for Machining Feature Recognition (MFR) from Boundary Representation (B-rep) models. We transform the original B-rep model into a graph representation as network-friendly input, incorporating local geometric shape and global topological relationships. Leveraging a graph neural network based on Transformer architecture and graph attention mechanism, we extract the feature representation of high-level semantic information to achieve machining feature recognition. Additionally, employing a two-step training strategy under a transfer learning framework, we enhance BrepMFR's generalization ability by adapting synthetic training data to real CAD data. Furthermore, we establish a large-scale synthetic CAD model dataset inclusive of 24 typical machining features, showcasing diversity in geometry that closely mirrors real-world mechanical engineering scenarios. Extensive experiments across various datasets demonstrate that BrepMFR achieves state-of-the-art machining feature recognition accuracy and performs effectively on CAD models of real-world mechanical parts.

Blockchain technology integration in service migration to 6G communication networks: a comprehensive review

<p>The next generation of wireless networks, 6G is being designed with data-intensive applications. One of the key technologies that will enable 6G is blockchain technology. The emergence of blockchain technology and 6G networks has revolutionized service migration. Service migration in 6G networks is a complex process that requires the integration of new technologies, such as artificial intelligence (AI), edge computing, and network slicing. Motivated by these facts, this comprehensive review includes an overview of blockchain and service migration integration in 6G. First, state of art, development frame work and related works were introduced. Then, we used content analysis by WordStat software and bibliographic analysis by VOSviewer to analysis the current status of service migration and blockchain integration in 6G networks. Next, patterns and characteristics, benefits and challenges and potential cases were reviewed. Then, we proposed an architectural blockchain-based model including decentralized architecture, edge computing, network slicing, software-defined networking, and 5G-6G interworking in 6G. Finally, we described potential application service migration-based in 6G networks including digital twin (DT), holograms, robot avatar, high density internet of things (IoT), AR and VR in 6G and collected open research and future directions of service migration and blockchain.</p>

Implementation of Smart Vehicle Assistant using Computer vision and Deep Learning

bstract: Autonomous cars are a popular research topic in the field of intelligent transportation systems because they have the potential to greatly reduce traffic and improve travel efficiency. Scene classification is a key technology for autonomous cars because it forms the basis of these vehicles' decision-making. In recent years, deep learning-based methods have demonstrated promise in resolving the scene classification issue. However, further research is needed on a few difficulties related to scene classification methodologies, such as how to treat similarities and contrasts within the same category. To tackle these problems, this research suggests an improved deep network-based scene classification method. Autonomous cars are a popular research topic in the field of intelligent transportation systems because they have the potential to greatly reduce traffic and improve travel efficiency.

Real-time deformable SLAM with geometrically adapted template for dynamic monocular laparoscopic scenes.

Intraoperative reconstruction of endoscopic scenes is a key technology for surgical navigation systems. The accuracy and efficiency of 3D reconstruction directly determine the effectiveness of navigation systems in a variety of clinical applications. While current deformable SLAM algorithms can meet real-time requirements, their underlying reliance on regular templates still makes it challenging to efficiently capture abrupt geometric features within scenes, such as organ contours and surgical margins. We propose a novel real-time monocular deformable SLAM algorithm with geometrically adapted template. To ensure real-time performance, the proposed algorithm consists of two threads: a deformation mapping thread updates the template at keyframe rate and a deformation tracking thread estimates the camera pose and the deformation at frame rate. To capture geometric features more efficiently, the algorithm first detects salient edge features using a pre-trained contour detection network and then constructs the template through a triangulation method with guidance of the salient features. We thoroughly evaluated this method on Mandala and Hamlyn datasets in terms of accuracy and performance. The results demonstrated that the proposed method achieves better accuracy with 0.75-7.95% improvement and achieves consistent effectiveness in data association compared with the closest method. This study verified an adaptive template does improve the performance of reconstruction of dynamic laparoscopic Scenes with abrupt geometric features. However, further exploration is needed for applications in laparoscopic surgery with incisal margins caused by surgical instruments. This research serves as a crucial step toward enhanced automatic computer-assisted navigation in laparoscopic surgery. Code is available at https://github.com/Tang257/SLAM-with-geometrically-adapted-template .

MODELING AND OPTIMIZATION OF A PCM-BASED OCEAN THERMAL ENERGY HARVESTER FOR POWERING UNCREWED UNDERWATER VEHICLES

Abstract As oceans cover over 70% of the planet's surface, they represent a large reservoir of resources that remained vastly untapped. Uncrewed Underwater Vehicles (UUVs) are becoming key technology for ocean exploration. Ocean thermal gradient is a permanent and reliable energy source that can be used to power UUVs using phase change material (PCM)-based thermal engines. When using PCM-based thermal engines to power UUVs, there are different energy conversion stages, thermal, hydraulic, kinetic, and electrical, dependent on a wide variety of parameters. Thus, optimization of the overall energy conversion is still a challenge for powering the increasing energy demanding UUVs for long missions. The goal of this study is to propose a PCM-based ocean thermal energy harvesting system for powering float type UUVs such as the Solo II float. This reduces the cost for battery replacement and expands the float's mission time. For this purpose, we developed a model consisting of hydraulic and electrical systems, designed to provide the electrical power needed by the UUV. The hydraulic and electrical systems are implemented using MATLAB-Simulink. The model developed can provide 13.66 kJ of electrical energy, which is more than 1.5 times the energy requirement per cycle for the SOLO II float.

Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends

Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs.

Mobile Robot Path Planning Algorithm Based on NSGA-II

Path planning for mobile robots is a key technology in robotics. To address the issues of local optima trapping and non-smooth paths in mobile robot path planning, a novel algorithm based on the NSGA-II (Non-dominated Sorting Genetic Algorithm II) is proposed. The algorithm utilizes a search window approach for population initialization, which improves the quality of the initial population. An innovative fitness function is designed as the objective function for optimization iterations. A probability-based selection strategy is employed for population selection and optimization, enhancing the algorithm’s ability to escape local minima and preventing premature convergence to suboptimal solutions. Furthermore, a path smoothing algorithm is developed by incorporating Bézier curves. By connecting multiple segments of Bézier curves, the problem of the high computational complexity associated with high-degree Bézier curves is addressed, while simultaneously achieving smooth paths. Simulation results demonstrated that the proposed path planning algorithm exhibited fewer iterations, superior path quality, and path smoothness. Compared to other methods, the proposed approach demonstrated better overall performance and practical applicability.

Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments

In the development of intelligent mines, unmanned driving transportation has emerged as a key technology to reduce human involvement and enable unmanned operations. The operation of unmanned vehicles in mining environments relies on remote operation, which necessitates the low-latency transmission of high-definition video data across multiple channels for comprehensive monitoring and precise remote control. To address the challenges associated with unmanned driving in mines, we propose a comprehensive scheme that leverages the capabilities of 5G super uplink, edge collaborative computing, and advanced video transmission strategies. This approach utilizes dual-frequency bands, specifically 3.5 GHz and 2.1 GHz, within the 5G super uplink framework to establish an infrastructure designed for high-bandwidth and low-latency information transmission, crucial for real-time autonomous operations. To overcome limitations due to computational resources at terminal devices, our scheme incorporates task offloading and edge computing methodologies to effectively reduce latency and enhance decision-making speed for real-time autonomous activities. Additionally, to consolidate the benefits of low latency, we implement several video transmission strategies, such as optimized network usage, service-specific wireless channel identification, and dynamic frame allocation. An experimental evaluation demonstrates that our approach achieves an uplink peak rate of 418.5 Mbps with an average latency of 18.3 ms during the parallel transmission of seven channels of 4K video, meeting the stringent requirements for remote control of unmanned mining vehicles.

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

China has made notable advancements in the intelligent construction of coal mines. However, for longwall top coal caving (LTCC) mining faces, a key obstacle impeding the intelligent transition of the coal-cutting process is automated control. This paper focuses on the aforementioned issue and comprehensively considers the pre-, intra-, and post-coal-caving stages. In this work, diverse detection and monitoring technologies are integrated at various stages through a computer platform, facilitating the construction of an automated coal caving control system with self-perception, self-learning, self-decision-making, and self-execution capabilities. Key technologies include ground-penetrating radar-based top coal thickness detection, inertial navigation-based shearer positioning, tail beam vibration-based identification of coal and gangue, and magnetostrictive sensor-based monitoring of the tail beam and insert plate attitude. In this study, the 12309 working face of the Wangjialing Coal Mine was experimentally validated, and the efficacy of the aforementioned key technologies was assessed. The results demonstrated that the control requirements for automated coal caving are satisfied by the maximum errors. Automatic regulation of coal caving was realized through the implementation of this system, thereby facilitating initiation and cessation and yielding promising experimental outcomes. Overall, this system offers practical insights for intelligent construction in current LTCC mining faces and the sustainable development of coal resources.

Comprehensive Insights into Potassium‐Ion Capacitors: Mechanisms, Materials, Devices and Future Perspectives

AbstractAlkali metal‐ion capacitors integrate two electrodes from both batteries and supercapacitors (SCs), combining the advantages of large capacity, high‐rate performance, and long cycle life. Potassium (K) has similar properties to sodium (Na) and lithium (Li), however, the abundance of K in the crust is the same with Na, and much higher than Li. Due to the fast kinetics and low self‐discharge of Potassium‐ion capacitors (PICs), PICs attract more interest from researchers in the field of electrochemical energy storage. The current dilemma is that the research on PICs is more inherited from sodium‐ion capacitors (SICs) and lithium‐ion capacitors (LICs). Despite advancements in electrode materials, there is still a lack of profound understanding of the intrinsic issues and key challenges of PICs. In order to provide a detailed and systematic analysis of the development of PICs, in this review, special attention is given on the following Accordingly, full eight key sections: i) development history, ii) defining equations, iii) energy storage mechanism, iv) device configuration, v) electrode materials, vi) electrolyte design, vii) key technologies, and viii) future perspectives. This review provides an intensive theoretical foundation for the development of PICs and is able to pave the path for the practical application of PICs.

Extracting Pulmonary Nodules and Nodule Characteristics from Radiology Reports of Lung Cancer Screening Patients Using Transformer Models

Abstract Pulmonary nodules and nodule characteristics are important indicators of lung nodule malignancy. However, nodule information is often documented as free text in clinical narratives such as radiology reports in electronic health record systems. Natural language processing (NLP) is the key technology to extract and standardize patient information from radiology reports into structured data elements. This study aimed to develop an NLP system using state-of-the-art transformer models to extract pulmonary nodules and associated nodule characteristics from radiology reports. We identified a cohort of 3080 patients who underwent LDCT at the University of Florida health system and collected their radiology reports. We manually annotated 394 reports as the gold standard. We explored eight pretrained transformer models from three transformer architectures including bidirectional encoder representations from transformers (BERT), robustly optimized BERT approach (RoBERTa), and A Lite BERT (ALBERT), for clinical concept extraction, relation identification, and negation detection. We examined general transformer models pretrained using general English corpora, transformer models fine-tuned using a clinical corpus, and a large clinical transformer model, GatorTron, which was trained from scratch using 90 billion words of clinical text. We compared transformer models with two baseline models including a recurrent neural network implemented using bidirectional long short-term memory with a conditional random fields layer and support vector machines. RoBERTa-mimic achieved the best F1-score of 0.9279 for nodule concept and nodule characteristics extraction. ALBERT-base and GatorTron achieved the best F1-score of 0.9737 in linking nodule characteristics to pulmonary nodules. Seven out of eight transformers achieved the best F1-score of 1.0000 for negation detection. Our end-to-end system achieved an overall F1-score of 0.8869. This study demonstrated the advantage of state-of-the-art transformer models for pulmonary nodule information extraction from radiology reports.

Lightweight-Improved YOLOv5s Model for Grape Fruit and Stem Recognition

Mechanized harvesting is the key technology to solving the high cost and low efficiency of manual harvesting, and the key to realizing mechanized harvesting lies in the accurate and fast identification and localization of targets. In this paper, a lightweight YOLOv5s model is improved for efficiently identifying grape fruits and stems. On the one hand, it improves the CSP module in YOLOv5s using the Ghost module, reducing model parameters through ghost feature maps and cost-effective linear operations. On the other hand, it replaces traditional convolutions with deep convolutions to further reduce the model’s computational load. The model is trained on datasets under different environments (normal light, low light, strong light, noise) to enhance the model’s generalization and robustness. The model is applied to the recognition of grape fruits and stems, and the experimental results show that the overall accuracy, recall rate, mAP, and F1 score of the model are 96.8%, 97.7%, 98.6%, and 97.2% respectively. The average detection time on a GPU is 4.5 ms, with a frame rate of 221 FPS, and the weight size generated during training is 5.8 MB. Compared to the original YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x models under the specific orchard environment of a grape greenhouse, the proposed model improves accuracy by 1%, decreases the recall rate by 0.2%, increases the F1 score by 0.4%, and maintains the same mAP. In terms of weight size, it is reduced by 61.1% compared to the original model, and is only 1.8% and 5.5% of the Faster-RCNN and SSD models, respectively. The FPS is increased by 43.5% compared to the original model, and is 11.05 times and 8.84 times that of the Faster-RCNN and SSD models, respectively. On a CPU, the average detection time is 23.9 ms, with a frame rate of 41.9 FPS, representing a 31% improvement over the original model. The test results demonstrate that the lightweight-improved YOLOv5s model proposed in the study, while maintaining accuracy, significantly reduces the model size, enhances recognition speed, and can provide fast and accurate identification and localization for robotic harvesting.

Long-Term Energy System Modelling for a Clean Energy Transition in Egypt’s Energy Sector

Egypt has the potential to generate a significant amount of energy from renewable technologies, in particular solar PV, concentrated solar power (CSP), and onshore and offshore wind. The energy sector is reliant on fossil fuels, particularly natural gas, for electricity production and is at risk of locking itself into a high carbon pathway. Globally, reducing greenhouse gas (GHG) emissions associated with national energy sectors is a target outlined in the UN’s Paris Agreement. To reduce carbon dioxide (CO2) emissions associated with a higher dependence on fossil fuels, Egypt must consider upscaling renewable energy technologies (RETs) to achieve a clean energy transition (CET). This research modelled six scenarios using clicSAND for OSeMOSYS to identify the technologies and policy target improvements that are needed to upscale RETs within Egypt’s energy sector. The results showed that solar PV and onshore wind are key technologies to be upscaled to contribute towards Egypt’s CET. The optimal renewable target is the International Renewable Energy Agency’s (IRENA) target of 53% of electricity being sourced from RETs by 2030, which will cost USD 16.4 billion more up to 2035 than Egypt’s current Integrated Sustainable Energy Strategy (ISES) target of 42% by 2035; it also saves 732.0 MtCO2 over the entire modelling period to 2070. Socio-economic barriers to this transition are considered, such as recent discoveries of natural gas reserves combined with a history of energy insecurity, political instability impacting investor confidence, and a lack of international climate funding. The paper concludes with policy recommendations that would enable Egypt to progress towards achieving a CET.

Artificial intelligence technologies in information and library systems

The effectiveness of innovative scientific and educational activities largely relies on the technologies of the library industry, including modern information and library systems. Academic and research libraries introduce new technologies in an effort to improve the quality of their services. Artificial intelligence is one of the key technologies for digital transformation of the library industry. The study is aimed at identifying trends in the use of artificial intelligence in information and library service technologies. The author applied the general dialectical method, theoretical analysis and generalization of the content of special, scientific and technical literature, regulatory documents in the field of digitalization of librarianship and artificial intelligence. The results of the study have revealed the role of the integration of artificial intelligence technologies into information and library systems, which lies in the fact that they contribute to the formation of national intellectual libraries, computerization of the routine library services, and structuring innovative academic digital space for the users. Using the example of integration of context awareness technology into some information and library systems, the ability of a digital system to make “reasonable” decisions to extract relevant information for learning and self-education is demonstrated, which also allows to find educational materials on the topic being studied, and to build explicit educational route for a user.

A Human–Machine Interaction Mechanism: Additive Manufacturing for Industry 5.0—Design and Management

Industry 5.0 is an emerging value-driven manufacturing model in which human–machine interface-oriented intelligent manufacturing is one of the core concepts. Based on the theoretical human–cyber–physical system (HCPS), a reference framework for human–machine collaborative additive manufacturing for Industry 5.0 is proposed. This framework establishes a three-level product–economy–ecology model and explains the basic concept of human–machine collaborative additive manufacturing by considering the intrinsic characteristics and functional evolution of additive manufacturing technology. Key enabling technologies for product development process design are discussed, including the Internet of Things (IoT), artificial intelligence (AI), digital twin (DT) technology, extended reality, and intelligent materials. Additionally, the typical applications of human–machine collaborative additive manufacturing in the product, economic, and ecological layers are discussed, including personalized product design, interactive manufacturing, human–machine interaction (HMI) technology for the process chain, collaborative design, distributed manufacturing, and energy conservation and emission reductions. By developing the theory of the HCPS, for the first time its core concepts, key technologies, and typical scenarios are systematically elaborated to promote the transformation of additive manufacturing towards the Industry 5.0 paradigm of human–machine collaboration and to better meet the personalized needs of users.

EXPRESS: Community Gifting and Recipients Engagement with Peers and Influencers: Evidence from Natural Experiments on Twitch

Community gifting, the phenomenon of donating digital goods to peers without selecting specific recipients, is one of live streaming's key social technologies for engaging online communities. In this study, we investigate the causal relationship between receiving a community gift and the recipient's subsequent social and monetary engagement behaviors by exploiting the randomization of recipient assignment on the popular live streaming platform, Twitch. We find that, relative to nonrecipients within a five-minute window of a community gifting event, community gift recipients exhibit a 69% chance of directing an additional message toward their peers, a 35% chance of directing an additional message toward the streamer and a 5% chance of gifting an incremental subscription to the community. However, recipients are no more likely to tip the streamer. We apply computational linguistics methods to illustrate that recipients' increased social engagement is accompanied by elevated sentiment and an increased likelihood of joining existing conversations. Finally, we conduct a series of moderator analyses, and find that recipients' gifting behavior is less frequent when there are more gifting events prior to the focal community gift, i.e., allowing the recipient to hide. Moreover, the social engagement effect of receiving a community gift is greater when prior chatter is more voluminous and discontinuous, i.e., when it is easier for the recipient to jump into the chat. We conclude that the spillovers to social engagement are more important than those to financial reciprocity given the positive feedback loop implied by our moderator analysis. Our results reveal how and when community gifting impacts audience engagement.

"Research on the Application of Visible Light Communication in IoT System Configuration"

The growing need for seamless connectivity on the internet of things (IoT) paradigm has fueled the development of innovative technologies. This research deeply focuses on the integration of visible light communications (VLC) to configure the wireless sensor network (WSN) nodes in an IoT system. VLC, an emerging technology that uses visible light to transmit data, is not limited in frequency and maximizes security in wireless data transmission. Furthermore, due to the use of light-emitting diodes (LEDs) in almost every aspect of our daily lives, VLC is providing massive connectivity for various types of large-scale IoT communications, from machine to machine, vehicle to infrastructure, infrastructure-to-vehicle, chip-to-chip as well as device to device. This article presents an investigation of the multifaceted applications of VLC, especially its role in configuring wireless fidelity Wi-Fi connectivity for smart devices and their location in the system within the IoT framework. In addition to conventional data transmission capabilities, VLC has emerged as a key technology for transmitting configuration data and location data, thereby supporting an accurate positioning system. This study shows the effective interaction between VLC and IoT, shedding light on the applications of VLC in IoT systems, especially in smart building

Research on key technology of cooled infrared bionic compound eye camera based on small lens array

Traditional 2D imaging technologies are limited by the need for a large field of view and their sensitivity to small target motion. Inspired by the characteristics of insect compound eye structure, we propose an infrared bionic compound eye camera based on a small lens array. The camera is composed of 61 small lens arrays mounted on a curved spherical shell and a relay optical system. The imaging device is a high-performance cooled mid-wave infrared detector. This is an innovative design for an infrared biomimetic compound eye camera system that provides a wide field of view and all-day detection capability. Aimed to meet the specified requirements. The optical system achieves a 100% cold-membrane match between the infrared optical system and the cooled detector, and the relay optical system optimizes the large-field aberration by introducing a higher-order aspheric surface and modifying the geometric surface of the lenses. Our entire system enables an observation field angle of 108∘×108∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$108^\\circ \ imes 108^\\circ$$\\end{document}. The experiments showed that the image quality of the system is high, each ommatidium was effective within the imaging range of the compound eye camera, resulting in an improved signal-to-noise ratio in various scenes.

Application and Research of Key Technologies of Big Data for Agriculture

With the rapid development of science and technology, advanced technical means such as information technology have been widely used in various fields of society, realizing the reform and innovation in different fields. Mage data technology, Internet, cloud computing technology and so on have changed people's production and lifestyle, and society has entered a new era of mage data. Combined with the current situation of agricultural development, we should create a mage data training base for enterprises, build a perfect training system, carry out cooperation with colleges and universities, vigorously introduce professional talents, and spare no effort to promote the effective application of mage data technology in the agricultural field. This paper analyzes the importance of the application of mage data technology in the agricultural field, mage data mining analysis provides decision support, and the specific application of mage data technology in the agricultural field. Finally, through k-means algorithm, the industrial scale can be increased to more than 75%.