Intelligent Construction Monitoring Systems

Towards automated greenhouse: A state of the art review on greenhouse monitoring methods and technologies based on internet of things

This paper designs an intelligent greenhouse monitoring system to achieve the purpose of intelligent and precise acquisition, storage, and transmission of various data.The main control chip of the system is the STM32F103C8T6 minimum system board, which uses soil moisture sensors, photoresistors, temperature and humidity sensors, raindrop sensors, CO2 sensors, and other sensors to collect data.An OLED display shows the monitored data on site, and an ESP8266 module transmits the data to the ONENET cloud platform via Wi-Fi.Then, an abnormal indoor parameter generates a high-level trigger to activate the buzzer alarm.After physical testing, the intelligent monitoring system can achieve intelligent monitoring of various environmental data such as indoor and outdoor temperature and humidity, light intensity, and air quality.

Intelligent field monitoring system for cruciferous vegetable pests using yellow sticky trap images and an improved Cascade R-CNN

In today's rapidly advancing information technology landscape, the Internet of Things (IoT) has profoundly impacted traditional agriculture. IoT technology, as an intelligent solution, enables real-time data collection, processing, and analysis, significantly enhancing the efficiency and intelligence of agricultural production. Among these advancements, the neural network-based intelligent agricultural monitoring system stands out. This system not only monitors the agricultural production environment in real-time but also employs deep learning algorithms to predict future trends, guiding agricultural practices to achieve precision farming. Therefore, exploring the intelligent agricultural monitoring and control system based on neural networks in the IoT environment holds significant importance.

In order to better ensure the effective operation of substation intelligent auxiliary monitoring system, aiming at the problems of slow data transmission speed and poor data processing security of current monitoring system, a high-speed data transmission method of the substation intelligent auxiliary monitoring system is proposed. By constructing the data processing platform of substation intelligent auxiliary monitoring system, the data transmission algorithm of auxiliary monitoring system is optimized, reasonably select the optimal path of data transmission and conduct security encryption processing, so as to improve the high-speed data transmission efficiency of substation intelligent auxiliary monitoring system. Finally, the experiment shows that the high-speed data transmission method of the substation intelligent auxiliary monitoring system has higher practicability in the process of practical application and fully meets the research requirements.

Creating intelligent information processing systems for monitoring complex technical objects and systems is an important task today. This paper presents the principles and models for creating systems of this class. The decision-making process in intelligent information-measuring monitoring systems is described. The article presents a model of intelligent monitoring system and a model of decision-making in intelligent monitoring systems. The presented model of decision-making in intelligent monitoring systems allows estimating the efficiency of their functioning. The article proposes a structural scheme reflecting the sequence of decision-making tasks.

The purpose of the project is to develop an Intelligent Neonatal Monitoring System based on temperature and pulse rate data. In the Neonatal Intensive Care Unit (NICU), there are premature babies and other ill babies who need extra care from the doctors, nurses as well as medical supplies. Therefore, an intelligent neonatal monitoring system should be a good solution in order to help them to observe neonates frequently and consistently. This system transmits the vital signs of the neonate such as body temperature and pulse rate to the Internet of Things (IoT) called ThingSpeak. The body temperature and the pulse rate will be detected by LM35 temperature sensor and pulse sensor respectively. These information will be sent to the IoT via ESP8266 Wi-Fi Shield. IoT helps the doctors and nurses to be connected with the neonate's vital signs and it is helpful in monitoring the neonates at anytime and anywhere through the internet. The percentage difference between LM35 temperature sensor and digital thermometer is less than 3% while the pulse rate can be varied according to the physical activity. This develops system will providing efficiency and reliability which will play a vital role for better care.

Biopharma Is Going Digital … Bit by Bit

The integration of artificial intelligence (AI) with cardiac implantable electronic devices (CIEDs) has revolutionized remote monitoring, predictive diagnostics, and personalized patient care. Traditional device management relies on periodic clinical evaluations, which may lead to delayed detection of malfunctions or performance degradation. AI-driven analytics, in combination with Internet of Things (IoT) and cloud computing, enable real-time assessment of electrophysiological signals, device telemetry, and patient-specific risk factors. Machine learning and deep learning models enhance the accuracy of anomaly detection, reducing false positives and false negatives in ECG interpretation. Hybrid AI frameworks combining rule-based and neural network architectures improve predictive maintenance, ensuring timely interventions and extending device longevity. Blockchain technology strengthens data security by establishing a decentralized, immutable ledger for managing sensitive patient information and device performance metrics. The integration of AI, IoT, and blockchain in cardiac device monitoring fosters a secure, transparent, and efficient healthcare ecosystem, minimizing clinical workload while optimizing patient outcomes. This chapter explores advanced AI methodologies, predictive analytics, and cybersecurity frameworks that enhance the reliability, efficiency, and security of AI-enabled cardiac device management. Future advancements in AI-driven diagnostics and regulatory considerations will be critical in achieving widespread clinical adoption of intelligent cardiac monitoring systems.

Aiming at the increasingly complex management and dispatching problems of power system, this paper combines Beidou precise space-time technology with digital twin system theory, and puts forward a method of constructing intelligent management and dispatching platform of power grid. The method combines Beidou and IoT (Internet of Things) sensors to form a digital twin management and scheduling platform which can perform state prediction, fault diagnosis, self-healing and other functions. Firstly, this paper analyzes the overall architecture of Beidou digital power twin system, then discusses the construction method of digital twin in power system from the aspects of digital twin modeling theory, data acquisition intelligent terminal based on Beidou, operation mode of digital twin platform, etc. Finally, the application test analysis is carried out. The research results show that the method proposed in this paper has reference value for improving the management and dispatching capability of smart grid.

In this paper, a novel intelligent monitoring system based on digital twin framework is developed for spacecraft vacuum thermal test. More precisely, the corresponding digital twin model mapping of real-world spacecraft vacuum thermal test is constructed by hierarchy design scheme with robustness. Then, in order to reliably obtain all the dynamical data during the entire vacuum thermal testing processes, the monitoring system is designed with hardware equipment and software integration. As a result, the testing data analysis, fault detection and decision optimization for spacecraft vacuum thermal test can be achieved, which can effectively save the implementation cost and improve the testing efficiency. In the end, the practical experiment case is presented for showing the usefulness of the designed intelligent monitoring system.

Combined with IoT(Internet of Things) network communication technology, sensor technology and cloud computing technology, the existing intelligent environment monitoring system is improved, and a design of intelligent environment monitoring visualization platform based on Internet of things is given. Through this system, the data information of meteorological environment, water quality and main roads can be obtained timely and accurately. Through the development and implementation of system engineering, the feasibility of intelligent environment monitoring visualization platform based on Internet of things is verified.

Intelligent battery management system in a smart farm has a very significant role for energy management. It gives the energy demand status of the entire farm, the available energy produced by the PV system, and the energy stored in the batteries. However, it is important that energy monitoring system to be efficient, most especially in optimizing the distribution of the available harvested energy. Also, since the loads will come from various systems such as aquaponics, vision system, irrigation system and communication systems, efficient system is a necessity. Therefore, it is necessary to have an intelligent monitoring system that will acquire all the possible energy demands of each respective system loads. With the aid of smart farming technology, accurate energy monitoring and distribution is made possible. To properly manage the use of energy harvested for smart farm system loads, this study discusses the design of a fuzzy logic-based intelligent battery management monitoring system (FLi-BMS) to optimize the energy distribution to several loads in the smart farm.

In the face of escalating demands for sustainable agriculture, this study introduces an innovative approach by deploying an intelligent monitoring and management system that utilises Internet of Things (IoT) sensors and machine learning algorithms. Focused on enhancing the precision of irrigation and fertilisation in farming, the system collects realtime data on soil moisture, temperature, and other vital parameters. A predictive random forest model, trained on historical crop data and current environmental conditions, analyses this data to accurately forecast water and fertiliser requirements. The model demonstrated an 87.4% accuracy for predicting irrigation needs and 85.7% for fertilisation, significantly optimising resource use and reducing environmental impact. The findings reveal that such technologies promise to revolutionise agricultural practices by making them more efficient and sustainable. They also highlight the challenges in their adoption, including the need for initial investment and overcoming the digital divide. This research underscores the potential of IoT and machine learning in achieving precision agriculture, marking a crucial step towards sustainable farming solutions that cater to the growing global food demands while preserving environmental resources.

INTRODUCTION. The increasing complexity of construction projects and growing competitive pressure in the industry have created a critical need to enhance planning methods and resour ce allocation in construction management. RESEARCH HYPOTHESIS. The introduction of integrated digital technologies, such as digital twins, artificial intelligence, the Internet of Things, and advanced planning methods, will significantly increase the efficiency of construction process planning, reduce costs and project execution time, while reducing risks and improving forecasting accuracy. THE AIM The purpose of this study was to develop a methodology for improving the efficiency of construction process planning through the integration of digital technologies and algorithmic optimisation methods. METHODS. The study was based on an analysis of the implementation of digital planning methods in seven leading international construction companies in 2023–2024, using data from technical documentation for deployed systems and financial statements. FINDINGS. The adoption of integrated digital platforms reduced planning time by 45–52% and minimised equipment downtime by 38–40%. The use of graph databases optimised the sequencing of construction operations, while predictive analytics improved forecasting accuracy for work deadlines to 92%. The integration of artificial intelligence (AI) and Internet of Things (IoT) technologies lowered logistics costs by 25–33% and reduced modelling (BIM) technologies, combined with collaborative planning systems, increased productivity by 25% and reduced schedule deviations by 32%. Furthermore, the development of a digital twin system enabled process modelling at the planning stage, facilitating early detection of potential issues and reducing error correction costs by 28%. The application of predictive analytics allowed a shift from reactive to proactive resource management through risk forecasting and automatic plan adjustments. CONCLUSION. The proposed phased methodology for digital solution implementation, incorporating a technology audit and personnel development programmes, can be applied by construction companies of various sizes to optimise planning processes and enhance operational efficiency. KEYWORDS: digital twins; intelligent systems; graph databases; manufacturing processes; collaborative planning.