Sensor Data Acquisition Research Articles

Sustainability in Industry 4.0: Edge Computing Microservices as a New Approach

The importance of the electronics sector in the modern world is unquestionable, as it demonstrates clean technology, dry processes, and efficient design, which favor Industry 4.0 and sustainability. Nonetheless, the large number of instruments developed, and their correspondent quick obsolescence, imply an increment in electronic waste. Therefore, in this work, with the aim of diminishing obsolescence, we developed and customized one application that runs independently of systems and takes advantage of the existing computing structures. The application is a new edge computing structure (the AIFC) that is based on an enterprise service bus (ESB) developed in decentralized microservices. In this study, we conducted action research involving the collaboration of researchers and practitioners, and the tests involved six different scenarios; they used existing low-cost, basic computing environments and ranged from the proof of concept, prototype, minimum viable product, and scalability to the roadmap for the structure implementation. The six scenarios emulated sections of a small and medium-sized enterprise (SME), and all the developed microservices communicate with each other to perform data filtering, processing, storage, query, and sensor data acquisition. The results show that it is possible to carry out these functions with low latency and without any decrement or even increase in performance when compared with more conventional cloud computing structures, and it is also possible to manipulate different products that do not have single, consolidated structures. Moreover, there is no need to update machines or communication structures, which are the main factors of rapid obsolescence. Therefore, following the steps of the AIFC development, the results from the proof of concept to the minimum viable product and scalability tests correspond to a roadmap for a sustainable solution and are an important tool for both Industry 4.0 and SMEs.

Geo‐spatial traffic behaviour analysis and anomaly detection for ITS applications

AbstractUnderstanding the behaviour of traffic participants within the geo‐spatial context of road/intersection topology is a vital prerequisite for any smart ITS application. This article presents a video‐based traffic analysis and anomaly detection system covering the complete data processing pipeline, including sensor data acquisition, analysis, and digital twin reconstruction. The system solves the challenge of geo‐spatial mapping of captured visual data onto the road/intersection topology by semantic analysis of aerial data. Additionally, the automated camera calibration component enables instant camera pose estimation to map traffic agents onto the road/intersection surface accurately. A novel aspect is approaching the anomaly detection problem by AI analysis of both the spatio‐temporal visual clues and the geo‐spatial trajectories for all type of traffic participants, such as pedestrians, bicyclists, and vehicles. This enables recognition of anomalies related to either traffic‐rule violations, for example, jaywalking, improper turns, zig‐zag driving, unlawful stops, or behavioural anomalies: littering, accidents, falling, vandalism, violence, infrastructure collapse etc. The method achieves leading anomaly detection results on benchmark datasets World Cup 2014, UCF‐Crime, XD‐Violence, and ShanghaiTech. All the obtained results are streamed and rendered in real‐time by the developed TGX digital twin visualizer. The complete system has been deployed and validated on the roads of Helmond town in The Netherlands.

A Hyperspectral Reflectance Database of Plastic Debris with Different Fractional Abundance in River Systems

Plastic debris pollution transported by river systems to lakes and oceans has emerged as a significant environmental concern with adverse impacts on ecosystems, food webs, and human health. Remote sensing presents a cost-effective approach to bolster interception and removal efforts. However, unlike marine environments, the optical properties of plastic debris in fresh waters remain poorly understood. This study aims to address this gap by providing an open-access hyperspectral reflectance database of floating weathered and virgin plastic debris found in river systems under controlled laboratory experiments. Utilizing natural waters from the Mississippi River, the database was assembled using a remote sensing data acquisition system deployed over a hydraulic flume operating under subcritical flow conditions and varying suspended sediment concentrations. The measurements encompass hyperspectral diffused light reflectance from ultraviolet (UV, 350 nm) to shortwave infrared (SWIR, 2500 nm) wavelengths. The database archived in Network Common Data Form (NetCDF) and Comma-separated values (CSV), offers valuable insights for better understanding key spectral signatures indicative of floating plastic debris, with different fractional abundance, in freshwater ecosystems.

Laser Scan Compression for Rail Inspection

The automation of rail track inspection addresses key issues in railway transportation, notably reducing maintenance costs and improving safety. However, it presents numerous technical challenges, including sensor selection, calibration, data acquisition, defect detection, and storage. This paper introduces a compression method tailored for laser triangulation scanners, which are crucial for scanning the entire rail track, including the rails, rail fasteners, sleepers, and ballast, and capturing rail profiles for geometry measurement. The compression technique capitalizes on the regularity of rail track data and the sensors’ limited measurement range and resolution. By transforming scans, they can be stored using widely available image compression formats, such as PNG. This method achieved a compression ratio of 7.5 for rail scans used in the rail geometry computation and maintained rail gauge reproducibility. For the scans employed in defect detection, a compression ratio of 5.6 was attained without visibly compromising the scan quality. Lossless compression resulted in compression ratios of 5.1 for the rail geometry computation scans and 3.8 for the rail track inspection scans.

Design and Experimental Test of Rope-Driven Force Sensing Flexible Gripper.

Robotic grasping is a common operation scenario in industry and agriculture, in which the force sensing function is a significant factor to achieve reliable grasping. Existing force sensing methods of flexible grippers require intelligent materials or force sensors embedded in the flexible gripper, which causes such problems of higher manufacturing requirements and contact surface properties changing. In this paper, a novel rope-driven force sensing flexible gripper is designed based on the fin-shaped gripper structure, which can realize the grasping sensing functions of contact nodes and contact forces without the need for force sensors. Firstly, the rope-driven force sensing flexible gripper is designed, including the driving unit, the transmission part, the gripper unit, and the force sensing unit. The force sensing unit and the gripper unit are connected by rope, and the prototype of the rope-driven force sensing flexible gripper is completed. Secondly, a force sensing algorithm and control system based on finite element method and grasping geometric relationship are designed to realize the rope-driven force sensing flexible gripper grasping control and sensor data acquisition and processing. Finally, the experimental system of the rope-driven force sensing flexible gripper is built, and the grasping experimental tests of objects with different diameters and different contact nodes are carried out to verify the force sensing function of the rope-driven force sensing flexible gripper. The force sensing flexible gripper designed in this paper can provide a new idea for the design and force sensing method of intelligent robotic grasping system in robotic teaching, scientific research, and industrial applications.

UCFTNet: multi-class dtection of city critical disaster information based on U-shaped cross fusion transformer

ABSTRACT With the continuous enhancement of remote sensing data acquisition capabilities, the demand for efficient extraction of multi-class critical urban disaster information has become increasingly urgent. However, current deep learning-based models primarily focus on single disaster objects and struggle with accuracy in complex post-disaster scenarios. To address this, we propose UCFTNet, a semantic segmentation model for urban post-disaster damaged roads and buildings. UCFTNet employs a U-shaped encoder-decoder structure and ConvNext modules, integrating spatial and multi-scale information of damaged buildings and roads from different scales and channels. Additionally, the decoder uses the CCT module and CFF module to merge multi-scale features and eliminate irrelevant background interference. Finally, a weighted loss function is designed to enhance the model's focus on disaster category pixels. Experimental results demonstrate that UCFTNet improves the accuracy of extracting damaged buildings and roads by at least 5% across three urban disaster datasets, confirming its effectiveness in post-disaster damage information extraction.

A low-power consumption security data fusion model for Industrial Internet of Things

Industrial Internet of Things (IIOT) is an important part of manufacturing industry in the future, and the front-end sensor data acquisition network is the core component of it. Due to the limited energy of IIOT, reducing transmission interaction, energy consumption, and computational complexity in the process of data transmission is the key and difficult point in designing model of IIOT. In this paper, a lightweight green security data fusion model for IIOT is proposed, called LCSDF model, which aims to achieve data security and integrity verifiability by using less energy consumption. The simulation results show that the proposed model can ensure data security and verifiability, and meet the energy consumption requirements of the system. The performance of the algorithm is also satisfactory, reflecting the lightweight, green, and security of the model.

CyberCortex.AI: An AI‐based operating system for autonomous robotics and complex automation

AbstractThe underlying framework for controlling autonomous robots and complex automation applications is Operating Systems (OS) capable of scheduling perception‐and‐control tasks, as well as providing real‐time data communication to other robotic peers and remote cloud computers. In this paper, we introduce CyberCortex.AI, a robotics OS designed to enable heterogeneous AI‐based robotics and complex automation applications. CyberCortex.AI is a decentralized distributed OS which enables robots to talk to each other, as well as to High Performance Computers (HPC) in the cloud. Sensory and control data from the robots is streamed toward HPC systems with the purpose of training AI algorithms, which are afterwards deployed on the robots. Each functionality of a robot (e.g., sensory data acquisition, path planning, motion control, etc.) is executed within a so‐called DataBlock of Filters shared through the internet, where each filter is computed either locally on the robot itself or remotely on a different robotic system. The data is stored and accessed via a so‐called Temporal Addressable Memory (TAM), which acts as a gateway between each filter's input and output. CyberCortex.AI has two main components: (i) the CyberCortex.AI.inference system, which is a real‐time implementation of the DataBlock running on the robots' embedded hardware, and (ii) the CyberCortex.AI.dojo, which runs on an HPC computer in the cloud, and it is used to design, train and deploy AI algorithms. We present a quantitative and qualitative performance analysis of the proposed approach using two collaborative robotics applications: (i) a forest fires prevention system based on an Unitree A1 legged robot and an Anafi Parrot 4K drone, as well as (ii) an autonomous driving system which uses CyberCortex.AI for collaborative perception and motion control.

Smart Industrial Internet of Things Framework for Composites Manufacturing.

Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred to as an Artificial Intelligence of Things (AIoT) framework for composites manufacturing. This framework improves production performance through real-time process monitoring and AI-based forecasting. It comprises three main components: (i) an array of temperature, heat flux, dielectric, and flow sensors for data acquisition from production machines and products being made, (ii) an IoT-based platform for instantaneous sensor data integration and visualisation, and (iii) an AI-based model for production process forecasting. Via these components, the framework performs real-time production process monitoring, visualisation, and prediction of future process states. This paper also presents a proof-of-concept implementation of the framework and a real-world composites manufacturing case study that showcases its benefits.

Artificial Neural Network Model for Estimating the Pelton Turbine Shaft Power of a Micro-Hydropower Plant under Different Operating Conditions

The optimal performance of a hydroelectric power plant depends on accurate monitoring and well-functioning sensors for data acquisition. This study proposes the use of artificial neural networks (ANNs) to estimate the Pelton turbine shaft power of a 10 kW micro-hydropower plant. In the event of a failure of the sensor measuring the torque and/or rotational speed of the Pelton turbine shaft, the synthetic turbine shaft power data generated by the ANN will allow the turbine output power to be determined. The experimental data were obtained by varying the operating conditions of the micro-hydropower plant, including the variation of the input power to the electric generator and the variation of the injector opening. These changes consequently affected the flow rate and the pressure head at the turbine inlet. The use of artificial neural networks (ANNs) was deemed appropriate due to their ability to model complex relationships between input and output variables. The ANN structure comprised five input variables, fifteen neurons in a hidden layer and an output variable estimating the Pelton turbine power. During the training phase, algorithms such as Levenberg–Marquardt (L–M), Scaled Conjugate Gradient (SCG) and Bayesian were employed. The results indicated an error of 0.39% with L–M and 7% with SCG, with the latter under high-flow and -energy consumption conditions. This study demonstrates the effectiveness of artificial neural networks (ANNs) trained with the Levenberg–Marquardt (L–M) algorithm in estimating turbine shaft power. This contributes to improved performance and decision making in the event of a torque sensor failure.

Identifying factors impacting missingness within smartphone-based research: Implications for intensive longitudinal studies of adolescent suicidal thoughts and behaviors.

Intensive longitudinal research-including experience sampling and smartphone sensor monitoring-has potential for identifying proximal risk factors for psychopathology, including suicidal thoughts and behaviors (STB). Yet, missing data can complicate analysis and interpretation. This study aimed to address whether clinical and study design factors are associated with missing data and whether missingness predicts changes in symptom severity or STB. Adolescents ages 13- to 18 years old (N = 179) reporting depressive, anxiety, and/or substance use disorders were enrolled; 65% reported current suicidal ideation and 29% indicated a past-year attempt. Passively acquired smartphone sensor data (e.g., global positioning system, accelerometer, and keyboard inputs), daily mood surveys, and weekly suicidal ideation surveys were collected during the 6-month study period using the effortless assessment research system smartphone app. First, acquisition of passive smartphone sensor data (with data on ∼80% of days across the whole sample) was strongly associated with survey data acquisition on the same day (∼44% of days). Second, STB and psychiatric symptoms were largely not associated with missing data. Rather, temporal features (e.g., length of time in study, weekends, and summer) explained more missingness of survey and passive smartphone sensor data. Last, within-participant changes in missing data over time neither followed nor predicted subsequent change in suicidal ideation and psychiatric symptoms. Findings indicate that considering technical and study design factors impacting missingness is critical and highlight several factors that should be addressed to maximize the validity of clinical interpretations in intensive longitudinal research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Enhanced Data Mining and Visualization of Sensory-Graph-Modeled Datasets through Summarization.

The acquisition, processing, mining, and visualization of sensory data for knowledge discovery and decision support has recently been a popular area of research and exploration. Its usefulness is paramount because of its relationship to the continuous involvement in the improvement of healthcare and other related disciplines. As a result of this, a huge amount of data have been collected and analyzed. These data are made available for the research community in various shapes and formats; their representation and study in the form of graphs or networks is also an area of research which many scholars are focused on. However, the large size of such graph datasets poses challenges in data mining and visualization. For example, knowledge discovery from the Bio-Mouse-Gene dataset, which has over 43 thousand nodes and 14.5 million edges, is a non-trivial job. In this regard, summarizing the large graphs provided is a useful alternative. Graph summarization aims to provide the efficient analysis of such complex and large-sized data; hence, it is a beneficial approach. During summarization, all the nodes that have similar structural properties are merged together. In doing so, traditional methods often overlook the importance of personalizing the summary, which would be helpful in highlighting certain targeted nodes. Personalized or context-specific scenarios require a more tailored approach for accurately capturing distinct patterns and trends. Hence, the concept of personalized graph summarization aims to acquire a concise depiction of the graph, emphasizing connections that are closer in proximity to a specific set of given target nodes. In this paper, we present a faster algorithm for the personalized graph summarization (PGS) problem, named IPGS; this has been designed to facilitate enhanced and effective data mining and visualization of datasets from various domains, including biosensors. Our objective is to obtain a similar compression ratio as the one provided by the state-of-the-art PGS algorithm, but in a faster manner. To achieve this, we improve the execution time of the current state-of-the-art approach by using weighted, locality-sensitive hashing, through experiments on eight large publicly available datasets. The experiments demonstrate the effectiveness and scalability of IPGS while providing a similar compression ratio to the state-of-the-art approach. In this way, our research contributes to the study and analysis of sensory datasets through the perspective of graph summarization. We have also presented a detailed study on the Bio-Mouse-Gene dataset, which was conducted to investigate the effectiveness of graph summarization in the domain of biosensors.

Leak and Burst Detection in Water Distribution Network Using Logic- and Machine Learning-Based Approaches

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems.

Computer-Vision-Oriented Adaptive Sampling in Compressive Sensing.

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems.

SHM System Architecture based on Ultrasonic Guided Waves for Aircraft Application Scenarios

Structural health monitoring (SHM) based on ultrasonic guided waves is a novel technology using permanently attached actuator and sensor networks, data acquisition and data evaluation systems to enable in-service inspection of aircraft structures. A modular and decentralized SHM system is presented that can be adapted to various aircraft application scenarios. The central element of the SHM system is a robust sensor array based on piezocomposite technology that can be efficiently integrated into the manufacturing process of CFRP structures (via co-bonding). The sensor array consists of integrated piezoceramic transducers, temperature sensors, electrical cables and integrated electronic components. The decentralized system architecture of the individual electronic components reduces the installation and cabling effort during the assembly of aircraft structures as well as the system weight. Integration and installation aspects as well as various system architectures are discussed using an Airbus A350 door surround structure. Furthermore, an outlook of damage detection under representative flight loads is given.

Review and Prospect of Research on Structural Health Monitoring Technology for Bridges

As a crucial infrastructure in the transport system, the safe operation of bridges is directly related to all aspects of people’s daily lives. The development of bridge structural health monitoring technology and its application play an important role in ensuring the safety and extending the service life of bridges. This paper carries out in-depth research and analysis on the related technology of bridge structural health monitoring. Firstly, the existing monitoring technologies at home and abroad are sorted out, and the advantages and problems of various methods are compared and analyzed, including nondestructive testing, stress measurement, vibration characteristic identification, and other commonly used monitoring technologies. Secondly, the key technologies and equipment in the bridge health monitoring system, such as sensor technology, data acquisition, and processing technology, are introduced in detail. Finally, the development trend in the field of bridge health monitoring is prospected from both theoretical research and technical application. In the future, with the development of emerging technologies such as big data, cloud computing, and the Internet of Things, it is expected that bridge health monitoring with intelligent and systematic features will be more widely applied to provide a stronger guarantee for the safe and efficient operation of bridges.

Model‐driven neural network based for HPO‐MIMO channel estimation

AbstractIntegrated Sensing and Communications (ISAC) need to process data streams in high‐speed sensor data acquisition or high‐speed wireless communications. To process the data can require more computing and communication resources, resulting in higher power consumption. Halved‐Phase Only Multiple Input Multiple Output (HPO‐MIMO) communication technology can solve this problem by using low‐power nonlinear detection devices. In ISAC, Channel Estimation (CE) technology can provide key channel characteristics and state information for sensing and collaborative work of perception and communication tasks. However, HPO‐MIMO system cannot realize CE using traditional receiver schemes because of the missing amplitude. In order to solve this problem, two HPO‐MIMO CE schemes based on model‐driven deep learning are proposed in this paper. The proposed schemes include a Densely Residual Network (DRN) and a Inception‐Resnet (IR), which is suitable for the case of sufficient data and insufficient data, respectively. The simulation results show that the performance of DRN based scheme is better than that of IR based scheme when the data amount is sufficient, and the performance of IR based scheme is better when the dataset is small. In addition, the proposed CE schemes work well with a range of antenna sizes and distances.

Advanced Traffic Light Controller using FPGA and ARDUINO

This study presents a novel approach to develop a traffic light controller using FPGA and Arduino platforms. The objective is to design a robust, adaptable, and efficient traffic management system. Methodology involves hardware integration of FPGA for real-time processing and Arduino for interfacing with sensors and actuators. The proposed system utilizes FPGA's parallel processing capabilities for rapid decision-making and Arduino's flexibility for sensor data acquisition and actuator control. Performance evaluation includes simulation and real-world testing to assess reliability, response time, and scalability. Results demonstrate the effectiveness of the proposed controller in optimizing traffic flow and improving intersection safety.

Theoretical Framework Construction of Remote Sensing Image Processing Technology in The Study of Urban Heat Island Effect

This study aims to construct a theoretical framework to discuss the application of remote sensing image processing technology in the study of urban heat island effect. Urban heat island effect refers to the phenomenon that the temperature of urban area is significantly higher than that of surrounding rural area, which has become an important problem in the process of global climate change and urbanization. Remote sensing technology provides a powerful tool for in-depth understanding and effective response to this phenomenon. This study first reviews the existing remote sensing image processing technologies, including multispectral and hyperspectral imaging, thermal infrared imaging, laser radar (LiDAR), and analyzes the application status of these technologies in the study of urban heat island effect. Then, this paper puts forward a comprehensive theoretical framework, combining the key steps of remote sensing data acquisition, image preprocessing, feature extraction, classification and segmentation, and change detection, systematically describes how to use remote sensing image processing technology to monitor and analyze urban heat island effect. In the research process, this paper adopts the methods of literature review and case analysis, and extracts the core elements and key technologies of remote sensing image processing technology in the study of urban heat island effect by systematically combing the relevant research results at home and abroad. The research results show that remote sensing image processing technology has significant advantages in the spatial distribution characteristics, time change rules, and analysis of influencing factors of urban heat island effect, and can provide high-precision and high-timeliness monitoring data, and provide scientific basis for urban planning and environmental management. The theoretical framework of this paper provides guidance for future empirical research, and provides a reference for policy makers and urban managers, which has important academic value and practical application significance.

Research and Development of an Embedded Multi-Channel Sensor Data Acquisition Device for Reservoir Dams

The embedded system of intelligent reservoir dam achieves the integration and efficient utilization of water conservancy dam system data through multi-channel data collection and analysis calculated by computer technology, CNC system, and neural network. Compared with traditional data collection and processing methods, both timeliness and accuracy have been greatly improved. This study aims to develop a multi-channel sensor data acquisition device for reservoir dams based on embedded system technology. This device can collect real-time and efficient data from sensors in various parts of the dam, ensuring the safe operation of the reservoir dam. By using advanced embedded system technology, this device has advantages such as low power consumption, high stability, and real-time data transmission. The Analytic Hierarchy Process (AHP) was used to study the embedded multi-channel sensor data acquisition device for reservoir dams in multiple directions and factors. The AHP method provides an effective means for problem decision-making in complex situations. Referring to the AHP method, the factors that affect reservoir dams can be divided into different levels. Compare the importance of two random factors in each level to obtain a specific quantitative expression of the relative important factors on a scale. Then repeat this step to obtain the weight ranking for different levels. At the same time, the device monitors key parameters such as temperature, humidity, displacement, and pressure in various parts of the dam through multiple sensors, providing strong support for early warning and decision-making of reservoir dams. The results of this study have important practical significance and application value for improving the safety and stability of reservoir dams.