Monitoring Method Research Articles

Error and Traffic State Analysis of Floating Car Data

Abstract. Floating car technology is widely applied in traffic information collection and has become a common method for real-time and dynamic monitoring of urban traffic conditions. Using floating car data from Shanghai as the research object, this paper analyzes the positioning error of floating cars and finds that most errors are within 15 meters. The error is significantly affected by vehicle speed but less by time, and the error decreases as the road grade increases. Speed and travel time ratio are used as parameters to assess the spatiotemporal traffic state, exploring the spatial distribution and temporal evolution of speed, as well as the spatiotemporal distribution of traffic congestion. In the spatial distribution of speed, clustering analysis is used to deduce the temporal distribution pattern of speed on the same road segment. Based on this, corresponding traffic management measures can be applied to different road segments and at different times.

A multi-year study of acoustic propagation and ambient sound in a Thalassia testudinum seagrass meadow in a shallow sub-tropical lagoona).

Seagrasses provide a multitude of ecosystem services and act as important carbon sinks. However, seagrass habitats are declining globally, and they are among the most threatened ecosystems on earth. For these reasons, long-term and continuous measurements of seagrass parameters are of primary importance for ecosystem health assessment and sustainable management. This paper presents results from both active and passive acoustical methods for ecosystem monitoring in seagrass meadows. From a propagation perspective, gas bodies contained within the seagrass tissue as well as photosynthetic-driven bubble production result in attenuation, dispersion, and scattering of sound that produce increased transmission loss. For the passive approach, the detachment of gas bubbles from the plants is an important component of the ambient soundscape. Examples of both techniques will be presented based on data collected as part of a two-year continuous deployment of an acoustical measurement system operating in a moderately dense seagrass bed dominated by Thalassia testudinum (turtle grass) in Corpus Christi Bay, Texas. The data show annual trends related to the seasonal growth pattern of Thalassia as well as diurnal trends correlated with photosynthetically active radiation.

Comparing Airborne and Along-Sidewall Ground-Based Measurements to Retrieve Thermal and PM10 Concentration Profiles in Two Alpine Valleys

Abstract The paper aims at investigating the effectiveness of estimating vertical profiles of air temperature and PM10 concentrations in Alpine valleys through ground stations positioned at different altitudes on one valley sidewall (i.e., pseudovertical profiles). Two case studies in the Italian Alps are investigated: Chiese Valley in Trentino Province and Camonica Valley in the Lombardy region. Vertical profiles of temperature and PM10 concentrations were derived from airborne measurements at the center of the two valleys by means of low-cost sensors installed on a drone during summer 2019 and a tethered balloon during winter 2020. At the same time, five stations, equipped with the same kind of low-cost sensors, simultaneously monitored the same variables on one mountain slope. Comparisons between pseudoprofiles and airborne soundings revealed that ground stations well approximated temperature and PM10 soundings during the night and early morning, while temperatures along the slopes were higher than in the center of the valley during daytime, due to solar radiative heating, with larger differences in summer than in winter. On the contrary, some episodes with PM10 concentrations slightly higher in the valley center than on the slope were recorded, due to transport events and upslope winds. Nonetheless, the pseudoprofiles based on slope ground measurements faithfully reproduced the vertical gradients of both air temperature and PM10 if compared to those assessed from the soundings performed at the center of the two valleys. Results show that pseudovertical profiles can be a reliable and inexpensive method for continuous monitoring of vertical air temperature and PM10 distribution in mountain valleys. Significance Statement The purpose of this study is to understand whether ground-based measurements of air temperature and particulate matter on mountain slopes can be used as suitable surrogates of vertical atmospheric soundings with tethered balloons or drones in mountain environment. This is important because balloon and drone soundings are expensive and regulative constrained. The knowledge of the vertical distribution of air temperature is crucial to predict the distribution of air pollutants in valleys, namely, the particulate matter emitted by wood burning. Our results showed that ground-based measurements on mountain slopes made with low-cost sensors can satisfactorily reproduce vertical gradients of air temperature and distribution of particulate matter in a reliable and inexpensive way.

The Core of the Matter-Importance of Identification Method and Biological Replication for Benthic Marine Monitoring.

Benthic macrofauna are important and widely used biological indicators of marine ecosystems as they have limited mobility and therefore integrate the effects of local environmental stressors over time. Recently, environmental DNA (eDNA) analysis has provided a potentially more resource-efficient approach for benthic biomonitoring than traditional morphology-based methods. Several studies have compared eDNA with morphology-based monitoring, but few have compared the two approaches using the exact same sediment cores. In addition, the meiofauna and pelagic organisms obtained as 'bycatch' using eDNA have largely been disregarded from comparisons. Here, we address these shortcomings through comparative invertebrate analyses of six sediment sample replicates from each of four stations in Denmark, using eDNA metabarcoding and morphological identification. Our results revealed large variation between the six replicates for both methods and little overlap in taxon compositions between methods. While the morphological dataset was dominated by molluscs and annelids, the eDNA dataset was dominated by arthropods and annelids. Using community composition data, we found that sampling stations could be distinguished both with eDNA and morphology. Finally, we inferred expected total richness from extrapolated accumulation curves of detected taxa from each method. This indicated that eDNA metabarcoding requires less replication than morphology for maximum coverage of diversity to be reached. However, both methods required high levels of replication, and our results on taxonomic composition add to the evidence that morphological and eDNA-based methods should preferably be used as complimentary tools for marine bioassessment.

Alternative Methods of Machine Online Condition Monitoring: Recommendations for Rotating Machines in the Petroleum and Chemical Industry

Alternative Methods of Machine Online Condition Monitoring: Recommendations for Rotating Machines in the Petroleum and Chemical Industry

Construction error control method of large-span spatial structures based on digital twin

In the process of construction of large-span spatial structures, there are many control elements, which need to carry on the dual control of internal force and shape. The traditional control method cannot measure the construction deviation completely and accurately, and there is a lag in information feedback. In this paper, spatio-temporal information is integrated into digital twin and a construction error control method is proposed for large-span spatial structures. By analyzing the fusion mechanism of spatio-temporal information and digital twin, a closed-loop control system for structural errors before, during and after construction is formed. Driven by the control architecture, the method of structure simulation analysis and real-time monitoring during construction is proposed, and the functional relationship between cable force and cable length is established. Based on point cloud data, construction error optimization detection method is proposed for structure formation state. The nonlinear relation between elevation change rate and cable force deviation is formed and the control measures of formation state error are obtained. The experimental model of cable truss structure is taken as an example to apply and verify the theoretical method. The construction error control twin platform is formed in the experiment. Driven by the platform, the twinning analysis and real-time monitoring of the construction process are carried out to control the error of the structure formation. The results show that this method integrates spatio-temporal information into digital twin to realize data integration processing. The optimized error detection method can save 30% time cost and effectively control the construction error within 3%. The integration of spatio-temporal information and digital twin provides theoretical and algorithm support for the intelligent management and control of large-span spatial structure construction.

Research on fatigue life of carbon fiber reinforced polymer strengthened single-sided butt welded joints utilizing resin pre-coating for strong adhesive bonding

Single-sided butt welding is a common connection method used in box beam structures such as excavator booms. Single-sided butt weld joints are prone to fatigue failure since the structures are subjected to cyclic loads over extended periods. The purpose of this study is to improve the fatigue performance of single-sided butt welded joints. Carbon fiber reinforced polymer (CFRP) strengthened single-sided butt welded joint specimens with permanent backing plates were prepared by pre-coating method, and constant-amplitude tension–tension fatigue tests were carried out on the specimens. The effects of welding groove parameters and CFRP reinforcement on fatigue life were studied. The test results show that the CFRP-strengthened specimens without blunt edges and with a groove angle of 40° show the highest average fatigue life. The single-sided bonding of 10 layers of CFRP sheets effectively inhibits the propagation of fatigue cracks and significantly improves the fatigue life of welded joints. In the CFRP/welded joint composite structure, the strain monitoring results show that the peak strain of the interface is the largest in the middle position, and the peak strain rises sharply in the rapid crack propagation stage, which indicates that the real-time interface strain peak can reflect the fatigue crack propagation. It provides an early warning method for real-time monitoring of fatigue life failure of welded structures in practical engineering.

Real-time modeling of transient crustal deformation through the quantification of uncertainty deduced from GNSS data

We propose a new method for real-time uncertainty monitoring of earthquake and volcano source models using data from the global navigation satellite system (GNSS) and explore its application concerning observation station placement. The Geospatial Information Authority of Japan operates two main types of GNSS earth observation network system (GEONET) coordinates for crustal deformation monitoring on different time scales: post-processing analysis values and real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD uses the Markov Chain Monte Carlo (MCMC) method termed real-time automatic uncertainty estimation of a coseismic single rectangular model using GNSS data (RUNE) for single rectangular fault model estimation to handle uncertainty. Thus far, no GNSS monitoring system can automatically detect transient crustal deformation events, such as volcanic activity and earthquake swarms, on timescales of a day or less. We extended RUNE and developed a core program for a new monitoring system for earthquake and volcanic source models and their uncertainties. Our program achieved automatic and stable MCMC utilization for rectangular fault, dike, Mogi, and spheroid models by increasing the computational speed, improving search efficiency, and adjusting hyperparameters. The program automatically determines the standard deviation of the likelihood function assuming a normal distribution with weights for each observation station. The calculation time was within 15 s for 1 × 106 samples on a standard 1U server. We assessed the reliability of the developed method using synthetic and observed GNSS data from the 2015 Sakurajima volcanic event. The results were consistent with the assumed model and previous studies and indicated an advantage in automatically quantifying uncertainty in a short computation time. Based on MCMC samples, we developed a new visualization algorithm to indicate areas on a map in which the number of observation stations should be expanded. We assessed the reliability using data from the 2023 Noto Peninsula earthquake [Mj 6.5]. The results indicate that the algorithm is helpful in studying the placement of stations. The above model extensions and their application are essential to achieve a rapid quantitative understanding of disaster events near urban areas and for utilizing this information in emergency response activities.Graphical

Artificial intelligence-driven tool for spectral analysis: identifying pesticide contamination in bees from reflectance profiling

Pesticide poisoning constantly threatens bees as they forage for resources in pesticide-treated crops. This poisoning requires thorough investigation to identify its causes, underscoring the importance of reliable pesticide detection methods for bee monitoring. Infrared spectroscopy provides reflectance data across hundreds of spectral bands (hyperspectral reflectance), presumably enabling the efficient classification of pesticide contamination in bee carcasses using artificial intelligence (AI) models, such as machine learning. In this study, bee contamination by commercial formulations of three insecticides—dimethoate (organophosphate), fipronil (phenylpyrazole), and imidacloprid (neonicotinoid)—as well as glyphosate, the most widely used herbicide globally, was detected using machine learning models. These models classified the hyperspectral reflectance profiles of the body surfaces of contaminated bees. The best-performing model, the linear discriminant analysis, achieved 98% accuracy in discriminating contamination across species Apis mellifera, Melipona mondury, and Partamona helleri, with prediction speeds of 0.27seconds. Our pioneering study introduced an effective method for discerning multiple classes of bees contaminated with pesticides using hyperspectral reflectance. An AI-driven spectral data analysis tool (https://github.com/bernardesrodrigoc/MACSS) was developed for the purpose of identifying and characterizing new samples through their spectral characteristics. This platform aids efforts to monitor and conserve bee populations and holds potential importance in environmental monitoring, agricultural research, and industrial quality control.

Biofilm Production in Intensive Care Units: Challenges and Implications

The prevalence of infections amongst intensive care unit (ICU) patients is inevitably high, and the ICU is considered the epicenter for the spread of multidrug-resistant bacteria. Multiple studies have focused on the microbial diversity largely inhabiting ICUs that continues to flourish despite treatment with various antibiotics, investigating the factors that influence the spread of these pathogens, with the aim of implementing sufficient monitoring and infection control methods. Despite joint efforts from healthcare providers and policymakers, ICUs remain a hub for healthcare-associated infections. While persistence is a unique strategy used by these pathogens, multiple other factors can lead to persistent infections and antimicrobial tolerance in the ICU. Despite the recognition of the detrimental effects biofilm-producing pathogens have on ICU patients, overcoming biofilm formation in ICUs continues to be a challenge. This review focuses on various facets of ICUs that may contribute to and/or enhance biofilm production. A comprehensive survey of the literature reveals the apparent need for additional molecular studies to assist in understanding the relationship between biofilm regulation and the adaptive behavior of pathogens in the ICU environment. A better understanding of the interplay between biofilm production and antibiotic resistance within the environmental cues exhibited particularly by the ICU may also reveal ways to limit biofilm production and indivertibly control the spread of antibiotic-resistant pathogens in ICUs.

Indoor tests of sensor-enabled piezoelectric geocable–geogrid composite structure for slope rehabilitation and monitoring

Sensor-enabled piezoelectric geocables were combined with a geogrid to acquire a sensor-enabled piezoelectric geogrid (SPGG) based on the impedance–strain relationship. Tension, pullout, and straight shear tests were conducted on this SPGG configuration. The tension test results indicated that the tensile strain–normalized impedance curves were exponential in form within the first 7 % of strain and the rate of shift in impedance was independent of the tension loading rate. An excellent correspondence between the peak strength and impedance inflection point was observed in the results of the pullout and straight shear tests. Additional validation of the proposed SPGG was conducted through a collapse test of the reinforced soil slope model. The results indicated that the SPGG-obtained strains were similar to actual strain gauge measurements but provided a larger measurement range and that the SPGG was able to sense real-time vibrations during the slope collapse using a voltage analysis, confirming that the proposed SPGG can simultaneously provide soil reinforcement, strain monitoring of reinforcement materials, and vibration sensing. This research is expected to inform the development of a dynamic and static monitoring, large range, and accurate method for monitoring the conditions of reinforced soil over their entire lifecycles.

Evolution characteristics of mining-induced fractures in overburden strata under close-multi coal seams mining based on optical fiber monitoring

Large-scale mining fractures resulting from repeated mining are a major cause of surface water loss in the northern Shaanxi mining area, China. Accurately detecting the evolution of mining-induced fractures is crucial for addressing the fragile ecological environment and ensuring coalmine production safety in this area. This study focuses on the close-multi coal seams mining at the Ningtiaota coalmine, northern Shaanxi, China, investigating the failure types of overburden rock, the evolution of mining-induced fractures, and the height of fracture zones. The results indicate that the failure type of overburden strata transforms from a “trapezoid shape” to an “overlapping trapezoid shape”, with the fracture zone height extending from 64.5 m to 158.5 m due to the superposition of secondary mining. Furthermore, the evolution characteristics of mining-induced fractures shift from a “three-stage and three-step” model to a “three-stage and two-step” model. A characterization model of overburden deformation based on optical fiber sensing is proposed to effectively describe the strain distribution characteristics of overburden failure. This model reveals the spatiotemporal evolution of overburden deformation from the perspective of “horizontal three areas and vertical three zones”, enabling real-time characterization of overburden deformation. The results demonstrate a relative error of less than 5.0 % between optical fiber monitoring and other methods, excluding theoretical calculations. This study offers a technical solution for detecting mining-induced fractures in the northern Shaanxi mining area and holds significant implications for broader studies of overburden deformation and failure under repeated mining.

Prevalence of masked hypertension in children with chronic kidney disease: a cross-sectional study.

Hypertension poses a significant risk as a complication of chronic kidney disease (CKD), contributing to its hastened advancement. Implementing ambulatory blood pressure monitoring (ABPM), a straightforward and non-invasive method proves beneficial in identifying masked hypertension. A cross-sectional study was carried out involving children aged 5-15years diagnosed with CKD to estimate the difference in masked HTN prevalence between the 2014 and 2022 AHA ABPM guidelines. The study encompassed a comprehensive assessment, including 24-h blood pressure monitoring and was performed using ABPM. Left ventricular mass (LVM) was computed based on measurements obtained from M-mode echocardiography. PWV was determined by calculating the ratio of distance (D) to time (t). We examined a cohort of 138 children diagnosed with CKD. Our findings reveal that, in accordance with the 2022 American Heart Association (AHA) ABPM guidelines, prevalence of masked hypertension stands at 29.7% marking a notable increase of 17.4% compared to the 2014 ABPM guidelines signifying a substantial proportion of undetected hypertensive cases. Furthermore, the prevalence of hypertension is 48.5% as detected by ABPM, marking a notable increase of 22.5%. A substantial occurrence of masked hypertension was identified in pediatric CKD patients through the application of ABPM. ABPM proves to be an effective tool for uncovering masked hypertension in children with CKD.

The Length of Xiphoid to Fundus as a Measure of Labor Progress: A Cross-sectional Study

Background: Vaginal examination is widely recognized as the most common method for monitoring labor progress. However, researchers are currently exploring alternative methods, which are potentially less invasive or aggressive, to assess labor progress. Objective: This study aimed to assess the correlation between the length of the xiphoid to the fundus and the cervical dilation in the active phase of labor. Methods: This cross-sectional study was conducted on 180 pregnant women in Varamin, Iran. The participants were recruited using convenience sampling. Data were collected using a researcher- made questionnaire that included specific items regarding demographic characteristics, health status, and a checklist to record the results of examinations and labor progress. The collected data were analyzed using descriptive statistics, correlation tests, and multiple linear regression with SPSS 22 software. The significance level was considered to be p <0.05. Results: A total of 174 eligible women participated in the study, with a mean age of 25.90 ± 4.56 years (mean±SD) and a mean gestational age of 39.71 ± 1.03 weeks. There was a significant negative correlation between the length of the xiphoid to the fundus and cervical dilatation (p = 0.0001, r = -0.568). Conclusions: The study revealed a significant negative correlation between the length of the xiphoid to the fundus and the cervical dilation. Therefore, the xiphoid to fundus measurement can serve as an alternative and complementary examination in cases that need frequent vaginal examinations.

Small-grid urban flood prediction model using Twitter data and population GPS data - an example of the 2019 Nagano city flood

In this study, a small-grid urban flood prediction model integrating Twitter data and population GPS data was constructed using the 2019 Nagano City flood as an example, and the validity of these two data for the model was determined. Using natural language processing techniques, Twitter data was filtered to extract real-time information relevant to flooding. At the same time, geographic information processing techniques were applied to analyze the population GPS data and obtain the distribution of the local population. Based on these two types of data, we combined with terrain, land use, traffic and infrastructure data related to flooding, a real-time flood prediction model was constructed using the random forest algorithm with a basic unit of a 70 m × 70 m grid. An analysis of the model accuracy showed that, the model that included both GPS and Twitter data showed an improvement in prediction accuracy of about 8% compared to flood prediction models that do not have these data sources. This indicated that the integrated use of Twitter and GPS data allowed us for a more accurate representation of the dynamic characteristics of flood disasters, thereby improving the performance of real-time flood prediction models and increasing real-time awareness of flood events. This approach provided effective flood monitoring methods for disaster management authorities.

Research on dynamic well control in riserless mud recovery system

In order to promote the development of RMR (Riserless Mud Recovery) system well control technology and ensure the safe operation of deep water drilling operations, the overflow control equation of RMR system considering the lifting force of subsea pump is established and verified, the conventional overflow monitoring method and the overflow monitoring method based on subsea pump parameter variation are compared and analyzed. the dynamic well control process of the RMR system after overflow under the two modes of constant subsea pump displacement and constant inlet pressure of subsea pump is simulated, and the influencing factors of inlet pressure of subsea pump are further analyzed. The results show that although the temperature effect is taken into account in the model established in this paper, the time error of the mud pool increment, the inlet pressure and the displacement of subsea pump after reaching the overflow criterion is less than 22.5% compared with Froyen's model. The conventional overflow monitoring method has fast response and small change, while the overflow monitoring method based on subsea pump parameter change is on the contrary, and the two should be used together. Both the constant inlet pressure of subsea pump mode and the constant subsea pump displacement mode can control overflow, but from the stability of subsea pump, service life and well control safety, the constant displacement mode is better. In the case of large formation pressure, low drilling fluid density, overflow criteria and circulating displacement, the pressure response adjustment speed of the subsea pump inlet is faster, and the back pressure load of the subsea pump inlet can be effectively alleviated by appropriately increasing the circulating displacement and drilling fluid density. This study has enriched the theoretical system of RMR system in overflow control, and has certain guiding significance for the development of deepwater and ultra-deepwater drilling safety technology.

Research on on-site real-time detection technology of transmission line pulling force based on sensors

During the transmission process, the stay wire tower plays an important role in supporting and stabilizing. By monitoring the tension of the stay wire, the deformation and abnormal force of the stay wire tower structure can be detected in a timely manner, and measures can be taken to adjust and repair it. In terms of wire tension monitoring, the main methods currently used include various monitoring technologies such as electronic, mechanical, and optical. However, traditional cable force monitoring methods have problems such as low monitoring accuracy and poor real-time performance, so it is necessary to develop a real-time detection technology based on sensors. This article uses computer technology to achieve real-time detection of transmission line pulling force based on sensors. Firstly, select a suitable sensor for measuring the pulling force. Then, the sensor data is transmitted to the computer system for real-time processing and analysis. Finally, the results are displayed through the interface, facilitating real-time monitoring and control by monitoring personnel. After experimental verification, the real-time detection technology based on sensors proposed in this article can accurately monitor the pulling force of transmission lines. The monitoring results show that this technology has high accuracy and good real-time performance, and can timely detect and handle abnormal wire force situations.

Sensitivity analysis for multi-measurement points based SHM in the mooring lines of floating offshore wind turbines

Abstract Structural health monitoring in floating offshore wind turbines’ mooring lines is vital for detecting early faults and preventing disruptions. Currently, sporadic and expensive monitoring is conducted via remote-operating vehicles. Efficient, automated, economical monitoring methods based on a continuous stream of data acquired via multiple measurement points in the wind turbine, are required. Such methods based on vibration signals have been investigated limitedly for steel chains. Recently, faulty synthetic mooring lines of a simulated 10MW semi-submersible wind turbine have been detected under varying environmental conditions and via the Functional Model Based Method (FMBM) equipped with functional models. The uncertainty due to the conditions’ stochasticity has been addressed by training the models using ten signal realizations per condition (wind) under the healthy wind turbine and from each of two measurement points. The current study presents a preliminary sensitivity analysis of the FMBM’s capability in detecting the simulated wind turbine’s faulty mooring lines when the functional models are trained using one signal realization per varying condition (wind) from each measurement point. The method’s effectiveness is evaluated with acceleration signals from 11 healthy/ 66 faulty (reduced stifness in one mooring line) cases. The FMBM detects all cases even when trained using one signal realization.

Riverbank plastic distributions and how to sample them

As plastic pollution exists in aquatic ecosystems globally, monitoring its abundance and distribution has become crucial for understanding transport pathways, sources, sinks, and impacts. Riverbanks are accumulation zones for plastic, but the selection of monitoring methods is constrained by research goals, available resources, and site-specific conditions. This diversity in approaches has led to disparate datasets, highlighting the need for standardized monitoring protocols. Here, we study the spatial distribution of plastic at the riverbank scale, quantify the uncertainty of existing riverbank methods, and provide recommendations for improved monitoring based on the balance between uncertainty loss and increase in effort. We measured riverbank plastic abundance at eight Dutch riverbanks, categorizing the items using 108 item categories (River-OSPAR). For every riverbank, an area of 100 by 25 meters was subdivided into five-by-five-meter squares, resulting in 100 individual monitored sub-areas. We found riverbank plastic exhibited high spatial variability, with deposition patterns ranging from parallel to the waterline to clustered, random, or uniform (Moran’s I between -0.050 and 0.301). Individual measurements from diverse sampling protocols are 5-49 times less accurate than estimates derived from extensive sampling, highlighting the diminishing impact of specific methods with increased data collection. Lastly, our findings suggest that increasing the sampling area quickly reaches diminishing returns in terms of accuracy. Reducing the sampled area by 80% only increases the uncertainty in estimating the true plastic density by 20%. While standardized protocols are essential for data comparability, a rigid, uniform sampling approach may be less efficient and resource-intensive than a flexible (step-wise) strategy that adapts to local conditions. By demonstrating that extensive sampling can mitigate the differences between unique sampling protocols, this study promotes a shift towards flexible and efficient riverbank plastic monitoring, ultimately accelerating global efforts to combat plastic pollution.

Monitoring of insulator pollution discharge in overhead distribution network based on acoustic emission technology

Abstract During the continuous operation of the distribution network, dirt often accumulates on the surface of insulators. Once encountering a humid climate environment, it may cause accidental discharge, leading to the occurrence of flashover pollution accidents. In order to effectively monitor this issue, this study adopted a monitoring method based on acoustic emission technology. This article introduces the discharge process and acoustic emission phenomenon of insulators in overhead distribution networks under pollution conditions. By using emission signal detection devices, acoustic signals generated during insulator discharge are captured. And the wavelet denoising algorithm was used to process its signal, based on which the discharge characteristics of insulator pollution in the overhead distribution network were extracted. The emission amount of insulator pollution in the overhead distribution network was determined according to the set threshold, achieving monitoring of pollution emissions. The results indicate that the proposed method can accurately monitor the discharge of insulators in overhead distribution networks, provide scientific basis for timely maintenance measures, and improve the operational safety and reliability of distribution networks.