Data Processing Platform Research Articles

Enhancing Data Governance with Real-Time Analytics in Oil Production Forecasting

This article explores the transformative impact of real-time analytics on oil production forecasting in the oil and gas industry. It discusses the shift from traditional batch processing to real-time data analysis, highlighting the limitations of batch processing and the benefits of real-time analytics. The article examines how advanced data processing platforms enable more effective data governance and faster insights, leading to improved decision-making, enhanced operational efficiency, and increased production. It also delves into the role of real-time analytics in strengthening compliance and accountability through automated data collection, validation, and consistent data handling. The article presents various case studies and numerical data demonstrating the significant improvements in forecasting accuracy, anomaly detection, and regulatory compliance achieved by implementing real-time analytics systems.

Performance of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) Model in the Northeast Region of Brazil

Study is presented on the potential for using the tools contained in the Google Earth Engine (GEE) geospatial data processing and analysis platform in climatological studies in the Northeast Region of Brazil (NEB), in particular the validation of estimates of total annual rainfall obtained by the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) model in seven homogeneous regions of the NEB. This data is made up of various sources of meteorological information, including information from satellites, numerical models and observations at weather stations in operational and research centers across the globe. The performance of the CHIRPS model was assessed using climatological data obtained from the National Meteorological Institute's (INMET) meteorological database, metrics and statistical tests. The results showed that the CHIRPS model has potential for use in six of the seven regions studied, with low errors and strong statistical correlation, and limitations in one of these regions. The conclusion is that this technological tool, which has a low operating cost and is free of charge, can be used to assist in climatological studies, planning and the development of strategies for mitigation, adaptation and coexistence with the dry periods observed in the sub-regions of the NEB, especially in regions that are in a situation of socio-economic vulnerability, as is the case in the semi-arid region of the NEB.

On the Capabilities of the IREA-CNR Airborne SAR Infrastructure

In this work, the airborne Synthetic Aperture Radar (SAR) infrastructure developed at the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council of Italy (CNR) is described. This infrastructure allows IREA-CNR to plan and execute airborne SAR campaigns and to process the acquired data with a twofold aim. On one hand, the aim is to develop research activities; on the other hand, the aim is to support the emergency prevention and management activities of the Department of Civil Protection of the Italian Presidency of the Council of Ministers, for which IREA-CNR serves as National Centre of Competence. Such infrastructure consists of a flight segment and a ground segment that include a multi-frequency airborne SAR sensor based on the Frequency-Modulated Continuous Wave (FMCW) technology and operating in the X and L bands, an Information Technology (IT) platform for data storage and processing and an airborne SAR data processing chain. In this work, the technical aspects related to the flight and ground segments of the infrastructure are presented. Moreover, a discussion on the response times and characteristics of the final products that can be achieved with the infrastructure is provided with the aim of showing its capabilities to support the monitoring activities required in a possible emergency scenario. In particular, as a case study, the acquisition and subsequent interferometric processing of airborne SAR data relevant to the Stromboli volcanic area in the Sicily region, southern Italy, are presented

All-optical combinational logical units featuring fifth-order cascade

Modern computational technologies are increasingly encountering significant limitations, driving a shift towards alternative paradigms such as optical computing. In this study, we introduce novel all-optical combinational logic units based on diffractive neural networks (D2NNs), designed to perform high-order logical operations both efficiently and swiftly using only two modulation layers. This innovative design offers increased processing speed, improved energy efficiency, robust environmental stability, and high error tolerance, making it exceptionally well-suited for a broad spectrum of applications in optical computing and communications. By leveraging transfer learning, we successfully developed a fifth-order cascaded combinational logic circuit for practical information transmission system. Furthermore, we reveal a pioneering application of our device in Optical Time Division Multiplexing (OTDM), demonstrating its capability to manage high-speed data transfer seamlessly without the need for electronic conversion. Extensive simulations and experimental validations highlight the potential of our model as a foundational technology for future optical computing architectures, paving the way toward more sustainable and efficient optical data processing platforms.

Large-scale data processing platform for laser absorption tomography

Abstract Laser absorption tomography (LAT) has been widely employed to capture two/three-dimensional reactive flow-field parameters with a penetrating spatiotemporal resolution. In industrial environments, LAT is generally implemented by measuring multiple, e.g. 30 to more than 100, wavelength modulated laser transmissions at high imaging rates, e.g. tens to thousands of frames per second (fps). A short-period LAT experiment can generate extensive load of data, which require massive computational source and time for data post-processing. In this work, a large-scale data processing platform is designed for industrial LAT. The platform significantly speeds up LAT signal processing by introducing a parallel computing architecture. By identifying the discrepancy between the measured and theoretical spectra, the new platform enables indexing of the laser-beam measurements that are disturbed by harsh-environment noise. Such a scheme facilitates effective removal of noise-distorted beams, which can lead to artefacts in the reconstructed images. The designed platform is validated by a lab-based LAT experiment, which is implemented by processing the laser transmissions of a 32-beam LAT sensor working at 250 fps. To process a 60-second LAT experimental dataset, the parallelism enabled by the platform saves computational time by 40.12% compared to the traditional single-thread approach. The error-detection scheme enables the successful accurate identification of noise-distorted measurements, i.e. 0.59% of overall laser-beam measurements that fall out of the physical model.

Integrating deep learning, MATLAB, and advanced CAD for predictive root cause analysis in PLC systems: A multi-tool approach to enhancing industrial automation and reliability

The integration of Deep Learning (DL), MATLAB, and Advanced Computer- Aided Design (CAD) in the root cause analysis of prognostic errors in Programmable Logic Controller (PLC) systems represents a significant advancement in industrial automation and reliability. This research explores the synergistic application of these technologies to diagnose, predict, and mitigate failures in PLC systems, which are critical for controlling automated processes in various industries. By employing DL algorithms, the study enhances predictive maintenance capabilities, allowing for early detection of anomalies and reducing downtime. MATLAB is utilized as the central platform for data processing, algorithm development, and simulation, providing a versatile environment for integrating DL models with real-time data from PLCs. Advanced CAD tools are employed to model and visualize the physical systems controlled by the PLCs, offering a comprehensive view that bridges the gap between digital analysis and physical implementation. The research methodology includes data collection from PLC systems, DL model training and validation, MATLAB-based simulations, and CAD modelling. The findings demonstrate improved accuracy in identifying the root causes of PLC prognostic errors, leading to more efficient maintenance strategies and enhanced system reliability. This paper concludes that the integration of DL, MATLAB, and CAD provides a powerful approach for advancing predictive maintenance in industrial settings, ultimately contributing to greater operational efficiency and cost savings.

A Two-Phase Blockchain-Enabled Framework for Securing Internet of Medical Things Systems

The healthcare industry has witnessed a transformative impact due to recent advancements in sensing technology, coupled with the Internet of Medical Things (IoMTs)-based healthcare systems. Remote monitoring and informed decision-making have become possible by leveraging an integrated platform for efficient data analysis and processing, thereby optimizing data management in healthcare. However, this data is collected, processed, and transmitted across an interconnected network of devices, which introduces notable security risks and escalates the potential for vulnerabilities throughout the entire data processing pipeline. Traditional security approaches rely on computational complexity and face challenges in adequately securing sensitive healthcare data against evolving threats, thus necessitating robust solutions that ensure trust, enhance security, and maintain data confidentiality and integrity. To address these challenges, this paper introduces a two-phase framework that integrates blockchain technology with IoMT to enhance trust computation, resulting in a secure cluster that supports the quality-of-service (QoS) for sensitive data. The first phase utilizes the decentralized interplanetary file system and hashing functions to create a smart contract for device registration, establishing a resilient storage platform that encrypts data, improves fault tolerance, and facilitates data access. In the second phase, communication overhead is optimized by considering power levels, communication ranges, and computing capabilities alongside the smart contract. The smart contract evaluates the trust index and QoS of each node to facilitate device clustering based on processing capabilities. We implemented the proposed framework using OMNeT++ simulator and C++ programming language and evaluated against the cutting-edge IoMT security approaches in terms of attack detection, energy consumption, packet delivery ratio, throughput, and latency. The qualitative results demonstrated that the proposed framework enhanced attack detection by 6.00%, 18.00%, 20.00%, and 27.00%, reduced energy consumption by 6.91%, 8.19%, 12.07%, and 17.94%, improved packet delivery ratio by 3.00%, 6.00%, 9.00%, and 10.00%, increased throughput by 7.00%, 8.00%, 11.00%, and 13.00%, and decreased latency by 4.90%, 8.81%, 11.54%, and 20.63%, against state-of-the-art methods and was supported by statistical analysis.

GeoFRESH – an online platform for freshwater geospatial data processing

ABSTRACT Freshwater ecosystems are characterized by their unique longitudinal and lateral habitat connectivity. As a result, spatial units in freshwater-specific analyses can often not be considered independent of each other. Accounting for this connectivity in modelling analyses requires advanced skills in Geographic Information Systems (GIS) for adequately processing and managing the data. To address this challenge, we developed the GeoFRESH online platform, which is available at https://geofresh.org . The platform provides a graphical, easy-to-use interface to create freshwater-specific analysis-ready data for any given location in the world, based on a high-resolution stream network (https://hydrography.org/hydrography90m/hydrography90m_layers). Users can (i) upload and visualize point coordinates, (ii) automatically assign points to the closest stream network segment, (iii) annotate the point data with a suite of 104 local and/or upstream-aggregated topographic, climatic, land-cover and soil variables, (iv) visualize summary plots, and (v) download the data in csv-format for further analyses. The platform can be expanded given its modular structure and it can serve as a key element to support freshwater science and management relying on high-resolution geospatial analyses. GeoFRESH provides a low-entry interface while being complementary to the hydrographr R-package, and contributes importantly to the re-usability of data as an important aspect of the FAIR principles.

The burden of hyperkalaemia on hospital healthcare resources

Hyperkalaemia is associated with prolonged hospital admission and worse mortality. Hyperkalaemia may also necessitate clinical consults, therapies for hyperkalaemia and high-dependency bed utilisation. We evaluated the ‘hidden’ human and organisational resource utilisation for hyperkalaemia in hospitalised patients. This was a single-centre, observational cohort study (Jan 2017–Dec 2020) at a tertiary-care hospital. The CogStack system (data processing and analytics platform) was used to search unstructured and structured data from individual patient records. Association between potassium and death was modelled using cubic spline regression, adjusted for age, sex, and comorbidities. Cox proportional hazards estimated the hazard of death compared with normokalaemia (3.5–5.0 mmol/l). 129,172 patients had potassium measurements in the emergency department. Incidence of hyperkalaemia was 85.7 per 1000. There were 49,011 emergency admissions. Potassium > 6.5 mmol/L had 3.9-fold worse in-hospital mortality than normokalaemia. Chronic kidney disease was present in 21% with potassium 5–5.5 mmol/L and 54% with potassium > 6.5 mmol/L. For diabetes, it was 20% and 32%, respectively. Of those with potassium > 6.5 mmol/L, 29% had nephrology review, and 13% critical care review; in this group 22% transferred to renal wards and 8% to the critical care unit. Dialysis was used in 39% of those with peak potassium > 6.5 mmol/L. Admission hyperkalaemia and hypokalaemia were independently associated with reduced likelihood of hospital discharge. Hyperkalaemia is associated with greater in-hospital mortality and reduced likelihood of hospital discharge. It necessitated significant utilisation of nephrology and critical care consultations and greater likelihood of patient transfer to renal and critical care.

Spatial Query Optimization With Learning

Query optimization is a key component in database management systems (DBMS) and distributed data processing platforms. Recent research in the database community incorporated techniques from artificial intelligence to enhance query optimization. Various learning models have been extended and applied to the query optimization tasks, including query execution plan, query rewriting, and cost estimation. The tasks involved in query optimization differ based on the type of data being processed, such as relational data or spatial geometries. This tutorial reviews recent learning-based approaches for spatial query optimization tasks. We go over methods designed specifically for spatial data, as well as solutions proposed for high-dimensional data. Additionally, we present learning-based spatial indexing and spatial partitioning methods, which are also vital components in spatial data processing. We also identify several open research problems in these fields.

Rekayasa SmartHome System Berbasis Internet of Things

In the current digital era, the Internet of Things (IoT) has emerged as a concept that presents new opportunities to expand sustainable internet connectivity. In the context of Smarthome, IoT enables the control of electronic devices through smartphone applications using internet connectivity, bringing various benefits, including increased comfort, enhanced security, better electricity savings, and introducing innovative solutions that not only allow real-time monitoring of home environments but also provide accessibility by considering the needs and preferences of various users, including families, busy individuals, and people with disabilities. The implementation of an IoT-based Smarthome system utilizes NodeMCU 8266 as a microcontroller, MIT App Inventor as a control tool, Thingspeak as a data processing platform, ESP32-CAM as a camera, and Telegram bot as a notification sender. This system includes the control of lights, fan, environmental monitoring, motion detection, gas leak detection, and fire detection. Various sensors are used, including Flame sensors for fire detection, MQ5 sensors for gas leak detection, and light sensors for automatically turning on lights. Relays are also applied to connect electronic devices to the system. Testing and system analysis show that equipment control operates according to the given commands, provided that the internet connection remains stable and continuous. This underscores the importance of a reliable network infrastructure in supporting the smooth operation of IoT-based Smarthome systems.

A Quasi Real-Time Evaluation of High-Resolution Mass Spectra of Complex Chlorinated Paraffin Mixtures and Their Transformation Products.

Chlorinated paraffins (CPs) are complex mixtures of polychlorinated n-alkanes with multiple carbon- (C-, nC = 9-30) and chlorine homologues (Cl-, nCl = 3-18). The mass spectrometric analysis of CPs is time-consuming and challenging, especially when interferences between CPs, their transformation products, or from the matrix are numerous. These analytical challenges and the lack of appropriate and accessible data evaluation tools are obstacles to their analysis. CP-Hunter is a web-based, open-access data processing platform for the automatic analysis of mass spectra of CPs and their transformation products. Extracts of two consumer plastic materials and sewage sludge were evaluated with CP-Hunter. C- and Cl-homologue distributions were obtained in quasi-real-time and the posterior calculated fingerprints were in agreement with the ones obtained by traditional methods. However, the data extraction and evaluation time were now reduced from several minutes to seconds. The implemented signal deconvolution method, i.e., to resolve mass spectrometric interferences, provides robust results, even when severe matrix effects are present. CP-Hunter facilitates the untargeted analysis of unknown products and the detection and elimination of false positive signals. Finally, data evaluation with CP-Hunter is performed locally without the transfer of data to external servers. The tool is safe, public, and accessible at https://cphunter.cheminfo.org/.

Chroma: A MATLAB package and open-source platform for biomarker data processing and automatic index calculations

The molecular ratio indices of biological markers (biomarkers), such as the Carbon Preference Index (CPI) or Paq, are frequently used as proxies for paleoclimatic and palaeoecological conditions. These indices are regularly extracted from the relative abundances of target molecules detected by a Gas Chromatography analyzer with a Flame Ionization Detector (GC-FID). Despite their use in biogeochemical studies for over a half-century, it remains common procedure to quantify the abundance of individual compounds by manual integration of chromatogram peaks (i.e., interpret baselines visually and characterize peaks by hand), which is time consuming and can lead to inconsistent results. Here, we introduce a new MATLAB package (Chroma) for the automatic detection and integration of standard-referenced biomarker abundances and the calculation of a variety of established hydrocarbon indices commonly reported in the published literature. The algorithm identifies the detector response timing of specific target peaks in a sample chromatogram by cross-referencing to a standard (e.g., Mix-A6, Schimmelmann, Indiana University Bloomington), then calculates the peak areas for an approximation of molecular abundance. This new toolkit for automatic and rapid integration of GC-acquired data provides a consistent and reproducible approach for the calculation of hydrocarbon indices and offers a standardized inter-laboratory platform for data comparisons and exchange. We validate the utility of the Chroma package with the chromatograms of plant wax n-alkanes, a widely used proxy for ecology and hydrology, from six stratigraphic sections in the Tibetan Plateau. Chroma is an effective tool for efficient data processing and will continuously evolve to accommodate extended uses in related areas of biomarker research beyond n-alkanes.

Application of Intelligent Cloud Computing Technology in Optical Communication Network Security of Smart Grid

In order to improve the security of smart grid optical communication network, this paper combines intelligent cloud computing technology to build a security system of optical communication network of power grid. In the aspect of improving communication security performance, multi-user access is realized by non-orthogonal power domain segmentation, and different users use different powers to add and superimpose the same spectrum resources, so as to increase spectrum utilization. At the sending end, this paper improves the safe channel capacity of users by means of pre-coding and artificial noise, and realizes the safe transmission of information. In terms of transmission stability, the cloud computing platform is used as a data processing platform, and multiple nodes are processed synchronously through optical communication state identification, which can more effectively improve the speed of optical communication state identification data. In order to test the performance of the power grid information dispatching model designed in this paper in optimizing power grid configuration and improving power grid load, simulation experiments are carried out. Through the experimental analysis, we can see that the communication method proposed in this paper can accurately identify the intrusion factors, and can effectively improve the security of smart grid optical communication network.

The Renoir Dataflow Platform: Efficient Data Processing without Complexity

Today, data analysis drives the decision-making process in virtually every human activity. This demands for software platforms that offer simple programming abstractions to express data analysis tasks and that can execute them in an efficient and scalable way. State-of-the-art solutions range from low-level programming primitives, which give control to the developer about communication and resource usage, but require significant effort to develop and optimize new algorithms, to high-level platforms that hide most of the complexities of parallel and distributed processing, but often at the cost of reduced efficiency.To reconcile these requirements, we developed Renoir, a novel distributed data processing platform written in Rust. Renoir provides a high-level dataflow programming model as mainstream data processing systems. It supports static and streaming data, it enables data transformations, grouping, aggregation, iterative computations, and time-based analytics, and it provides all these features incurring in a low overhead.In this paper, we present the programming model and the implementation details of Renoir. We evaluate it under heterogeneous workloads. We compare it with state-of-the-art solutions for data analysis and high-performance computing, as well as alternative research products, which offer different programming abstractions and implementation strategies. Renoir programs are compact and easy to write: developers need not care about low-level concerns such as resource usage, data serialization, concurrency control, and communication. At the same time, Renoir consistently presents comparable or better performance than competing solutions, by a large margin in several scenarios.We conclude that Renoir offers a good tradeoff between simplicity and performance, allowing developers to easily express complex data analysis tasks and achieve high performance and scalability.

Optimizing gula apong production with an IoT-based temperature monitoring system

<p>Determining the quality of <em>gula apong</em> is crucial to optimizing its production, with cooking temperature being a key factor affecting both taste and shelf life. The <em>gula apong</em> industry faced challenges due to the lack of reliable real-time temperature monitoring methods during the cooking process. Traditional approaches were inefficient and inaccurate, leading to difficulties in maintaining consistent product quality and meeting market demands. This highlights the necessity of monitoring the temperature throughout each cooking process. This research aims to develop an internet of things (IoT)- based cooking temperature monitoring system to enhance quality control measures in the production of <em>gula apong</em>. The IoT prototype collects temperature data from the thermocouple sensor, then transmits it to cloud storage through a Wi-Fi communication network, utilizing the Node-RED platform for data processing and analysis. Data obtained from the on-site measurement shows that the optimal temperature for producing standard-quality <em>gula apong</em> is approximately around 165 °C. The recommended boiling temperature for Nipah sap is 140 °C. This IoT system can reduce the cooking time of <em>gula apong</em> to 3 hours from the traditional 4 to 6 hours. Utilizing the data acquired from this study helps the producers not only maintaining the quality of <em>gula apong</em> but also reduce the cooking time and cost.</p>

Fluidic shear stress alters clathrin dynamics and vesicle formation in endothelial cells

Endothelial cells (ECs) experience a variety of highly dynamic mechanical stresses. Among others, cyclic stretch and increased plasma membrane tension inhibit clathrin-mediated endocytosis (CME) in non-ECs. It remains elusive how ECs maintain CME in these biophysically unfavorable conditions. Previously, we have used simultaneous two-wavelength axial ratiometry (STAR) microscopy to show that endocytic dynamics are similar between statically cultured human umbilical vein endothelial cells (HUVECs) and fibroblast-like Cos-7 cells. Here, we asked whether biophysical stresses generated by blood flow influence CME. We used our data processing platform—DrSTAR—to examine if clathrin dynamics are altered in HUVECs after experiencing fluidic shear stress (FSS). We found that HUVECs cultivated under a physiological level of FSS had increased clathrin dynamics compared with static controls. FSS increased both clathrin-coated vesicle formation and nonproductive flat clathrin lattices by 2.3-fold and 1.9-fold, respectively. The curvature-positive events had significantly delayed curvature initiation relative to clathrin recruitment in flow-stimulated cells, highlighting a shift toward flat-to-curved clathrin transitions in vesicle formation. Overall, our findings indicate that clathrin dynamics and clathrin-coated vesicle formation can be modulated by the local physiological environment and represent an important regulatory mechanism.

Novel Approach for Vehicle Accident Tracking using IoT

Abstract: In recent years, there has been significant interestin integrating Internet of Things (IoT) technology into vehicle safety systems to enhance emergency response capabilities and road safety. This paper introduces a comprehensive Vehicle Accident Tracking System (VATS) that utilizes IoT principles. The proposed system incorporates various sensors and communication devices installed within vehicles to monitor theirstatus and detect potential accidents in real-time. When anaccident is detected, the system automatically triggers an alert mechanism, notifying relevant stakeholders such as emergency services and fleet managers. Key components of the VATSinclude accelerometers, GPS modules, and onboard communication units, which work together to provide precise accident geospatial data and vehicle condition data. The systemutilizes cloud-based platforms for data storage and processing, ensuring seamless integration with existing infrastructure and scalability. By continuously monitoring and analyzing vehicle telemetry data, the VATS facilitates proactive accident prevention measures and post-accident analysis. Additionally, it offers benefits such as reduced emergency response times, improved accident documentation for insurance claims, and enhanced overall road safety. Privacy and security concerns areaddressed through encryption protocols and access control mechanisms to protect sensitive information. Overall, the Vehicle Accident Tracking System presented in this paper represents a significant advancement in leveraging IoT technology to mitigate the impact of accidents on roadways, thereby contributing to a safer and more efficient transportation ecosystem.

Big data processing and analysis platform based on deep neural network model

Users are increasingly turning to big data processing systems to extract valuable information from massive datasets as the field of big data grows. Data analytics platforms are used by e-commerce enterprises to improve product suggestions and model business processes. In order to meet the needs of large-scale data center operation and maintenance management, Internet companies often use Flink to process log data. This paper takes the big data processing and analysis platforms built by Internet financial companies and large banks as examples, and implants a stock prediction model based on Deep Neural Network (DNN). In this context, this paper completes the following work: 1) The research status of big data processing and analysis platforms at home and abroad is introduced. 2) Drawing on the modular design idea, the commercial bank big data platform is designed and the functions of each sub-module are introduced. Then the basic principle and structure of Convolutional Neural Networks (CNN) are expounded. 3) The optimal parameters of Convolutional Neural Networks are selected through experiments, and then the trained model is used for experiments. It can be seen that the stock prediction model proposed in this article has a higher prediction accuracy compared to existing models, which also verifies the validity of the proposed model. Input the data and compare the obtained results with the actual results, and finally show that the model in this paper has a good performance on stock prediction.

Exploring the chemical compositions of Fufang Yinhua Jiedu granules based on ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry combined with multistage intelligent data annotation strategy

Fufang Yinhua Jiedu granules (FYJG) is a Traditional Chinese Medicine (TCM) compound formulae preparation comprising ten herbal drugs, which has been widely used for the treatment of influenza with wind-heat type and upper respiratory tract infections. However, the phytochemical constituents of FYJG have rarely been reported, and its constituent composition still needs to be elucidated. The complexity of the natural ingredients of TCMs and the diversity of preparations are the major obstacles to fully characterizing their constituents. In this study, an innovative and intelligent analysis strategy was built to comprehensively characterize the constituents of FYJG and assign source attribution to all components. Firstly, a simple and highly efficient ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-QTOF MSE) method was established to analyze the FYJG and ten single herbs. High-accuracy MS/MS data were acquired under two collision energies using high-definition MSE in the negative and positive modes. Secondly, a multistage intelligent data annotation strategy was developed and used to rapidly screen out and identify the compounds of FYJG, which was integrated with various online software and data processing platforms. The in-house chemical library of 2949 compounds was created and operated in the UNIFI software to enable automatic peak annotation of the MSE data. Then, the acquired MS data were processed by MS-DIAL, and a feature-based molecular networking (FBMN) was constructed on the Global Natural Product Social Molecular Networking (GNPS) to infer potential compositions of FYJG by rapidly classifying and visualizing. It was simultaneously using the MZmine software to recognize the source attribution of ingredients. On this basis, the unique chemical categories and characteristics of herbaceous plant species are utilized further to verify the accuracy of the source attribution of multi-components. This comprehensive analysis successfully identified or tentatively characterized 279 compounds in FYJG, including flavonoids, phenolic acids, coumarins, saponins, alkaloids, lignans, and phenylethanoids. Notably, twelve indole alkaloids and four organic acids from Isatidis Folium were characterized in this formula for the first time. This study demonstrates a potential superiority to identify compounds in complex TCM formulas using high-definition MSE and computer software-assisted structural analysis tools, which can obtain high-quality MS/MS spectra, effectively distinguish isomers, and improve the coverage of trace components. This study elucidates the various components and sources of FYJG and provides a theoretical basis for its further clinical development and application.