Acquisition System Research Articles

Extracting Bridge Modal Frequencies Using Stationary Versus Drive-By Modes of Smartphone Measurements

In this research, we harmonize the two mobility approaches, stationary and mobile measurements, within the same framework to generate comparison opportunities, particularly in terms of identified bridge modal frequencies. Vibration tests were conducted to determine the natural frequency of a pedestrian bridge located in University College Dublin using smartphones. Both stationary and mobile smartphone measurements were collected, a novel use of two levels of mobility. Stationary measurements involved leaving the smartphone on the bridge deck at different positions along the bridge for a period of time, and mobile measurements were carried out using an electric scooter to ride across the bridge with the smartphone attached to the scooter deck. Single-output identification results were then compared to visualize the differences at two mobility levels. The tests showed that it is possible to extract the first natural frequency of the bridge using both stationary and mobile smartphone measurement techniques, although operational uncertainties seemed to alter the latter one. A first natural frequency of 5.45 Hz from a reference data acquisition system confirmed the accuracy of stationary smartphone data. On the other hand, the mobile data require consideration of the driving frequency, a function of the speed of the test vehicle and length of the bridge. These results show that smartphone sensors can be regarded as an alternative to industrial accelerometers with certain barriers to account for the multi-modality of the mobile sensing and identification process.

Structure-guided inhibitor design targeting CntL provides the first chemical validation of the staphylopine metallophore system in bacterial metal acquisition

To survive in the metal-scarce environment within the host, pathogens synthesize various small molecular metallophores to facilitate the acquisition of transition metals. The cobalt and nickel transporter (Cnt) system synthesizes and transports staphylopine, a nicotianamine-like metallophore, and serves as a primary transition metal uptake system in Gram-positive bacteria including the human pathogen Staphylococcus aureus. In this study, we report the design of the first inhibitor of the Cnt system by targeting the key aminobutanoyltransferase CntL which is involved in the biosynthesis of staphylopine. Through structure-guided fragment linking and optimization, a class of acceptor-adenosine dual-site inhibitors against S. aureus CntL (SaCntL) were designed and synthesized. The most potent inhibitor, compound 9, demonstrated a ΔTm value of 9.4 °C, a Kd value of 0.021 ± 0.004 μM, and an IC50 value of 0.06 μM against SaCntL. The detailed mechanism by which compound 9 inhibits SaCntL has been elucidated through a high-resolution co-crystal structure. Treatment with compound 9 resulted in a moderate downregulation of intracellular concentrations of iron, nickel, and cobalt ions in the S. aureus cells cultured in the metal-scarce medium, providing the first chemical validation of the important role of Cnt system in bacterial metal acquisition. Our findings pave the way for the development of CntL-based antibacterial agents in future.

Thickness monitoring of threshing mixture on the oscillating plate of corn grain harvester

Cleaning is a critical operation in the corn grain harvesting process, and the operation is seriously affected by the uneven distribution of the threshing mixture on the oscillating plate. To monitor the thickness of the threshing mixture on the oscillating plate of the corn grain harvester, in this study, we adopted the lever principle to design a corn threshing mixture thickness monitoring device based on the piezoresistive effect and theoretically analyzed the lever impact mechanics. The factors affecting the force on the sensor of the monitoring device are the vibration frequency of the oscillating plate, the thickness of the threshing mixture, and grain moisture content, which were obtained through theoretical analysis. The operational performance of the corn threshing mixture thickness monitoring device was assessed through a set of single-factor experiments, revealing the influence laws of three factors on the impact voltage: vibration frequency, thickness of the threshing mixture, and grain moisture content. A back-propagation (BP)11Back-propagation (BP) neural network prediction model was established using an orthogonal experiment with impact voltage, vibration frequency, and grain moisture content as input layers and the thickness of the corn threshing mixture as the output layer. An STM32 microcontroller was used as the primary control unit to build a thickness monitoring data acquisition and processing system that could process and operate the impact voltage collected by the pressure sensor and the vibration frequency signal collected by a Hall sensor. The data were wirelessly transmitted to a PC to predict the thickness of the threshing mixture by using the trained BP neural network. The experimental results of monitoring the variation in corn threshing mixture thickness demonstrated that the device could effectively monitor changes in thickness. The monitoring accuracy of the monitoring device was found to be over 90%, and the system can be used for monitoring the threshing mixture thickness on the oscillating plate of the corn grain harvester.

Preparation of cellulose nanofiber/polyvinyl alcohol-based composite films for metal ion detection by starch/disodium calcium ethylenediaminetetraacetate synergistic complexation effect

Heavy metal pollution causes irreversible damage to plants, animals, and humans. Therefore, it is meaningful to develop facile, fast, and efficient strategies for heavy metal ion (HMI) detection. Here, a portable composite film (CPSE) was designed for HMI detection with high sensitivity and wide detection range. The CPSE was prepared by using cellulose nanofibers (CNF)/polyvinyl alcohol (PVA) as the matrix and utilizing modified starch (HPS) to assist disodium calcium ethylenediaminetetraacetate (EDTA-Ca) to form stable complexes with HMI. The R, G and B values (the three primary colors of light) were captured using an image acquisition system to produce an HMI standard color card successfully. Specifically, the composite film can effectively distinguish Cu2+, Pb2+ and Fe3+. The response time of the composite film to HMI was 2–4 s, and the detection ranges of Cu2+ and Fe3+ were 5–700 ppm and 10–1000 ppm, respectively. Additionally, the synergistic effect of HPS and EDTA-Ca led to the increase in tensile strength (1.59–1.71 times), tear strength (3.29–3.57 times), and glass transition temperature (~ 6 °C) compared to CNF/PVA/HPS and CNF/PVA/EDTA-Ca films. This study confirms the value of CPSE films as materials for HMI detection and suggests innovative ideas for designing similar biomass detection materials in the future.

Predictive Model for Grid Stability Analysis Using Supervisory Control and Data Acquisition (SCADA) System

Predictive Model for Grid Stability Analysis Using Supervisory Control and Data Acquisition (SCADA) System

JET CODAS - the final status

The JET Control and Data Acquisition System (CODAS) has stood the test of time and seen us through to the end of JET plasma operations in 2023. The system architecture has remained largely un-changed over the last decade or so although many new diagnostics and control systems have been added and the volume of data collected has grown massively. CAMAC remains at the heart of the system, particularly for continuous acquisition and control for much of the traditional, stable parts of the system. However, most of the newer diagnostics and control systems are network attached. The CAMAC interface was changed, about a decade ago, to remove the serial highway driver from the subsystem host so that the subsystem hosts could be virtualised and run on more powerful Oracle/Sun hosts, with the Serial Highway driver running on legacy sub-system host network attached. Other significant changes have been the development of a standard, web-based interface for control and data acquisition for diagnostics, the adoption of EPICS for several diagnostics and plant control along with integration into CODAS and the adoption of the ITER SDN protocols over ethernet to supplement the original ATM based real time control infrastructure.These developments were driven by the increased monitoring and machine protection requirements for the ITER Like Wall and the enhanced requirements for the high power Tritium Campaigns (DTE2 and DTE3). The latter, including significant expansion of the neutron and gamma diagnostics, along with expansion of the Tritium introduction systems and enhanced control systems. Towards the end of plasma operations, the requirement for the Laser Induced Desorption with Quadrupole Mass Spectroscopy (LIDS-QMS) involved significant development work to incorporate the associated control and data acquisition systems together with pushing the mode of operation for JET Pulses. At the very end of plasma operations, the requirements for long pulse operation also pushed the pulse operating mode. We now progress to Decommissioning and Repurposing JET and CODAS continues to be adapted to support the diminishing number of systems required to support the plant that is still operational and support diagnostic calibration later this year.

Methodological approach for fast high-resolution image selection: FAHRIS algorithm

Recent research highlights advancements in collecting Artificial Light at Night (ALAN) data using radiosondes on stratospheric balloons, revealing a need for enhanced in-flight image stabilization. This paper proposes a twofold approach: Firstly, it introduces a design concept for a high-resolution image acquisition and stabilization system for aerial instruments (e.g., drones, balloons). Secondly, it presents a novel Fast Algorithm for High-Resolution Image Selection (FAHRIS) for rapid image selection, grouping and stitching of acquired imagery. FAHRIS’ effectiveness is validated using datasets from three flights over Italy: a stratospheric balloon flight reaching 34 kms over Florence, and drone flights using a DJI Mavic 2 up to 253 m over Trevisoand 330 m over Padua. Limitations and challenges encountered during the validation of FAHRIS, such as computational constraints affecting dataset processing, are addressed. Additionally, the results of the image stitching process highlight potential distortions and stretching issues, particularly evident in images with significant relative angles.•Design proposition of stabilization system for aerial instruments.•Development of a novel and fast image selection, grouping and stitching algorithm (FAHRIS).•Validation of algorithm against data sets from three flights.

Securing the Future: Cybersecurity Challenges and Solutions in Digital Oilfields

Digital oilfield is a concept that applies advanced technology to automate workflows in the oil and gas industry for the sole purpose of maximizing production, reducing costs, and minimizing the overall risks associated with operations. However, despite the numerous advantages, careful planning and mitigation strategies put in place, there has been a spike in the rate of cyber-attacks carried out on critical infrastructure across various industries. Cyber threats like malware, ransomware, code injection attacks and phishing attacks pose significant risks to the smooth functioning of these digital infrastructures. Its sensitivities, including outdated legacy systems, Supervisory Control and Data Acquisition (SCADA) systems, and numerous IoT devices, including human error also play considerable roles in cybersecurity breaches, highlighting the need for robust security protocols and strategies. However, with the advent of safer AI technologies, enhanced employee training on cybersecurity, cyber incidents as reported in the case studies, could be mitigated. This research examines critical cyber-attacks in the oil and gas industry to identify vulnerabilities and develop robust preventive strategies. The findings emphasize the importance of a multi-layered security approach, including network segmentation, end-to-end data encryption, and systematic software patch management. Organizations must implement comprehensive incident response plans and conduct regular security audits to maintain operational resilience. The study highlights that effective cybersecurity in the oil and gas sector requires both regulatory compliance and strategic asset prioritization. By identifying and classifying critical infrastructure, companies can allocate resources more effectively and strengthen their security posture where it matters most. The research demonstrates that successful incident management hinges on well-defined response and recovery protocols. Emerging technologies play a pivotal role in advancing cybersecurity defenses. Artificial intelligence enables predictive threat detection, while blockchain technology enhances data integrity and traceability. Cloud security solutions and machine learning algorithms provide scalable, adaptive protection against evolving threats. Through evaluation of case study of cyber incidents across two distinct sectors, it becomes imperative that cybersecurity is a topic that cuts across various sectors and demands industry collaboration and stringent regulatory compliance to avoid future breaches.

Research on Key Technologies for Automated Control of Packaging Production Lines

This paper focuses on the key technologies of automated control in packaging production lines, systematically explaining the importance of automation control in improving production efficiency, quality stability, and flexible production. By analyzing core technologies such as sensor and detection technology, Programmable Logic Controllers (PLC), servo drive and motion control systems, and data acquisition and real-time monitoring systems, the paper explores the practical application effects of these technologies in different scenarios. Combined with typical case analyses, the paper summarizes the current status and challenges of automated control for packaging production lines and proposes insights on future development trends and integration with smart manufacturing, providing theoretical support and practical guidance for further enhancing the level of automation and intelligence of packaging production lines.

Sand gradation detection method based on local sampling

Sand gradation is an important reference factor for concrete proportion design, which has a significant impact on the strength and workability of concrete. The efficiency of using the traditional vibrating sieving method to measure the gradation is low, and the machine vision-based sand gradation inspection method can realize the automation of sand gradation inspection, which can significantly improve the inspection efficiency and reduce the inspection cost. The particle size of construction sand is usually between 0.075 and 4.75 mm, and to capture images of sand below 0.15 mm requires a high-resolution camera that can only maintain a small object distance and a small shooting field of view, so it is difficult for the image acquisition system to capture images of all the sand particles, and this is a challenging problem in sand grading inspection. In order to solve this problem, this study proposes a sand gradation detection method based on localized sampling, and develops a hardware system for localized sampling and a software system for gradation detection. The hardware system uses a flexible vibrating disk to uniformly disperse the sand particles, realizing random local sampling of sand particles, and calculating the grain size gradation of the test samples based on the local data of multiple local sampling. The experimental results show that the local sampling gradation and the sample gradation have a similar distribution, and the local gradation detection error of multiple sampling is smaller. This study provides an effective method for the automated detection of sand grain size gradation between 0.075 and 4.75 mm, which can significantly increase the frequency of construction sand gradation detection, improve the quality control level of construction sand, and improve the quality of construction.

Learning continuous scattering length density profiles from neutron reflectivitiesusing convolutional neural networks

Abstract Interpreting neutron reflectivity (NR) data using ad hoc multi-layer models and
physics-based models provides information about spatially resolved neutron scattering
length density (NSLD) profiles. Recent improvements in data acquisition systems
have allowed acquiring thousands of NR curves in a couple of hours, which has led to
a need for automated data analysis tools to interpret NR measurements in real-time.
Here, we present a machine learning analysis workflow that uses a series of models,
based on a Convolutional Neural Network (CNN), to learn the relation between the
NSLDs and the NRs, and subsequently produce continuous NSLD profiles directly
from NRs. The usefulness of our CNN-based models is demonstrated by constructing
NSLDs from NRs of several films containing homopolymer polyzwitterions (PZs) and
diblock copolymers mixed with different types of salts. Comparisons of the NSLDs
with those constructed using ad hoc multi-layer models reveal a very good agreement,
suggesting the potential of CNN-based models for real-time automated data analysis
of NRs.

Experimental investigation on seakeeping performance of variable-structure SWATH in heading regular waves

The variable-structure unmanned surface vehicle (VS-USV) have high maneuverability, strong survival, and multitasking characteristics in complex sea environment. In this study, a variable-structure small waterplane area twin hull (VS-SWATH) is proposed, which combines the advantages of straight strut SWATH and inclined strut SWATH. In order to study the seakeeping performance of the VS-SWATH with rotatable strut and hydrofoils at medium to low speeds, a series of seakeeping towing model tests was conducted. A variable-structure device was installed on the test model to change the strut rotation angle, and a detachable hydrofoil adjustment device was designed for the test model enabling the exchange of wingless and winged models. The test equipment includes a seaworthiness instrument system and a data acquisition system to measure values such as pitch, heave, vertical acceleration, and monitor the fluid around the ship. The test results show that the VS-SWATH can avoid resonance by changing the strut rotation angle. Hydrofoils mainly reduce heave and pitch at longer wavelengths, but their improvement in seakeeping performance is relatively limited in short waves.

Research on anomaly detection and correction of power metering data based on machine learning algorithm

Electric energy measurement is the basis of marketization of electric energy. If the power metering device is abnormal, it will directly affect the economic interests of both sides. At present, the electric energy measurement data of power grid enterprises has generally adopted the mode of remote centralized collection. The existing methods of abnormal detection and location of electric energy metering are mainly through the analysis of the abnormal data alarm issued by the electric energy acquisition system and the on-site inspection of the metering device. With the continuous expansion of the scale of electric power data, the existing methods highlight the shortcomings of low accuracy and low efficiency. In order to explore the optimal solution to the above problems, this paper constructs a multi-model fusion anomaly detection method of electric energy measurement data based on machine learning, and gives the anomaly correction scheme of electric energy measurement data. The results show that the fusion model has the best performance in the actual situation, with AUC reaching 0.9653 and TPR exceeding 0.64 under the condition of zero FPT. The comprehensive performance is better than that of other single models.

An automated toolbox for microcalcification cluster modeling for mammographic imaging.

Mammographic imaging is essential for breast cancer detection and diagnosis. In addition to masses, calcifications are of concern and the early detection of breast cancer also heavily relies on the correct interpretation of suspicious microcalcification clusters. Even with advances in imaging and the introduction of novel techniques such as digital breast tomosynthesis and contrast-enhanced mammography, a correct interpretation can still be challenging given the subtle nature and large variety of calcifications. Computer simulated lesion models can serve to develop, optimize, or improve imaging techniques. In addition to their use in comparative (virtual clinical trial) detection experiments, these models have potential application in training deep learning models and in the understanding and interpretation of breast lesions. Existing simulation methods, however, often lack the capacity to model the diversity occurring in breast lesions or to generate models relevant for a specific case. This study focuses on clusters of microcalcifications and introduces an automated, flexible toolbox designed to generate microcalcification cluster models customized to specific tasks. The toolbox allows users to control a large number of simulation parameters related to model characteristics such as lesion size, calcification shape, or number of microcalcifications per cluster. This leads to the capability of creating models that range from regular to complex clusters. Based on the input parameters, which are either tuned manually or pre-set for a specific clinical type, different sets of models can be simulated depending on the use case. Two lesion generation methods are described. The first method generates three-dimensional microcalcification clusters models based on geometrical shapes and transformations. The second method creates two-dimensional (2D) microcalcification cluster models for a specific 2D mammographic image. This novel method employs radiomics analysis to account for local textures, ensuring the simulated microcalcification cluster is appropriately integrated within the existing breast tissue. The toolbox is implemented in the Python language and can be conveniently run through a Jupyter Notebook interface, openly accessible at https://gitlab.kuleuven.be/medphysqa/deploy/breast-calcifications. Validation studies performed by radiologists assessed the level of malignancy and realism of clusters tuned with specific parameters and inserted in mammographic images. The flexibility of the toolbox with multiple simulation methods is illustrated, as well as the compatibility with different simulation frameworks and image types. The automation allows for the straightforward and fast generation of diverse microcalcification cluster models. The generated models are most likely applicable for various tasks as they can be configured in a variety of ways and inserted in different types of mammographic images of multiple acquisition systems. Validation studies confirmed the capacity to simulate realistic clusters and capture clinical properties when tuned with appropriate parameter settings. This simulation toolbox offers a flexible means of simulating microcalcification cluster models with potential use in both technical and clinical research in mammography imaging. The 3D generation methods allow for specifying many characteristics regarding the calcification shape and cluster architecture, and the 2D generation method presents a novel manner to create microcalcification clusters tailored to existing breast textures.

A Secure IIoT Environment That Integrates AI-Driven Real-Time Short-Term Active and Reactive Load Forecasting with Anomaly Detection: A Real-World Application

The Industrial Internet of Things (IIoT) revolutionizes both industrial and residential operations by integrating AI (artificial intelligence)-driven analytics with real-time monitoring, optimizing energy usage, and significantly enhancing energy efficiency. This study proposes a secure IIoT framework that simultaneously predicts both active and reactive loads while also incorporating anomaly detection. The system is optimized for real-time deployment on an edge server, such as a single-board computer (SBC), as well as on a cloud or centralized server. It ensures secure and reliable industrial operations by integrating smart data acquisition systems with real-time monitoring, control, and protective measures. We propose a Temporal Convolutional Networks-Gated Recurrent Unit-Attention (TCN-GRU-Attention) model to predict both active and reactive loads, which demonstrates superior performance compared to other conventional models. The performance metrics for active load forecasting are 0.0183 Mean Squared Error (MSE), 0.1022 Mean Absolute Error (MAE), and 0.1354 Root Mean Squared Error (RMSE), while for reactive load forecasting, the metrics are 0.0202 (MSE), 0.1077 (MAE), and 0.1422 (RMSE). Furthermore, we introduce an optimized Isolation Forest model for anomaly detection that considers the transient conditions of appliances when identifying irregular behavior. The model demonstrates very promising performance, with the average performance metrics for all appliances using this Isolation Forest model being 95% for Precision, 98% for Recall, 96% for F1 Score, and nearly 100% for Accuracy. To secure the entire system, Transport Layer Security (TLS) and Secure Sockets Layer (SSL) security protocols are employed, along with hash-encoded encrypted credentials for enhanced protection.

Design of a Single-Edge Nibble Transmission Signal Simulation and Acquisition System for Power Machinery Virtual Testing

Design of a Single-Edge Nibble Transmission Signal Simulation and Acquisition System for Power Machinery Virtual Testing

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.

Design and Implementation of SCADA Architecture Based on MATLAB App Designer for a Hybrid Power System

Hybrid renewable power systems (HRPS) are now considered reliable solutions for power generation under various conditions. A critical challenge for deploying HRPS involves the design and implementation of an effective supervisory control and data acquisition (SCADA) system, which is essential for real-time monitoring and control. The SCADA system plays a vital role in remote monitoring and control by enabling real-time data management, early fault detection, and timely troubleshooting. This study presents the design and implementation of a SCADA architecture based on a MATLAB App Designer for an HRPS consisting of wind turbines, photovoltaic panels, diesel generators, and batteries. The system utilizes Arduino Mega 2560 as remote terminal units (RTUs) to interface with actuators and measure critical system parameters such as voltage and current. Arduino boards can be controlled directly from MATLAB® via the MATLAB® Support Package for Arduino® Hardware. A laptop is the main terminal unit (MTU), communicating with the Arduino via the Firmata protocol. MATLAB App Designer is the central monitoring interface, providing real-time data acquisition, processing, and visualization. In addition, a relay module is used to control the operation of the diesel generator. The relay module acts as an intermediary between the control system and the diesel generator, allowing for automated control of the generator's operation. It receives commands from the SCADA system to either start or stop the generator based on the energy demands and the availability of renewable resources. By managing the diesel generator's activity, the relay module helps ensure that the HRPS maintains a balanced and efficient power supply, minimizing reliance on non-renewable sources when renewable energy is sufficient. Based on experimental results, the proposed SCADA system effectively monitors and controls HRPS under different conditions.

Polarimetric BSSRDF Acquisition of Dynamic Faces

Acquisition and modeling of polarized light reflection and scattering help reveal the shape, structure, and physical characteristics of an object, which is increasingly important in computer graphics. However, current polarimetric acquisition systems are limited to static and opaque objects. Human faces, on the other hand, present a particularly difficult challenge, given their complex structure and reflectance properties, the strong presence of spatially-varying subsurface scattering, and their dynamic nature. We present a new polarimetric acquisition method for dynamic human faces, which focuses on capturing spatially varying appearance and precise geometry, across a wide spectrum of skin tones and facial expressions. It includes both single and heterogeneous subsurface scattering, index of refraction, and specular roughness and intensity, among other parameters, while revealing biophysically-based components such as inner- and outer-layer hemoglobin, eumelanin and pheomelanin. Our method leverages such components' unique multispectral absorption profiles to quantify their concentrations, which in turn inform our model about the complex interactions occurring within the skin layers. To our knowledge, our work is the first to simultaneously acquire polarimetric and spectral reflectance information alongside biophysically-based skin parameters and geometry of dynamic human faces. Moreover, our polarimetric skin model integrates seamlessly into various rendering pipelines.

Optimization of energy acquisition system in smart grid based on artificial intelligence and digital twin technology

In response to the low operating speed and poor stability of energy harvesting systems in smart grids, an energy harvesting optimization method based on improved convolutional neural networks and digital twin technology is proposed in the experiment. Firstly, a smart grid data transmission framework integrating digital twin technology is proposed. A digital twin mapping method based on time, data, and topology structure is used to realize the digital twin mapping at the device level of power grid. Through data synchronization and interaction between the physical power grid and the digital twin model, the operational efficiency and reliability of the power grid are improved. Then, the classical convolutional neural network and attention mechanism are used to comprehensively analyze the physical topology data in the smart grid energy acquisition system. The improved lightweight target detection model is combined to monitor the equipment status of the smart grid and extract key features. Simultaneously utilizing convolutional attention mechanism to dynamically adjust the feature weights of channels or spaces, completing the preprocessing of energy harvesting data. Finally, combined with energy harvesting and power grid switching system, the process of energy harvesting and power grid operation are optimized together. On the training and validation sets, when the channels exceeded 60, the proposed method achieved a system energy efficiency of 55% during operation. The system energy efficiency of the other three comparative algorithms was all less than 40%. In practical applications, as the energy transfer loss increased to 1.0, the system throughput increased to 50 bits. The electricity needs of different users were met, and the difference between power allocation and optimal power allocation was small, which was very reasonable. This proves that the research has effectively optimized the energy harvesting system in the smart grid, improving the efficiency and reliability of the system in practical applications of the smart grid. At the same time, in the increasingly severe energy problem, this system can further provide technical references for the utilization of renewable energy and help achieve the goal of sustainable energy.