Gridded Data Research Articles

Research on the Analysis Method of Measured Pressure Data during the Transient Process of Load Rejection in Hydropower Stations

Abstract This paper proposes a method to analyze the measured pressure data in the transition process of hydropower station. The method first separates the measured pressure data to the mean pressure and pressure fluctuation data, then divides the pressure fluctuation data into different time domain grids, and performs the confidence probability processing on the pressure fluctuation data in each time grid, and finally obtains the credible measured pressure data of transient process. In this paper, the specific implementation steps of the method are introduced by taking the results of a load rejection test of a pumped storage power station as an example.

Evaluation of Grid-Based Aridity Indices in Classifying Aridity Zones in Iraq

In this study, the aridity index (AI) based on gridded climate data was validated for defining aridity and classifying aridity zones in Iraq through comparison with the results obtained by the station-based aridity index. Gauge-based gridded climate data taken from Climatic Research Unit Timeseries (CRU TS) were used to determine the annual value of four aridity indices (Lang, De Martonne, Ernic and UNEP AI) over the period 1998-2011. The results showed that the aridity distribution maps derived using grid-based aridity indices were reasonably close to those found using station-based ones. The four aridity indices properly identified similar aridity (dryness) classifications in both the station-based and grid-based aridity maps. The area percentage of each aridity class predicted by grid-based AIs was also compared with that obtained by the station-based AIs. The results showed that the variances between the area percentages predicted by grid-based AIs and those estimated using station-based AIs are fairly slight. The Lang AI exhibited the least variance (0.4%) while the De Martonne AI had the biggest variance (-4.8%). Despite these minor variances, it is however possible to conclude that the grid-based aridity index classified the aridity zones of Iraq as properly as the station-based aridity index did.

Demand side management with wireless channel impact in IoT-enabled smart grid system

Demand Side Management (DSM) is a vital issue in smart grids, given the time-varying user demand for electricity and power generation cost over a day. On the other hand, wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid. The design of any DSM system using a wireless network must consider the wireless link impairments, which is missing in existing literature. In this paper, we propose a DSM system using a Real-Time Pricing (RTP) mechanism and a wireless Neighborhood Area Network (NAN) with data transfer uncertainty. A Zigbee-based Internet of Things (IoT) model is considered for the communication infrastructure of the NAN. A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link. The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users, decision-makers, and energy providers. A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices. Simulation results indicate that the proposed system benefits users and energy providers. Furthermore, experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN, which can substantially impact the performance of the proposed DSM system. Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price, user welfare, and provider welfare.

Real Estate Website

Abstract: The motivation to develop Real Estate Web Application comes from my urge to learn Visual Studio .NET 2005 for building the business logic of the application, SQL server 2005 for database designing and using new web technologies like AJAX, Java Script for website designing. The most influential factor for selecting this application is to add some innovative features to the search engine of a Real Estate Website which can make the task of a property buyer easy to search for property listings. The most significant feature in these websites is the interactive search criterion which lets the buyer specify their requirements to get the correct set of records from the database. The search tool should be strong enough to include all the required features which a buyer may desire. Also, each search requires a post back call to the database to retrieve some set of records but making the website AJAX enabled prevents the annoying post backs. Thus, each call refreshes just the data grid to display the listings rather the whole web page. Some other new features like drag and drop tool to save the listings, retrieving related results for each search, etc make this application more feature rich.

Urban pollution: A global perspective

We use worldwide gridded satellite data to analyse how population size and density affect urban PM2.5 pollution. We find that more populated and denser grid cells are more exposed to pollution. However, across urban areas, exposure increases with cities’ population size but decreases with density. Moreover, the population effect is driven mostly by population commuting to core cities rather than the core city population itself. We analyse heterogeneity by geography and income levels. A counterfactual simulation shows that PM2.5 exposure could fall by up to 40% if population size were equalized across all cities within countries, but the relocation of population from large to small cities that maximizes welfare would be small.

Trends in intraseasonal temperature variability in Europe: Comparison of station data with gridded data and reanalyses

AbstractTrends in temperature variability are often referred to have higher effect on temperature extremes than trends in the mean. We investigate trends in three complementary measures of intraseasonal temperature variability: (a) standard deviation of mean daily temperature (SD), (b) mean absolute value of day‐to‐day temperature change (DTD) and (c) 1‐day lagged temporal autocorrelation of temperature (LAG). It is a well‐established fact that different types of data (station, gridded, reanalyses) provide different temperature characteristics and particularly their trends. Moreover, we have uncovered that trends in measures of variability are considerably sensitive to data inhomogeneities. Therefore, we use five different datasets, one station based (ECA&D), one gridded (EOBS) and three reanalyses (JRA‐55, NCEP/NCAR, 20CR), and compare them. The period from 1961 to 2014 where all datasets overlap is examined, and the linear regression method is utilized to calculate trends of investigated measures in summer and winter. Intraseasonal temperature variability tends to decrease in winter, especially in eastern and northern Europe, where trends below −7%·decade−1 are detected for all measures. Decreases in DTD and LAG (indicating increase in persistence) prevail also in summer while summer SD tends to increase. The increase in the width of temperature distribution and the simultaneous increase in persistence indicate a tendency towards the rise in the frequency of extended extreme events in summer. Unlike previous studies, our results imply that reanalyses are not the least accurate in determining trends. JRA‐55 appears to be the least diverging from other datasets, while the largest discrepancies are detected for DTD at station data.

A unified dataset for pre-processed climate indicators weighted by gridded economic activity

Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (total precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density, night-time light intensity, cropland, and concurrent population count – all proxies of economic activity – before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, and we validate our data through a systematic comparison with those employed in leading climate impact studies.

Enhanced Wind Field Spatial Downscaling Method Using UNET Architecture and Dual Cross-Attention Mechanism

Before 2008, China lacked high-coverage regional surface observation data, making it difficult for the China Meteorological Administration Land Data Assimilation System (CLDAS) to directly backtrack high-resolution, high-quality land assimilation products. To address this issue, this paper proposes a deep learning model named UNET_DCA, based on the UNET architecture, which incorporates a Dual Cross-Attention module (DCA) for multiscale feature fusion by introducing Channel Cross-Attention (CCA) and Spatial Cross-Attention (SCA) mechanisms. This model focuses on the near-surface 10-m wind field and achieves spatial downscaling from 6.25 km to 1 km. We conducted training and validation using data from 2020–2021, tested with data from 2019, and performed ablation experiments to validate the effectiveness of each module. We compared the results with traditional bilinear interpolation methods and the SNCA-CLDASSD model. The experimental results show that the UNET-based model outperforms SNCA-CLDASSD, indicating that the UNET-based model captures richer information in wind field downscaling compared to SNCA-CLDASSD, which relies on sequentially stacked CNN convolution modules. UNET_CCA and UNET_SCA, incorporating cross-attention mechanisms, outperform UNET without attention mechanisms. Furthermore, UNET_DCA, incorporating both Channel Cross-Attention and Spatial Cross-Attention mechanisms, outperforms UNET_CCA and UNET_SCA, which only incorporate one attention mechanism. UNET_DCA performs best on the RMSE, MAE, and COR metrics (0.40 m/s, 0.28 m/s, 0.93), while UNET_DCA_ars, incorporating more auxiliary information, performs best on the PSNR and SSIM metrics (29.006, 0.880). Evaluation across different methods indicates that the optimal model performs best in valleys, followed by mountains, and worst in plains; it performs worse during the day and better at night; and as wind speed levels increase, accuracy decreases. Overall, among various downscaling methods, UNET_DCA and UNET_DCA_ars effectively reconstruct the spatial details of wind fields, providing a deeper exploration for the inversion of high-resolution historical meteorological grid data.

Strategic Transportation Planning: Soft Mobility Network Planning Technical Report

This technical report presents a comprehensive approach to soft mobility network planning, focusing on assessing the potential for growth in bicycle infrastructure. Utilizing GIS-based analysis and multi-criteria decision-making, the study aims to identify and prioritize areas for new bike infrastructure development. The methodology involved defining influential factors, selecting variables from available datasets, assigning value levels, weighting parameters, and generating cycling infrastructure potential maps. Factors such as connectivity, safety, and attractiveness are considered, with each variable assessed and scored based on current conditions and ideal benchmarks. The results highlight high-potential areas for infrastructure improvement and propose desired lines to enhance the existing network. Limitations of the study, including data currency and grid dimensions, are acknowledged, suggesting avenues for future research and improvement. Overall, the study underscores the importance of integrated planning approaches and the utilization of spatial analysis tools in promoting cycling as a sustainable and accessible mode of transportation

The role of data-driven methods in power system security assessment from aggregated grid data

Data-driven techniques have been considered as an enabling technology for reducing the computational burden of both static and dynamic power system security analysis. Anyway, the studies reported in the literature mainly focused on inferring from historical data the mapping between the bus variables before and after a certain contingencies set, while, to the best of the Author's knowledge, limited contributions have been devoted to try and classify the power system security state by processing aggregated grid data. This is a relevant issue to address for a Transmission System Operator since it could allow a sensible decrease in the computational burden and, considering that aggregated grid data can be reliably predicted from several hours to one day ahead, it may enable the evolution of security assessment to security forecasting. In trying and filling this research gap, this paper explores the role of machine learning and feature selection algorithms. A realistic case study involving 2 years of synthetic grid data simulated on the Italian power system model against future potential operational scenarios characterized by a high share of renewables is presented and discussed to identify the most promising computing paradigms, analyzing the criticality of tuning the feature selection and classifier algorithms.

An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids

With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.

Trends in temperature and precipitation at high and low elevations in the main mountain ranges of the Iberian Peninsula (1894–2020): The Sierra Nevada and the Pyrenees

AbstractThis study describes temperature and precipitation trends in the two National Parks located in the two highest mountain ranges on the Iberian Peninsula: the Sierra Nevada (Sierra Nevada National Park, SN) and the Pyrenees (Aigüestortes i Sant Maurici National Park, ASM). Special focus is placed on analysing disparities between the lowlands and the highlands, as well as the agreement between observational data and grid data (IBERIA01 and E‐OBS). For this purpose, a quality‐controlled and homogeneity‐adjusted database of the daily maximum temperature, minimum temperature and precipitation (SMADS database) has been generated. Regional trends in mean temperature indicate that warming in ASM (0.17°C·decade−1) was greater than in the Sierra Nevada (SN) (0.13°C·decade−1) in the longest joint period, 1930–2020. For annual precipitation, the trends over the past nine decades were negative, although not significantly. Only the summer in SN showed a significant negative trend, which has intensified in recent decades to −13.4%·decade−1 for 1975–2020. A parallel evolution was observed in the annual mean temperature of the highlands (>1500 m) and lowlands (<1500 m) of ASM, with a common trend of 0.17°C·decade−1, while in SN negative elevation‐dependent warming was detected. Differences between lowlands and highlands were also noted in precipitation trends in both mountain ranges: a positive trend in precipitation was found in the lowlands while in the highlands practically null trends (ASM) or decreasing precipitation trends (SN) were detected. The comparison of the Spanish Mountain Adjusted Daily Series (SMADS) results with the IBERIA01 and E‐OBS grid series yielded differences no greater than ±0.2°C·decade−1. No notable differences were detected between the regional trends calculated with observational series or with grid series. These results worsened when the differences in the trends detected in the individual observed temperature series were compared against the corresponding grid series.

Post-stack 3D merging to fast-track regional interpretation – offshore Otway Basin case study

Ideally when combining legacy 3D seismic surveys, differences in acquisition parameters warrant full pre-stack reprocessing from field data. However, there are occasions where this is not possible due to time, financial or data access constraints; a valuable alternative is post-stack merging and enhancement of existing migrations. This case was to produce a regularised and seamless 3D dataset of the highest possible quality, for the offshore Otway Basin, within 2 months. The input migrated volumes varied by data extent, migration methodology, angle range and grid orientation. Fourteen input volumes totalling 8092 km2 were post-stack merged and processed to produce a continuous and consistent volume, enabling more efficient and effective interpretation of the region. The surveys were regularised onto a common grid, optimised for structural trends, prior to survey matching. A mis-tie analysis algorithm, applied over a time window optimised for interpretation of key events, was used to derive corrections for timing, phase and amplitude, using a reference. This was followed by time-variant spectral and amplitude matching to improve continuity between volumes. Additional enhancements including noise removal and lateral amplitude scaling were also applied. The final merged volume offers significant uplift over the inputs, providing better imaging of structure and events and dramatically improving the efficiency and quality of interpretation. This enables rapid reconnaissance of the area by explorers.

Brazilian Atmospheric Inventories – BRAIN: a comprehensive database of air quality in Brazil

Abstract. Developing air quality management systems to control the impacts of air pollution requires reliable data. However, current initiatives do not provide datasets with large spatial and temporal resolutions for developing air pollution policies in Brazil. Here, we introduce the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database of air quality and its drivers in Brazil. BRAIN encompasses hourly datasets of meteorology, emissions, and air quality. The emissions dataset includes vehicular emissions derived from the Brazilian Vehicular Emissions Inventory Software (BRAVES), industrial emissions produced with local data from the Brazilian environmental agencies, biomass burning emissions from FINN – Fire INventory from the National Center for Atmospheric Research (NCAR), and biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (https://doi.org/10.57760/sciencedb.09858, Hoinaski et al., 2023a; https://doi.org/10.57760/sciencedb.09886, Hoinaski et al., 2023b). The meteorology dataset has been derived from the Weather Research and Forecasting Model (WRF) (https://doi.org/10.57760/sciencedb.09857, Hoinaski and Will, 2023a; https://doi.org/10.57760/sciencedb.09885, Hoinaski and Will, 2023c). The air quality dataset contains the surface concentration of 216 air pollutants produced from coupling meteorological and emissions datasets with the Community Multiscale Air Quality Modeling System (CMAQ) (https://doi.org/10.57760/sciencedb.09859, Hoinaski and Will, 2023b; https://doi.org/10.57760/sciencedb.09884, Hoinaski and Will, 2023d). We provide gridded data in two domains, one covering the Brazilian territory with 20×20 km spatial resolution and another covering southern Brazil with 4×4 km spatial resolution. This paper describes how the datasets were produced, their limitations, and their spatiotemporal features. To evaluate the quality of the database, we compare the air quality dataset with 244 air quality monitoring stations, providing the model's performance for each pollutant measured by the monitoring stations. We present a sample of the spatial variability of emissions, meteorology, and air quality in Brazil from 2019, revealing the hotspots of emissions and air pollution issues. By making BRAIN publicly available, we aim to provide the required data for developing air quality policies on municipal and state scales, especially for under-developed and data-scarce municipalities. We also envision that BRAIN has the potential to create new insights into and opportunities for air pollution research in Brazil.​​​​​​​

Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model

Abstract With the continuous development of Earth Observation technology, resolution of imagery and gridded data has significantly increased, leading to a rapid increase in data volume. To efficiently acquire and analyze these vast amounts of imagery and gridded data, image tiling technology has been developed to effectively access data of interested areas. Tiling technology divides large-scale image data into smaller tiles, providing fast, accurate, and efficient access support for imagery and gridded data. The spatial grid model, as the foundational framework of the new generation of geographic spatial information, plays a critical role in the retrieval, integration, services, and applications of imagery and gridded data resources. When tiling image data based on the spatial grid model, it always generates both complete and incomplete tiles. Particularly, when conducting image tile retrieval using the same rule-based grid in the retrieval area, incomplete tile phenomena along the boundary regions of the retrieved images often occur, resulting in gaps within the retrieval area. To tackle this issue, in this study, we present a new topological model called the Grid Six-Intersection Model (G-6IM), specifically designed for regular rectangular grids, to accurately represent boundary issues in image tiling. Through a practical case study, we demonstrate the effectiveness and practical application potential of the proposed G-6IM model, providing new insights and guidance for the improvement and optimization of imagery and gridded data tiling technology.

Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest

Mapping dynamically distributed livestock in the vast steppe area based on statistical data collected by administrative units is very difficult as it is limited by the quality of statistical data and local geographical environment factors. While, spatial mapping of livestock gridded data is critical and necessary for animal husbandry management, which can be easily integrated and analyzed with other natural environment data. Facing this challenge, this study introduces a spatialization method using random forest (RF) in the Selenge River Basin, which is the main animal husbandry region in Mongolia. A spatialized model was constructed based on the RF to obtain high-resolution gridded distribution data of total livestock, sheep & goats, cattle, and horses. The contribution of factors influencing the spatial distribution of livestock was quantitatively analyzed. The predicted results showed that (1) it has high livestock densities in the southwestern regions and low in the northern regions of the Selenge River Basin; (2) the sheep & goats density was mainly concentrated in 0–125 sheep/km2, and the high-density area was mainly distributed in Khuvsgul, Arkhangai, Bulgan and part soums of Orkhon; (3) horses and cattle density were concentrated in 0–25 head/km2, mainly distributed in the southwest and central parts of the basin, with few high-density areas. This indicates that the RF simulation results effectively depict the characteristics of Selenge River Basin. Further study supported by Geodetector showed human activity was the main driver of livestock distribution in the basin. This study is expected to provide fundamental support for the precise regulation of animal husbandry in the Mongolian Plateau or other large steppe regions worldwide.

Landscape evolution during Holocene transgression of a mid‐latitude low‐relief coastal plain: The southern North Sea

AbstractLow‐relief coastal landscapes are at major risk of rising sea levels, as vertical changes in relative sea level have far‐reaching lateral effects. Integration of a dense 2D grid of seismic reflection data with sedimentological and geotechnical data obtained in two offshore wind farm zones allows detailed documentation of postglacial landforms and environmental change over a 1,021 km2 area in the western sector of the southern North Sea. Following the retreat of Last Glacial Maximum ice sheets from the southern North Sea, the resulting postglacial terrestrial landscape provided a surface for peatland formation as climate started to warm and the water table rose in response to relative sea‐level rise. Southward‐draining fluvial networks formed contemporaneously with the peatlands, and remnants of this terrestrial wetland landscape are buried beneath Holocene marine sediments. Distinctive isolated incisional features and discrete widening of fluvial channels that cut through the peats are interpreted as either tidal ponds or relict tidal channels. These features record the evolution of this landscape through the Early Holocene as marine transgression inundated a low‐relief coastal plain. The erosion of the peatlands observed in the cores, the patchy preservation of the organic wetland landscape, and the lack of preserved barrier systems recorded by the seismic reflection data suggest that the rate of relative sea‐level rise outpaced sediment supply during the Late Postglacial and Early Holocene in this area of the southern North Sea. In a regional context, the southward draining river channels contrast to northward fluvial drainage to the North Sea, pointing to a subtle drainage divide in the palaeolandscape and the presence of a low‐relief land bridge separating the North Sea and the English‐Channel/La Manche during the Early Holocene. The documented scenario of rising sea levels combined with decreasing sediment supply in a low‐relief setting is a situation faced by many global deltas and coastlines, which makes the southern North Sea a crucial archive of coastal landscape change.

Using single-beam bathymetric data in digital depth models, for viewing the Sargassum furcatum bed in Cabo Frio Island

Single-beam bathymetric surveys are used in several areas, namely, in the assessment of underwater geography as determining factor for navigation; conducting scientific studies and exploration of oil and natural resources. The data acquired in this type of surveys offers a grid of X, Y and Z sample points. For the correct spatial visualization in a continuous data grid, it is necessary to apply mathematical interpolators to estimate the depth value in the points not covered by the survey. The​​​​​​​ Sargassum furcatum bed presents on Cabo Frio island is the target of several studies due the importance of its primary productivity and biodiversity. It was verified after the elaboration of the digital depth models, characteristics regarding the location and spatial distribution of the Sargassum furcatum bed. Based on results obtained in the construction of the digital depth models, it was possible to demonstrate that the association of surveys, mining and interpolation methods for single-beam bathymetry data, developed during this study, were suitable to assess the visualization the Sargassum furcatum bed in Cabo Frio Island, Arraial do Cabo - RJ.

Detection of False Data Injection Attacks on Smart Grids Based on A-BiTG Approach

A false data injection attack (FDIA) is the main attack method that threatens the security of smart grids. FDIAs mislead the control center to make wrong judgments by modifying the measurement data of the power grid system. Therefore, the effective and accurate detection of FDIAs is crucial for the safe operation of smart grids. However, the current deep learning-based methods do not fully exploit the short-term local characteristics and long-term dependencies of power grid data and have poor correlation with past and future time series information, resulting in a lack of credibility in the detection results. In view of this, an FDIA detection model combining a bidirectional temporal convolutional network and bidirectional gated recurrent unit with an attention mechanism (A-BiTG) was proposed. The proposed model utilizes a bidirectional time convolutional network (BiTCN) and bidirectional gated recurrent unit (BiGRU) to consider past and future temporal information in the grid. This enhances the ability of the model to capture long-term dependencies and extract features, while also solving the model’s problem of exploding and vanishing gradients. In addition, an attention mechanism (AM) was added to dynamically assign weights to the extracted feature information and retain the most valuable features to improve the detection accuracy of the model. Finally, the proposed method was compared with existing methods on the IEEE 14-bus and IEEE 118-bus test systems. The results show that the proposed detection model is more robust and superior under different noise environments and FDIA signals with different intensities.

Enhancing Security and Efficiency: A Fine-Grained Searchable Scheme for Encryption of Big Data in Cloud-Based Smart Grids

The smart grid, as a crucial part of modern energy systems, handles extensive and diverse data, including inputs from various sensors, metering devices, and user interactions. Outsourcing data storage to remote cloud servers presents an economical solution for enhancing data management within the smart grid ecosystem. However, ensuring data privacy before transmitting it to the cloud is a critical consideration. Therefore, it is common practice to encrypt the data before uploading them to the cloud. While encryption provides data confidentiality, it may also introduce potential issues such as limiting data owners’ ability to query their data. The searchable attribute-based encryption (SABE) not only enables fine-grained access control in a dynamic large-scale environment but also allows for data searches on the ciphertext domain, making it an effective tool for cloud data sharing. Although SABE has become a research hotspot, existing schemes often have limitations in terms of computing efficiency on the client side, weak security of the ciphertext and the trapdoor. To address these issues, we propose an efficient server-aided ciphertext-policy searchable attribute-based encryption scheme (SA-CP-SABE). In SA-CP-SABE, the user’s data access authority is consistent with the search authority. During the search process, calculations are performed not only to determine whether the ciphertext matches the keyword in the trapdoor, but also to assist subsequent user ciphertext decryption by reducing computational complexity. Our scheme has been proven under the random oracle model to achieve the indistinguishability of the ciphertext and the trapdoor and to resist keyword-guessing attacks. Finally, the performance analysis and simulation of the proposed scheme are provided, and the results show that it performs with high efficiency.