Efficient Data Acquisition Research Articles

Efficient data acquisition for traceability and analytics

Implementing processes for traceability is required in various industries to assure product quality during manufacturing, provide evidence on required processing conditions or facilitate product recalls. Commonly, radio-frequency identification (RFID) or code recognition techniques (e.g. Data Matrix) are applied to track the flow of workpieces through a manufacturing system and link processing data accordingly. Although the analysis of tracking data is well-examined, we still see a gap in the research on the trade-off between data acquisition, data analytics and data quality. Here, we present a framework to increase the value of existing data by enabling data analytics while addressing common pitfalls and reducing the costs of data management.

The Development and Application of MTPA Calibration Method Based on INCA FLOW and MATLAB

Article The Development and Application of MTPA Calibration Method Based on INCA FLOW and MATLAB Zhijun Liu 1,2, Renyi Huang 1,*, Xiaoyan Zhuo 1, Kailiang Chen 1, and Guanglan Li 1 1 Liuzhou Saike Technology Development Co., Ltd., Liuzhou 545005, China 2 Liuzhou Key Laboratory of Electrified Powertrain Electronic Control, Liuzhou 545005, China * Correspondence: renyi.huang@foxmail.com Received: 20 July 2023 Accepted: 6 November 2023 Published: 28 November 2023 Abstract: In industrial applications, the maximum torque per ampere (MTPA) control algorithm typically employs an offline look-up table (LUT) method instead of an online MTPA control method to reduce the online computational resources. However, the LUT method has some drawbacks, such as heavy testing workload, longer cycles, and cumbersome data processing, significantly diminishing the efficiency of MTPA data acquisition. To address the limitations of the LUT method, this study proposes an automatic MTPA calibration method combining INCA FLOW and MATLAB. The calibration efficiency and data accuracy are improved by utilizing INCA FLOW for automatic MTPA testing and MATLAB for data processing. An experimental test bench was constructed to evaluate the proposed method. The experimental results demonstrate that this method efficiently performs MTPA data testing and visualizes the results. Compared with the traditional manual MTPA calibration method, this method achieves a 320% improvement in calibration efficiency. The main technical contribution of this study is the introduction of the automatic MTPA calibration method that not only optimizes data accuracy but also enhances calibration efficiency.

Multi-Scalar Oblique Photogrammetry-Supported 3D webGIS Approach to Preventive Mining-Induced Deformation Analysis

Underground coal mining will inevitably cause serious ground deformation, and therefore, preventive mining-induced deformation analysis (MIDA) is of great importance in assisting mining planning and decision-making. Current web-based Geographic Information System (webGIS)-based applications usually use 2D GIS data and lack a holistic framework. This study presents a multi-scalar oblique photogrammetry-supported unified 3D webGIS framework for MIDA applications to fill this gap. The developed web platform uses multiple open-source JavaScript libraries, and the prototype system provides user-friendly interfaces for GIS data collecting and corresponding database establishment, geo-visualization and query, dynamic prediction, and spatial overlapping analysis within the same framework. The proposed framework was tested and evaluated in the Qianyingzi mining area in eastern China. The results demonstrated that multi-scalar oblique photogrammetry balances data quality and acquisition efficiency and provides a good source of GIS datasets, and the web-based platform has a good absolute and relative spatial accuracy verified by two types of validation data. Practical application results proved the feasibility and reliability of the system. The developed web-based MIDA prototype system attains an appealing performance and can be easily extended to similar geoscience applications.

An inversion model of microplastics abundance based on satellite remote sensing: a case study in the Bohai Sea

Nowadays, microplastics (MPs) as emerging contaminants have posed great risks to marine ecosystems and human health. However, non-continuous field sampling data makes it difficult to meet the needs of scientific research and pollution control of marine MPs. Consequently, the development of rapid monitoring techniques for marine MPs to achieve efficient acquisition of data is increasingly essential. Remote sensing technology provides a convenient and effective tool for monitoring and mapping marine MPs pollution. Therefore, we established an inversion model based on multiple regression by combining the remote sensing data and the measured data to predict the MPs pollution status in the Bohai Sea. The feature variables of a model are crucial to the prediction, and we proposed three methods of variable selection, namely successive projections algorithm (SPA), band combination method, and remote sensing index method. By comparing accuracy evaluation metrics, an approach based on SPA was selected to analyze the abundance and spatio-temporal distribution of MPs in the Bohai Sea in 2022. The determination coefficient of the SPA model is 0.75, and the root mean square error is 0.38 items/m3. The error of the model is within an acceptable range. It was found that the MPs abundance on the sea surface of the Bohai Sea varied significantly in different seasons and regions. This study indicates that satellite remote sensing technology has great potential in monitoring marine MPs.

Exploiting redundancy in large materials datasets for efficient machine learning with less data

Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to 95% of data can be safely removed from machine learning training with little impact on in-distribution prediction performance. The redundant data is related to over-represented material types and does not mitigate the severe performance degradation on out-of-distribution samples. In addition, we show that uncertainty-based active learning algorithms can construct much smaller but equally informative datasets. We discuss the effectiveness of informative data in improving prediction performance and robustness and provide insights into efficient data acquisition and machine learning training. This work challenges the “bigger is better” mentality and calls for attention to the information richness of materials data rather than a narrow emphasis on data volume.

Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS.

While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of millimolar. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient SNR comes at the cost of a long acquisition time. We propose to use deep-learning approaches to denoise MRS data without increasing NSA. This method has potential to reduce the acquisition time as well as improve SNR and quality of spectra, which could enhance the diagnostic value and broaden the clinical applications of MRS. The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA=1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA=192), which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improvement in SNR. To prevent overfitting, the SAE network was trained in a patch-based manner. We then tested the denoising methods on noise-containing data collected from the phantom and human subjects, including data from brain tumor patients. We evaluated their performance by comparing the SNR levels and mean squared errors (MSEs) calculated for the whole spectra against high SNR "ground truth", as well as the value of chemical shift of N-acetyl-aspartate (NAA) before and after denoising. With the SAE model, the SNR of low NSA data (NSA=1) obtained from the phantom increased by 28.5% and the MSE decreased by 42.9%. For low NSA data of the human parietal and temporal lobes, the SNR increased by 32.9% and the MSE decreased by 63.1%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra without significant distortion to the spectra after denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels. The reported SAE denoising method is a model-free approach to enhance the SNR of MRS data collected with low NSA. With the denoising capability, it is possible to acquire MRS data with a few NSA, shortening the scan time while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest. This approach has the potential to improve the efficiency and effectiveness of clinical MRS data acquisition by reducing the scan time and increasing the quality of spectroscopic data.

Ergodic sampling: Acquisition design to maximize information from limited samples

AbstractData acquisition using equal spacing has been a standard practice in geophysics. The dense uniform sampling derived from Nyquist–Shannon sampling includes redundant samples, and it is sufficient but not necessary to adequately record target signals. We propose an ergodic sampling, which avoids the redundant samples in the dense uniform sampling and possesses the ability to capture the sufficiently similar information content as does Nyquist sampling. Ergodicity means that a key part of the system can represent the average performance of the entire system. Our ergodic samples are a critical subset of dense uniform samples and can represent the full uniform Nyquist samples. To find such a critical subset, we first examine the properties of different sampling patterns, including the sampling interval distribution, sampling angle distribution, areal sample density, and resolution in the spectral domain. We define the information sampling ability of sampling patterns based on these properties. The concept of information sampling ability that we have proposed serves as the criterion to compare different sampling patterns and assess their sampling performances. The sampling patterns with the same information sampling ability have the same capability to gather information, even though the appearance of sampling patterns may be different. We formulate an optimization problem to find this critical subset of sample locations, which has the fewest number of samples but has a similar information sampling ability as that of the desired dense uniform samples. This critical subset is irregularly located, has the optimized properties and forms the ergodic sampling pattern. We define this process of sampling design and associated understanding as the ergodic sampling. Ergodic sampling can be applied to gain two major benefits in practice. First, this approach can save a significant number of samples. We demonstrate ergodic sampling using one‐dimensional synthetic data and a two‐dimensional field geophysical dataset. The simulations confirm that, compared with other sampling strategies, ergodic sampling can use fewer samples to acquire the same amount of information, so that we can save cost. Alternatively, with the same budget, we can use the same number of samples through ergodic sampling to acquire more information. The new ergodic sampling can lead to a new generation of economic and efficient geophysical data acquisition, which could assist in increasing the discovery rate in resource exploration, tackling more earth science problems with a limited budget and can also benefit the environment in the process by reducing the invasiveness in potentially sensitive regions.

Adaptive Acquisition and Visualization of Point Cloud Using Airborne LIDAR and Game Engine

The development of digital twin for smart city applications requires real-time monitoring and mapping of urban environments. This work develops a framework of real-time urban mapping using an airborne light detection and ranging (LIDAR) agent and game engine. In order to improve the accuracy and efficiency of data acquisition and utilization, the framework is focused on the following aspects: (1) an optimal navigation strategy using Deep Q-Network (DQN) reinforcement learning, (2) multi-streamed game engines employed in visualizing data of urban environment and training the deep-learning-enabled data acquisition platform, (3) dynamic mesh used to formulate and analyze the captured point-cloud, and (4) a quantitative error analysis for points generated with our experimental aerial mapping platform, and an accuracy analysis of post-processing. Experimental results show that the proposed DQN-enabled navigation strategy, rendering algorithm, and post-processing could enable a game engine to efficiently generate a highly accurate digital twin of an urban environment.

Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning.

Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8-30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD.

EVALUATION OF A GNSS/IMU/LIDAR-INTEGRATION FOR AIRBORNE LASER SCANNING USING RTKLIB PPK AND PPP GNSS SOLUTIONS

Abstract. Airborne laser scanning allows for efficient acquisition of accurate 3D data for large areas. Because georeferencing of the LiDAR data requires knowledge of the platform trajectory, the laser scanner system commonly comprises a global navigation satellite system (GNSS) receiver/antenna and an inertial measurement unit (IMU). The standard processing pipeline consists of GNSS/IMU integration, georeferencing, and subsequent adjustment of the laser data. Here, we consider a holistic GNSS/IMU/LiDAR-integration approach based on least-squares adjustment. The GNSS is loosely coupled, and the GNSS positions are obtained using either postprocessing kinematic or precise point positioning GNSS processing strategies using the open-source software RTKLib. In this contribution, we compare the resulting point clouds to those of a standard processing workflow and evaluate the impact of the different processing strategies on point cloud quality in terms of internal consistency and absolute accuracy for a airborne laser bathymetry (ALB) dataset. Although the GNSS solutions themselves differ strongly, both the PPK- and the PPP-derived point clouds show better strip differences (below 2.5 cm) and similar absolute accuracy (<4 cm RMSE w.r.t. reference targets after correction of constant datum shift) compared to the reference solution.

Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining

The calculation of volumes for irregular bodies holds significant relevance across various production processes. This spans tasks such as evaluating the growth status of crops and fruits, conducting morphological analyses of spatial objects based on volume parameters, and estimating quantities for earthwork and excavation. While methods like drainage, surface reconstruction, and triangulation suffice for smaller irregular bodies, larger ones introduce heightened complexity. Technological advancements, such as UAV photogrammetry and LiDAR, have introduced efficient point cloud data acquisition methods, bolstering precision and efficiency in calculating volumes for substantial irregular bodies. Notably, open-pit mines, characterized by their dynamic surface alterations, exemplify the challenges posed by large irregular bodies. Ensuring accurate excavation quantity calculations in such mines is pivotal, impacting operational considerations, acceptance, as well as production cost management and project oversight. Thus, this study employs UAV-acquired point cloud data from open-pit mines as a case study. In practice, calculating volumes for substantial irregular bodies often relies on the point cloud slicing method. However, this approach grapples with distinguishing multi-contour boundaries, leading to inaccuracies. To surmount this hurdle, this paper introduces an enhanced point cloud slicing method. The methodology involves segmenting point cloud data at fixed intervals, followed by the segmentation of slice contours using the Euclidean clustering method. Subsequently, the concave hull algorithm extracts the contour polygons of each slice. The final volume calculation involves multiplying the area of each polygon by the spacing and aggregating these products. To validate the efficacy of our approach, we employ model-derived volumes as benchmarks, comparing errors arising from both the traditional slicing method and our proposed technique. Experimental outcomes underscore the superiority of our point cloud volume calculation method, manifesting in an average relative error of 1.17%, outperforming the conventional point cloud slicing method in terms of accuracy.

Visualization and quantitative evaluation of delamination defects in GFRPs via sparse millimeter-wave imaging and image processing

Multilayer glass fiber reinforced polymer (GFRP) has broad applications, playing a pivotal role in various fields including aerospace and wind energy production. These structures may develop internal delamination defects during their manufacture or service, which can significantly impair the integrity and safety of the equipment. Thus, the inspection of such delamination defects during the service life of the equipment is crucial for assessing its operational performance and lifespan. Millimeter-wave (MMW) nondestructive testing (NDT) presents a promising method for detecting these defects in GFRPs, due to its high resolution and powerful penetration characteristics. This paper proposes a visual NDT and quantitative characterization method based on sparse imaging and image processing. This method encompasses the following three key points. (i) To facilitate rapid detection and visualization in monostatic mode, we proposed a detection approach utilizing sparse scanning and imaging. Initially, we developed a sparse sampling control matrix grounded to guide the probe for spatial automatic sparse sampling, thereby enhancing data acquisition efficiency and reducing data volume. Subsequently, we introduced a rapid sparse imaging method, integrating wave spectrum reconstruction and compressed sensing (CS). Beginning from the original data domain, we designed a fast imaging operator employing forward and inverse transformations of spectrum reconstruction imaging. (ii) In order to further refine the quality of the detection images, we proposed an adaptive singular value decomposition (SVD) approach to suppress the coupling signal and enhance the specimen signal. By adaptively adjusting the singular values, we generated a signal with the optimal target-to-background ratio (TBR). (iii) We ascertained the position and area parameters of the delamination defects using the proposed defect quantification method, combining image segmentation and image statistics. Experimental validation affirmed the effectiveness of the proposed method, revealing the following results: 1) The proposed method, despite utilizing a small amount of observational data, generates better visual images while maintaining efficient detection; 2) It can effectively identify all 0.5 mm-thick internal delamination defects in GFRPs, simultaneously retaining high quantitative characterization precision; 3) The positional quantification error remains less than 0.5 mm, and the relative area quantification error is constrained within 8%.

A Forest Wildlife Detection Algorithm Based on Improved YOLOv5s.

A forest wildlife detection algorithm based on an improved YOLOv5s network model is proposed to advance forest wildlife monitoring and improve detection accuracy in complex forest environments. This research utilizes a data set from the Hunan Hupingshan National Nature Reserve in China, to which data augmentation and expansion methods are applied to extensively train the proposed model. To enhance the feature extraction ability of the proposed model, a weighted channel stitching method based on channel attention is introduced. The Swin Transformer module is combined with a CNN network to add a Self-Attention mechanism, thus improving the perceptual field for feature extraction. Furthermore, a new loss function (DIOU_Loss) and an adaptive class suppression loss (L_BCE) are adopted to accelerate the model's convergence speed, reduce false detections in confusing categories, and increase its accuracy. When comparing our improved algorithm with the original YOLOv5s network model under the same experimental conditions and data set, significant improvements are observed, in particular, the mean average precision (mAP) is increased from 72.6% to 89.4%, comprising an accuracy improvement of 16.8%. Our improved algorithm also outperforms popular target detection algorithms, including YOLOv5s, YOLOv3, RetinaNet, and Faster-RCNN. Our proposed improvement measures can well address the challenges posed by the low contrast between background and targets, as well as occlusion and overlap, in forest wildlife images captured by trap cameras. These measures provide practical solutions for enhanced forest wildlife protection and facilitate efficient data acquisition.

Comparative Study of Cloud Forensic Investigation Using ADAM And NIST 800-86 Methods in Private Cloud Computing

As information technology advances, associated risks also increase, particularly in the field of private cloud computing services. These services are subject to potential internal abuse risks, either due to system vulnerabilities or other factors. However, the investigation of these incidents in private cloud computing varies greatly due to the different frameworks and unique characteristics of each cloud service. The lack of a standardized approach to analyzing and assessing investigative processes in cloud computing services has been a persistent problem. This lack of consensus affects the accuracy, efficiency, and data acquisition process when dealing with digital evidence in each method, causing concern among researchers. To overcome this, a comparative study was carried out with a focus on the ADAM (The Advanced Data Acquisition Model) method and the NIST (National Institute of Standards and Technology) method. The goal is to identify the most effective investigative process to deal with cyber attack incidents on both the server and client side of cloud computing services. By testing these methods in a network that is built on private cloud computing services, then the results from this research include the weaknesses and strengths of the ADAM and NIST methods are found when applied to cloud computing case studies and these have not been identified in previous research, then produce recommendations for investigators when conducting investigations on case studies on cloud computing, and in this study managed to find a bug in the ownCloud application version 10.9.1. Then this study also aims to provide researchers with valuable references to carry out analysis and assessment in the investigative process, where standardization is still an unresolved issue.

AN INTERACTION MODEL OF RELAY PROTECTION MEASURING TRANSDUCERS WITH COMPUTING SYSTEMS VIA IIOT TECHNOLOGY

"The object of this research is the interaction model between measuring transducers and computing systems, particularly focusing on its application within the framework of Industrial Internet of Things (IIoT) and Cloud technologies. The imperative driving this study is the increasing demand for precise and efficient data acquisition, protection and processing in various industrial sectors. The research aims to address this need by elucidating an interaction model that optimizes measuring computing systems through IIoT and Cloud technologies. As a result of our research, a comprehensive interaction model that encompasses the entire data flow process was established. This model integrates data acquisition, analytics, real-time communication protocols, and secure data transmission methodologies. It offers a structured framework for acquiring, transmitting, and analyzing data with high accuracy and efficiency. The significance of our results lies in their capacity to resolve critical challenges in data acquisition and processing of the electrical power data, as well as monitoring and protection against electrical faults. These results hold practical value under diverse industrial conditions, including manufacturing, energy production and energy distribution. Keywords: relay protection, open architecture, encryption, data processing, data visualization, internet of things "

Application of quantum key distribution in intelligent security operation and maintenance of power communication networks

With the continuous development of power communication network and the improvement of intelligent degree, network security and operation and maintenance become more and more important. Therefore, this paper proposes a solution for intelligent security operation and maintenance of power communication network based on Quantum key distribution (QKD). QKD is an encryption technology based on the principle of quantum mechanics, which can realize secure Key exchange and resist attacks of traditional encryption algorithms. We use QKD technology to generate and distribute security keys for protecting communication and data transmission in power communication networks. The results show that the efficiency and accuracy of communication data acquisition, transmission, analysis and processing of the Artificial Intelligence for IT Operations system of the power communication network using the power Internet of Things technology are far higher than that of the traditional operation and maintenance technology, which improves the operation and maintenance accuracy of the power communication network by about 25%. The intelligent operation and maintenance system of the power communication network designed by the author, it effectively improves the communication data processing capability and the reliability of operation and maintenance results, and can meet the current practical engineering application requirements. This study provides an innovative solution for the secure operation and maintenance of power communication networks, which is of great significance for protecting the security and stable operation of power communication networks.

Decarbonization of the Food Industry—The Solution for System Design and Operation

Digital transformation in industry is seen as a key technology enabling decarbonization. It is obvious that measures to increase the energy efficiency and integration of renewable energy technologies must be fostered, and in most cases, these measures need a smart combination of several solution pathways. This results in a significant increase in both the design and operation complexity of these systems. However, there is no clear guidance regarding optimized systems. This work presents a standardized methodology enabling the optimized management of the demand and supply side of an industrial process towards decarbonization. The methodology is presented and showcased based on examples from the food industry and demonstrates how to realize energy efficiency measures and the integration of renewable energy by combining the supply side (SS) and demand side (DS) of industrial processes. The results show that data availability and individualized modeling are major challenges in implementing the methodology. To show the impact of optimization, well-selected key performance indicators (KPIs) are a proper means of evaluation and validation, but it is important to select them on purpose. Therefore, future research should focus on efficient data acquisition, expanding expertise, standardized modeling tools, and KPI definitions.

Efficient magnetotelluric data acquisition using irregular stations and compressive sensing reconstruction

A major challenge in magnetotelluric (MT) surveys is the limited number of stations that can be collected due to the cost of receiver instrumentation and the time required to deploy them in large numbers. Many equal-distance acquisition grid used in MT surveys are either over sampling or faced with severe aliasing. We have applied the compressive sensing theory to MT acquisition and developed an efficient acquisition approach that uses optimized station layout with sparse irregular patterns. This acquisition strategy uses the optimized survey designs to acquire a sparse data set first, and then applies a compressive sensing reconstruction to obtain the equivalent dense data set on a regularly spaced grid. Once reconstructed, the dense data can be used in the traditional manner in the subsequent steps of processing, inversion, and interpretation. Our simulations show that comparable information can be obtained as that from a full regular grid in exploration settings by using as fewer as 25% stations. Furthermore, redeploying the same number of stations in a coarse grid used crustal studies can collect much more information. This new acquisition approach can lead to significant savings in both the acquisition cost and operational time and to maximized information from a given number of stations. The substantial savings in the cost, or the increase in the information content, could make many geoscientific investigations viable that might otherwise be impossible due to limited budget.

Advances in remote sensing for sustainable forest management: monitoring and protecting natural resources

Remote sensing has emerged as a powerful tool for the monitoring and management of forests, contributing to the sustainable utilization and protection of natural resources. This paper presents a review of recent advances in remote sensing techniques and technologies for forest management, highlighting their role in monitoring and protecting forests. The integration of remote sensing with other geospatial methods enhances the accuracy and efficiency of data acquisition, aiding in the assessment of forest cover, biomass estimation, disturbance detection, and biodiversity monitoring. Furthermore, the potential of remote sensing for supporting decision-making processes in sustainable forest management is explored, emphasizing its versatility, cost-effectiveness, and ability to provide invaluable insights at both local and global scales. The paper concludes by discussing current challenges and future opportunities in remote sensing applications for sustainable forest management.

Development of high‐throughput wet‐chemical synthesis techniques for material research

AbstractCombining material big data with artificial intelligence constitutes the fourth paradigm of material research. However, the sluggish development of high‐throughput (HT) experimentation has resulted in a lack of experimentally verified and validated material data, which has become the bottleneck of data‐driven material research. Wet‐chemical synthesis has the benefits of low equipment cost and scalability, but traditional wet‐chemical techniques are time‐consuming and ineffective at disclosing the interrelationships between synthesis, compositions, structures, and performance. Constructing a HT workflow in wet‐chemical synthesis is crucial to achieving the preparation of multidimensional materials and establishing the composition–structure–synthesis–performance relationships of functional materials for diverse applications. In this review, the most recent development in HT wet‐chemical synthesis techniques for material research are analyzed in depth. Additionally, the application of HT wet‐chemical synthesis in the fabrication of advanced hydrogels and catalysts is demonstrated through illustrative instances. Finally, this review suggests possible paths for enhancing the efficiency of HT experimentation and data acquisition in order to facilitate more effective material discovery.