Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Enhanced prediction of anisotropic deformation behavior using machine learning with data augmentation

Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition.This characteristic results in a diverse range of flow curves that vary with a deformation condition.This study proposes a novel approach for accurately predicting an anisotropic deformation behavior of wrought Mg alloys using machine learning(ML)with data augmentation.The developed model combines four key strategies from data science:learning the entire flow curves,generative adversarial networks(GAN),algorithm-driven hyperparameter tuning,and gated recurrent unit(GRU)architecture.The proposed model,namely GAN-aided GRU,was extensively evaluated for various predictive scenarios,such as interpolation,extrapolation,and a limited dataset size.The model exhibited significant predictability and improved generalizability for estimating the anisotropic compressive behavior of ZK60 Mg alloys under 11 annealing conditions and for three loading directions.The GAN-aided GRU results were superior to those of previous ML models and constitutive equations.The superior performance was attributed to hyperparameter optimization,GAN-based data augmentation,and the inherent predictivity of the GRU for extrapolation.As a first attempt to employ ML techniques other than artificial neural networks,this study proposes a novel perspective on predicting the anisotropic deformation behaviors of wrought Mg alloys.

Application of 9-component S-wave 3D seismic data to study sedimentary facies and reservoirs in a biogasbearing area:A case study on the Pleistocene Qigequan Formation in Taidong area,Sanhu Depression,Qaidam Basin,NW China

To solve the problems in restoring sedimentary facies and predicting reservoirs in loose gas-bearing sediment,based on seismic sedimentologic analysis of the first 9-component S-wave 3D seismic dataset of China,a fourth-order isochronous stratigraphic framework was set up and then sedimentary facies and reservoirs in the Pleistocene Qigequan Formation in Taidong area of Qaidam Basin were studied by seismic geomorphology and seismic lithology.The study method and thought are as following.Firstly,techniques of phase rotation,frequency decomposition and fusion,and stratal slicing were applied to the 9-component S-wave seismic data to restore sedimentary facies of major marker beds based on sedimentary models reflected by satellite images.Then,techniques of seismic attribute extraction,principal component analysis,and random fitting were applied to calculate the reservoir thickness and physical parameters of a key sandbody,and the results are satisfactory and confirmed by blind testing wells.Study results reveal that the dominant sedimentary facies in the Qigequan Formation within the study area are delta front and shallow lake.The RGB fused slices indicate that there are two cycles with three sets of underwater distributary channel systems in one period.Among them,sandstones in the distributary channels of middle-low Qigequan Formation are thick and broad with superior physical properties,which are favorable reservoirs.The reservoir permeability is also affected by diagenesis.Distributary channel sandstone reservoirs extend further to the west of Sebei-1 gas field,which provides a basis to expand exploration to the western peripheral area.

Data Component:An Innovative Framework for Information Value Metrics in the Digital Economy

The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.

Analysis of Secured Cloud Data Storage Model for Information

This paper was motivated by the existing problems of Cloud Data storage in Imo State University, Nigeria such as outsourced data causing the loss of data and misuse of customer information by unauthorized users or hackers, thereby making customer/client data visible and unprotected. Also, this led to enormous risk of the clients/customers due to defective equipment, bugs, faulty servers, and specious actions. The aim if this paper therefore is to analyze a secure model using Unicode Transformation Format (UTF) base 64 algorithms for storage of data in cloud securely. The methodology used was Object Orientated Hypermedia Analysis and Design Methodology (OOHADM) was adopted. Python was used to develop the security model;the role-based access control (RBAC) and multi-factor authentication (MFA) to enhance security Algorithm were integrated into the Information System developed with HTML 5, JavaScript, Cascading Style Sheet (CSS) version 3 and PHP7. This paper also discussed some of the following concepts;Development of Computing in Cloud, Characteristics of computing, Cloud deployment Model, Cloud Service Models, etc. The results showed that the proposed enhanced security model for information systems of cooperate platform handled multiple authorization and authentication menace, that only one login page will direct all login requests of the different modules to one Single Sign On Server (SSOS). This will in turn redirect users to their requested resources/module when authenticated, leveraging on the Geo-location integration for physical location validation. The emergence of this newly developed system will solve the shortcomings of the existing systems and reduce time and resources incurred while using the existing system.

Inter-agency government information sharing under data-driven blockchain framework

The inter-agency government information sharing(IAGIS)plays an important role in improving service and efficiency of government agencies.Currently,there is still no effective and secure way for data-driven IAGIS to fulfill dynamic demands of information sharing between government agencies.Motivated by blockchain and data mining,a data-driven framework is proposed for IAGIS in this paper.Firstly,the blockchain is used as the core to design the whole framework for monitoring and preventing leakage and abuse of government information,in order to guarantee information security.Secondly,a four-layer architecture is designed for implementing the proposed framework.Thirdly,the classical data mining algorithms PageRank and Apriori are applied to dynamically design smart contracts for information sharing,for the purposed of flexibly adjusting the information sharing strategies according to the practical demands of government agencies for public management and public service.Finally,a case study is presented to illustrate the operation of the proposed framework.

YOLO-Based Damage Detection with StyleGAN3 Data Augmentation for Parcel Information-Recognition System

Damage to parcels reduces customer satisfactionwith delivery services and increases return-logistics costs.This can be prevented by detecting and addressing the damage before the parcels reach the customer.Consequently,various studies have been conducted on deep learning techniques related to the detection of parcel damage.This study proposes a deep learning-based damage detectionmethod for various types of parcels.Themethod is intended to be part of a parcel information-recognition systemthat identifies the volume and shipping information of parcels,and determines whether they are damaged;this method is intended for use in the actual parcel-transportation process.For this purpose,1)the study acquired image data in an environment simulating the actual parcel-transportation process,and 2)the training dataset was expanded based on StyleGAN3 with adaptive discriminator augmentation.Additionally,3)a preliminary distinction was made between the appearance of parcels and their damage status to enhance the performance of the parcel damage detection model and analyze the causes of parcel damage.Finally,using the dataset constructed based on the proposed method,a damage type detection model was trained,and its mean average precision was confirmed.This model can improve customer satisfaction and reduce return costs for parcel delivery companies.

Digital Transformation of Enterprise Human Resource Management Enabled by Big Data

With the continuous development of big data technology,the digital transformation of enterprise human resource management has become a development trend.Human resources is one of the most important resources of enterprises,which is crucial to the competitiveness of enterprises.Enterprises need to attract,retain,and motivate excellent employees,thereby enhancing the innovation ability of enterprises and improving competitiveness and market share in the market.To maintain advantages in the fierce market competition,enterprises need to adopt more scientific and effective human resource management methods to enhance organizational efficiency and competitiveness.At the same time,this paper analyzes the dilemma faced by enterprise human resource management,points out the new characteristics of enterprise human resource management enabled by big data,and puts forward feasible suggestions for enterprise digital transformation.

Research on high energy efficiency and low bit-width floating-point type data for abnormal object detection of transmission lines

Achieving a balance between accuracy and efficiency in target detection applications is an important research topic.To detect abnormal targets on power transmission lines at the power edge,this paper proposes an effective method for reducing the data bit width of the network for floating-point quantization.By performing exponent prealignment and mantissa shifting operations,this method avoids the frequent alignment operations of standard floating-point data,thereby further reducing the exponent and mantissa bit width input into the training process.This enables training low-data-bit width models with low hardware-resource consumption while maintaining accuracy.Experimental tests were conducted on a dataset of real-world images of abnormal targets on transmission lines.The results indicate that while maintaining accuracy at a basic level,the proposed method can significantly reduce the data bit width compared with single-precision data.This suggests that the proposed method has a marked ability to enhance the real-time detection of abnormal targets in transmission circuits.Furthermore,a qualitative analysis indicated that the proposed quantization method is particularly suitable for hardware architectures that integrate storage and computation and exhibit good transferability.

Data Secure Storage Mechanism for IIoT Based on Blockchain

With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of IIoT.Blockchain technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the IIoT.In the traditional blockchain,data is stored in a Merkle tree.As data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based IIoT.Accordingly,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of data.To solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC structure.Secondly,this paper uses PVC instead of the Merkle tree to store big data generated by IIoT.PVC can improve the efficiency of traditional VC in the process of commitment and opening.Finally,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of experiments.This mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.

Hadoop-based secure storage solution for big data in cloud computing environment

In order to address the problems of the single encryption algorithm,such as low encryption efficiency and unreliable metadata for static data storage of big data platforms in the cloud computing environment,we propose a Hadoop based big data secure storage scheme.Firstly,in order to disperse the NameNode service from a single server to multiple servers,we combine HDFS federation and HDFS high-availability mechanisms,and use the Zookeeper distributed coordination mechanism to coordinate each node to achieve dual-channel storage.Then,we improve the ECC encryption algorithm for the encryption of ordinary data,and adopt a homomorphic encryption algorithm to encrypt data that needs to be calculated.To accelerate the encryption,we adopt the dualthread encryption mode.Finally,the HDFS control module is designed to combine the encryption algorithm with the storage model.Experimental results show that the proposed solution solves the problem of a single point of failure of metadata,performs well in terms of metadata reliability,and can realize the fault tolerance of the server.The improved encryption algorithm integrates the dual-channel storage mode,and the encryption storage efficiency improves by 27.6% on average.

基于re3data的中英科学数据仓储平台对比研究

以re3data为数据获取源,选取中英两国406个科学数据仓储为研究对象,从分布特征、责任类型、仓储许可、技术标准及质量标准等5个方面、11个指标对两国科学数据仓储的建设情况进行对比分析,试图为我国数据仓储的可持续发展提出建议:广泛联结国内外异质机构,推进多学科领域的交流与合作,有效扩充仓储许可权限与类型,优化技术标准的应用现况,提高元数据使用的灵活性。

A multiple sensitive attributes data publishing method with guaranteed information utility

Data publishing methods can provide available information for analysis while preserving privacy.The multiple sensitive attributes data publishing,which preserves the relationship between sensitive attributes,may keep many records from being grouped and bring in a high record suppression ratio.Another category of multiple sensitive attributes data publishing,which reduces the possibility of record suppression by breaking the relationship between sensitive attributes,cannot provide the sensitive attributes association for analysis.Hence,the existing multiple sensitive attributes data publishing fails to fully account for the comprehensive information utility.To acquire a guaranteed information utility,this article defines comprehensive information loss that considers both the suppression of records and the relationship between sensitive attributes.A heuristic method is leveraged to discover the optimal anonymity scheme that has the lowest comprehensive information loss.The experimental results verify the practice of the proposed data publishing method with multiple sensitive attributes.The proposed method can guarantee information utility when compared with previous ones.

Geological characteristics and exploration breakthroughs of coal rock gas in Carboniferous Benxi Formation,Ordos Basin,NW China

To explore the geological characteristics and exploration potential of the Carboniferous Benxi Formation coal rock gas in the Ordos Basin,this paper presents a systematic research on the coal rock distribution,coal rock reservoirs,coal rock quality,and coal rock gas features,resources and enrichment.Coal rock gas is a high-quality resource distinct from coalbed methane,and it has unique features in terms of burial depth,gas source,reservoir,gas content,and carbon isotopic composition.The Benxi Formation coal rocks cover an area of 16×104km^(2),with thicknesses ranging from 2 m to 25 m,primarily consisting of bright and semi-bright coals with primitive structures and low volatile and ash contents,indicating a good coal quality.The medium-to-high rank coal rocks have the total organic carbon(TOC)content ranging from 33.49%to 86.11%,averaging75.16%.They have a high degree of thermal evolution(Roof 1.2%-2.8%),and a high gas-generating capacity.They also have high stable carbon isotopic values(δ13C1of-37.6‰to-16‰;δ13C2of-21.7‰to-14.3‰).Deep coal rocks develop matrix pores such as gas bubble pores,organic pores,and inorganic mineral pores,which,together with cleats and fractures,form good reservoir spaces.The coal rock reservoirs exhibit the porosity of 0.54%-10.67%(averaging 5.42%)and the permeability of(0.001-14.600)×10^(-3)μm^(2)(averaging 2.32×10^(-3)μm^(2)).Vertically,there are five types of coal rock gas accumulation and dissipation combinations,among which the coal rock-mudstone gas accumulation combination and the coal rock-limestone gas accumulation combination are the most important,with good sealing conditions and high peak values of total hydrocarbon in gas logging.A model of coal rock gas accumulation has been constructed,which includes widespread distribution of medium-to-high rank coal rocks continually generating gas,matrix pores and cleats/fractures in coal rocks acting as large-scale reservoir spaces,tight cap rocks providing sealing,source-reservoir integration,and five types of efficient enrichment patterns(lateral pinchout complex,lenses,low-amplitude structures,nose-like structures,and lithologically self-sealing).According to the geological characteristics of coal rock gas,the Benxi Formation is divided into 8 plays,and the estimated coal rock gas resources with a buried depth of more than 2000 m are more than 12.33×10^(12)m^(3).The above understandings guide the deployment of risk exploration.Two wells drilled accordingly obtained an industrial gas flow,driving the further deployment of exploratory and appraisal wells.Substantial breakthroughs have been achieved,with the possible reserves over a trillion cubic meters and the proved reserves over a hundred billion cubic meters,which is of great significance for the reserves increase and efficient development of natural gas in China.

Reservoir quality evaluation of the Narimba Formation in Bass Basin,Australia:Implications from petrophysical analysis,sedimentological features,capillary pressure and wetting fluid saturation

The evaluation of reservoir quality was accomplished on the Late Paleocene to Early Eocene Narimba Formation in Bass Basin,Australia.This study involved combination methods such as petrophysical analysis,petrography and sedimentological studies,reservoir quality and fluid flow units from derivative parameters,and capillary pressure and wetting fluid saturation relationship.Textural and diagenetic features are affecting the reservoir quality.Cementation,compaction,and presence of clay minerals such as kaolinite are found to reduce the quality while dissolution and secondary porosity are noticed to improve it.It is believed that the Narimba Formation is a potential reservoir with a wide range of porosity and permeability.Porosity ranges from 3.1%to 25.4%with a mean of 15.84%,while permeability ranges between 0.01 mD and 510 mD,with a mean of 31.05 mD.Based on the heterogenous lithology,the formation has been categorized into five groups based on permeability variations.Group I showed an excellent to good quality reservoir with coarse grains.The impacts of both textural and diagenetic features improve the reservoir and producing higher reservoir quality index(RQI)and flow zone indicators(FZI)as well as mostly mega pores.The non-wetting fluid migration has the higher possibility to flow in the formation while displacement pressure recorded as zero.Group II showed a fair quality reservoir with lower petrophysical properties in macro pores.The irreducible water saturation is increasing while the textural and digenetic properties are still enhancing the reservoir quality.Group III reflects lower quality reservoir with mostly macro pores and higher displacement pressure.It may indicate smaller grain size and increasing amount of cement and clay minerals.Group IV,and V are interpreted as a poor-quality reservoir that has lower RQI and FZI.The textural and digenetic features are negatively affecting the reservoir and are leading to smaller pore size and pore throat radii(r35)values to be within the range of macro,meso-,micro-,and nano pores.The capillary displacement pressure curves of the three groups show increases reaching the maximum value of 400 psia in group V.Agreement with the classification of permeability,r35 values,and pore type can be used in identifying the quality of reservoir.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Dual Additives for Stabilizing Li Deposition and SEI Formation in Anode-Free Li-Metal Batteries

Anode-free Li-metal batteries are of significant interest to energy storage industries due to their intrinsically high energy.However,the accumulative Li dendrites and dead Li continuously consume active Li during cycling.That results in a short lifetime and low Coulombic efficiency of anode-free Li-metal batteries.Introducing effective electrolyte additives can improve the Li deposition homogeneity and solid electrolyte interphase(SEI)stability for anode-free Li-metal batteries.Herein,we reveal that introducing dual additives,composed of LiAsF6 and fluoroethylene carbonate,into a low-cost commercial carbonate electrolyte will boost the cycle life and average Coulombic efficiency of NMC‖Cu anode-free Li-metal batteries.The NMC‖Cu anode-free Li-metal batteries with the dual additives exhibit a capacity retention of about 75%after 50 cycles,much higher than those with bare electrolytes(35%).The average Coulombic efficiency of the NMC‖Cu anode-free Li-metal batteries with additives can maintain 98.3%over 100 cycles.In contrast,the average Coulombic efficiency without additives rapidly decline to 97%after only 50 cycles.In situ Raman measurements reveal that the prepared dual additives facilitate denser and smoother Li morphology during Li deposition.The dual additives significantly suppress the Li dendrite growth,enabling stable SEI formation on anode and cathode surfaces.Our results provide a broad view of developing low-cost and high-effective functional electrolytes for high-energy and long-life anode-free Li-metal batteries.

Multiple enrichment mechanisms of organic matter in the Fengcheng Formation of Mahu Sag,Junggar Basin,NW China

Based on core and thin section data,the source rock samples from the Fengcheng Formation in the Mahu Sag of the Junggar Basin were analyzed in terms of zircon SIMS U-Pb geochronology,organic carbon isotopic composition,major and trace element contents,as well as petrology.Two zircon U-Pb ages of(306.0±5.2)Ma and(303.5±3.7)Ma were obtained from the first member of the Fengcheng Formation.Combined with carbon isotopic stratigraphy,it is inferred that the depositional age of the Fengcheng Formation is about 297-306 Ma,spanning the Carboniferous-Permian boundary and corresponding to the interglacial period between C4 and P1 glacial events.Multiple increases in Hg/TOC ratios and altered volcanic ash were found in the shale rocks of the Fengcheng Formation,indicating that multiple phases of volcanic activity occurred during its deposition.An interval with a high B/Ga ratio was found in the middle of the second member of the Fengcheng Formation,associated with the occurrence of evaporite minerals and reedmergnerite,indicating that the high salinity of the water mass was related to hydrothermal activity.Comprehensive analysis suggests that the warm and humid climate during the deposition of Fengcheng Formation is conducive to the growth of organic matter such as algae and bacteria in the lake,and accelerates the continental weathering,driving the input of nutrients.Volcanic activities supply a large amount of nutrients and stimulate primary productivity.The warm climate and high salinity are conducive to water stratification,leading to water anoxia that benefits organic matter preservation.The above factors interact and jointly control the enrichment of organic matter in the Fengcheng Formation of Mahu Sag.

The coupling control of biological precursors and environmental factors onβ-carotane enrichment in alkaline lacustrine source rocks:A case study from the Fengcheng formation in the western Junggar Basin,NW China

The organic-rich mudstones and dolostones of the Permian Fengcheng Formation(Fm.)are typically alkaline lacustrine source rocks,which are typified by impressively abundantβ-carotane.Abundant β-carotane has been well acknowledged as an effective indicator of biological sources or depositional environments.However,the specific biological sources of β-carotane and the coupling control of biological sources and environmental factors on the enrichment of β-carotane in the Fengcheng Fm.remains obscure.Based on a comprehensive investigation of the bulk,molecular geochemistry,and organic petrology of sedimentary rocks and the biochemistry of phytoplankton in modern alkaline lakes,we proposed a new understanding of the biological precursors of β-carotane and elucidated the enrichment mechanism of β-carotane in the Fengcheng Fm.The results show that the biological precursors crucially control the enrichment of β-carotane in the Fengcheng Fm.The haloalkaliphilic cyanobacteria are the primary biological sources of β-carotane,which is suggested by a good positive correlation between the 2-methylhopane index,7-+8-methyl heptadecanes/C_(max),C_(29%),and β-carotane/C_(max)in sedimentary rocks and the predominance of cyanobacteria with abundantβ-carotene in modern alkaline lakes.The enrichment of β-carotane requires the reducing condition,and the paleoredox state that affects the enrichment of β-carotane appears to have a threshold.The paleoclimate conditions do not considerably impact the enrichment of β-carotane,but they have some influence on the water's paleosalinity by affecting evaporation and precipitation.While it does not directly affect the enrichment of β-carotane in the Fengcheng Fm.,paleosalinity does have an impact on the cyanobacterial precursor supply and the preservation conditions.