In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and ot...In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.展开更多
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heter...With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.展开更多
In order to solve the data surge brought by largescale increasing of mobile de-vices, Massive MIMO ultra dense networking can greatly improve the system spectral efficiency and energy efficiency. It plays an important...In order to solve the data surge brought by largescale increasing of mobile de-vices, Massive MIMO ultra dense networking can greatly improve the system spectral efficiency and energy efficiency. It plays an important role in coping with the exponential growth of the business, but also brought big problems and challenges. For heterogeneous ultra dense networks, both macro and femto users are facing with both the cross-layer interference and co-layer in-terference. The precoding technology studied in this paper resolves the cross-layer interference and co-layer interference for macro users and femto users, and lays a theoretical foundation for the deployment of heterogeneous and ultra dense networks.展开更多
Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development...Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.展开更多
In this paper,we consider the distributed inference for heterogeneous linear models with massive datasets.Noting that heterogeneity may exist not only in the expectations of the subpopulations,but also in their varian...In this paper,we consider the distributed inference for heterogeneous linear models with massive datasets.Noting that heterogeneity may exist not only in the expectations of the subpopulations,but also in their variances,we propose the heteroscedasticity-adaptive distributed aggregation(HADA)estimation,which is shown to be communication-efficient and asymptotically optimal,regardless of homoscedasticity or heteroscedasticity.Furthermore,a distributed test for parameter heterogeneity across subpopulations is constructed based on the HADA estimator.The finite-sample performance of the proposed methods is evaluated using simulation studies and the NYC flight data.展开更多
To address the underutilization of Chinese research materials in nonferrous metals,a method for constructing a domain of nonferrous metals knowledge graph(DNMKG)was established.Starting from a domain thesaurus,entitie...To address the underutilization of Chinese research materials in nonferrous metals,a method for constructing a domain of nonferrous metals knowledge graph(DNMKG)was established.Starting from a domain thesaurus,entities and relationships were mapped as resource description framework(RDF)triples to form the graph’s framework.Properties and related entities were extracted from open knowledge bases,enriching the graph.A large-scale,multi-source heterogeneous corpus of over 1×10^(9) words was compiled from recent literature to further expand DNMKG.Using the knowledge graph as prior knowledge,natural language processing techniques were applied to the corpus,generating word vectors.A novel entity evaluation algorithm was used to identify and extract real domain entities,which were added to DNMKG.A prototype system was developed to visualize the knowledge graph and support human−computer interaction.Results demonstrate that DNMKG can enhance knowledge discovery and improve research efficiency in the nonferrous metals field.展开更多
As a product of the deep integration between next-generation information technology and industrial systems,digital twin technology has demonstrated significant advantages in real-time monitoring,predictive maintenance...As a product of the deep integration between next-generation information technology and industrial systems,digital twin technology has demonstrated significant advantages in real-time monitoring,predictive maintenance,and optimization decision-making for thermal power plants.To address challenges such as low equipment efficiency,high maintenance costs,and difficulties in safety risk management in traditional thermal power plants,this study developed a digital twin simulation system that covers the entire lifecycle of power generation units.The system achieves real-time collection and processing of critical parameters such as temperature,pressure,and flow rate through a collaborative architecture integrating multi-source heterogeneous sensor networks with Programmable Logic Controllers(PLCs).A three-tier processing framework handles data preprocessing,feature extraction,and intelligent analysis,while establishing a hybrid storage system combining time-series databases and relational databases to enable millisecond-level queries and data traceability.The simulation model development module employs modular design methodology,integrating multi-physics coupling algorithms including computational fluid dynamics(CFD)and thermal circulation equations.Automated parameter calibration is achieved through intelligent optimization algorithms,with model accuracy validated via unitlevel verification,system-level cascaded debugging tests,and virtual test platform simulations.Based on the modular layout strategy,the user interface and interaction module integrates 3D plant panoramic view,dynamic equipment model and multi-mode interaction channel,supports cross-terminal adaptation of PC,mobile terminal and control screen,and improves fault handling efficiency through AR assisted diagnosis function.展开更多
The rapid urbanization and structural imbalances in Chinese megacities have exacerbated the housing supplydemand mismatch,creating an urgent need for fine-scale diagnostic tools.This study addresses this critical gap ...The rapid urbanization and structural imbalances in Chinese megacities have exacerbated the housing supplydemand mismatch,creating an urgent need for fine-scale diagnostic tools.This study addresses this critical gap by developing the Housing Contradiction Evaluation Weighted Index(HCEWI)model,making three key contributions to high-resolution housing monitoring.First,we establish a tripartite theoretical framework integrating dynamic population pressure(PPI),housing supply potential(HSI),and functional diversity(HHI).The PPI innovatively combines mobile signaling data with principal component analysis to capture real-time commuting patterns,while the HSI introduces a novel dual-criteria system based on Local Climate Zones(LCZ),weighted by building density and residential function ratio.Second,we develop a spatiotemporal coupling architecture featuring an entropy-weighted dynamic integration mechanism with self-correcting modules,demonstrating robust performance against data noise.Third,our 25-month longitudinal analysis in Shenzhen reveals significant findings,including persistent bipolar clustering patterns,contrasting volatility between peripheral and core areas,and seasonal policy responsiveness.Methodologically,we advance urban diagnostics through 500-meter grid monthly monitoring and process-oriented temporal operators that reveal“tentacle-like”spatial restructuring along transit corridors.Our findings provide a replicable framework for precision housing governance and demonstrate the transformative potential of mobile signaling data in implementing China’s“city-specific policy”approach.We further propose targeted intervention strategies,including balance regulation for high-contradiction zones,Transit-Oriented Development(TOD)activation for low-contradiction clusters,and dynamic land conversion mechanisms for transitional areas.展开更多
基金supported by National Natural Science Foundation of China(12174350)Science and Technology Project of State Grid Henan Electric Power Company(5217Q0240008).
文摘In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
文摘With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
文摘In order to solve the data surge brought by largescale increasing of mobile de-vices, Massive MIMO ultra dense networking can greatly improve the system spectral efficiency and energy efficiency. It plays an important role in coping with the exponential growth of the business, but also brought big problems and challenges. For heterogeneous ultra dense networks, both macro and femto users are facing with both the cross-layer interference and co-layer in-terference. The precoding technology studied in this paper resolves the cross-layer interference and co-layer interference for macro users and femto users, and lays a theoretical foundation for the deployment of heterogeneous and ultra dense networks.
基金National Key Research and Development Program of China(No.2023YFB3907103).
文摘Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.
基金Supported by the National Science Foundation of China(Grant No.12271014)China Postdoctoral Science Foundation(Grant No.2022M720334)MOE(Ministry of Education in China)Project of Humanities and Social Sciences(Grant No.23YJCZH259)。
文摘In this paper,we consider the distributed inference for heterogeneous linear models with massive datasets.Noting that heterogeneity may exist not only in the expectations of the subpopulations,but also in their variances,we propose the heteroscedasticity-adaptive distributed aggregation(HADA)estimation,which is shown to be communication-efficient and asymptotically optimal,regardless of homoscedasticity or heteroscedasticity.Furthermore,a distributed test for parameter heterogeneity across subpopulations is constructed based on the HADA estimator.The finite-sample performance of the proposed methods is evaluated using simulation studies and the NYC flight data.
文摘To address the underutilization of Chinese research materials in nonferrous metals,a method for constructing a domain of nonferrous metals knowledge graph(DNMKG)was established.Starting from a domain thesaurus,entities and relationships were mapped as resource description framework(RDF)triples to form the graph’s framework.Properties and related entities were extracted from open knowledge bases,enriching the graph.A large-scale,multi-source heterogeneous corpus of over 1×10^(9) words was compiled from recent literature to further expand DNMKG.Using the knowledge graph as prior knowledge,natural language processing techniques were applied to the corpus,generating word vectors.A novel entity evaluation algorithm was used to identify and extract real domain entities,which were added to DNMKG.A prototype system was developed to visualize the knowledge graph and support human−computer interaction.Results demonstrate that DNMKG can enhance knowledge discovery and improve research efficiency in the nonferrous metals field.
文摘As a product of the deep integration between next-generation information technology and industrial systems,digital twin technology has demonstrated significant advantages in real-time monitoring,predictive maintenance,and optimization decision-making for thermal power plants.To address challenges such as low equipment efficiency,high maintenance costs,and difficulties in safety risk management in traditional thermal power plants,this study developed a digital twin simulation system that covers the entire lifecycle of power generation units.The system achieves real-time collection and processing of critical parameters such as temperature,pressure,and flow rate through a collaborative architecture integrating multi-source heterogeneous sensor networks with Programmable Logic Controllers(PLCs).A three-tier processing framework handles data preprocessing,feature extraction,and intelligent analysis,while establishing a hybrid storage system combining time-series databases and relational databases to enable millisecond-level queries and data traceability.The simulation model development module employs modular design methodology,integrating multi-physics coupling algorithms including computational fluid dynamics(CFD)and thermal circulation equations.Automated parameter calibration is achieved through intelligent optimization algorithms,with model accuracy validated via unitlevel verification,system-level cascaded debugging tests,and virtual test platform simulations.Based on the modular layout strategy,the user interface and interaction module integrates 3D plant panoramic view,dynamic equipment model and multi-mode interaction channel,supports cross-terminal adaptation of PC,mobile terminal and control screen,and improves fault handling efficiency through AR assisted diagnosis function.
基金National Natural Science Foundation of China(No.42101346)Undergraduate Training Programs for Innovation and Entrepreneurship of Wuhan University(GeoAI Special Project)(No.202510486196).
文摘The rapid urbanization and structural imbalances in Chinese megacities have exacerbated the housing supplydemand mismatch,creating an urgent need for fine-scale diagnostic tools.This study addresses this critical gap by developing the Housing Contradiction Evaluation Weighted Index(HCEWI)model,making three key contributions to high-resolution housing monitoring.First,we establish a tripartite theoretical framework integrating dynamic population pressure(PPI),housing supply potential(HSI),and functional diversity(HHI).The PPI innovatively combines mobile signaling data with principal component analysis to capture real-time commuting patterns,while the HSI introduces a novel dual-criteria system based on Local Climate Zones(LCZ),weighted by building density and residential function ratio.Second,we develop a spatiotemporal coupling architecture featuring an entropy-weighted dynamic integration mechanism with self-correcting modules,demonstrating robust performance against data noise.Third,our 25-month longitudinal analysis in Shenzhen reveals significant findings,including persistent bipolar clustering patterns,contrasting volatility between peripheral and core areas,and seasonal policy responsiveness.Methodologically,we advance urban diagnostics through 500-meter grid monthly monitoring and process-oriented temporal operators that reveal“tentacle-like”spatial restructuring along transit corridors.Our findings provide a replicable framework for precision housing governance and demonstrate the transformative potential of mobile signaling data in implementing China’s“city-specific policy”approach.We further propose targeted intervention strategies,including balance regulation for high-contradiction zones,Transit-Oriented Development(TOD)activation for low-contradiction clusters,and dynamic land conversion mechanisms for transitional areas.
