Research on Cadre Appointment Decision-Making Based on Data Analysis

In this paper, we propose a novel framework of integration of multi-source heterogeneous data and intelligent maintenance decision support for High-speed Railway Signal System. The framework uses the multi-agent system to deal with the multi-source data. Each agent has its own task varying from transferring RDB (Rational Database) data to RDF (Resource Description Framework) file, and then fusing local RDF to global RDF by Apache Jena API and D2RQ. In addition, a CBR (Case-Based Reasoning) based intelligent maintenance decision support for the fault diagnosis has been constructed by combing the rough set with the Euclidean distance. The correctness and efficiency of the proposed framework has been verified by maintenance data derived from VOBE (Vehicle Equipment On Board) of CTCS-300T operating on Wuhan-Guangzhou Line.

Exercise and training data is a core asset for enhancing military training and operational decision-making capabilities. Current military exercise and training data faces management challenges such as multi-source heterogeneity, inconsistent standards, and low data quality, which severely restrict its value realization. To address these challenges, this paper designs and implements a data governance system based on big data and data warehouse technologies. This system first constructs a data resource catalog and standard system, utilizing technologies like Flink and HDFS inotify to achieve the aggregation and integration of multi-source heterogeneous data. Through standardized ETL processes and a layered data warehouse, the data undergoes deep processing and organization. Finally, a unified data service and visualization platform supports upper-layer decision-making analysis. Application practices demonstrate that this system significantly improves data standardization, processing efficiency, and analytical capability, providing key technical support for the digital and intelligent transformation of military training.

Space is the fundamental carrier for production, living, and ecological activities, and optimizing the spatial pattern is of vital importance to promote regional sustainable development. To achieve this goal, the core issues are to identify the risks of resource and environmental constraints of development and to realize the rational distribution of human living space. Based on the integration of multisource heterogeneous data, taking Yunnan Province, a typical mountainous area in China, as an example, this research proposes a multi-object suitability evaluation method based on 50 × 50 m grid data at the provincial scale. We build a spatial conflict analysis model to identify production–living–ecological space (PLES) and propose governance suggestions for different functional areas. The results show that (1) areas suitable for ecology make up the greatest proportion of Yunnan Province, but areas with living and ecological functions show obvious spatial complementarity; (2) areas suitable for production are restricted by steep slope, geological hazards and fragmented pattern; (3) areas suitable for living is rare, and they are mainly concentrated in the plains of central Yunnan; and (4) twenty-seven percent of area has potential spatial conflicts, among which 4.38% of the area is all suitable for production–living–ecological. The production–living advantage areas are concentrated in the central Yunnan UA (Urban agglomeration), which has a high spatial overlap. These results are expected to provide valuable insights to support comprehensive multifunctional spatial utilization and sustainable development in mountainous areas.

The control and eradication of invasive alien plants in South Africa where about 10.1 million hectares of land has been invaded requires information about their extent and distribution. In this study it has been shown that in order to effectively and economically control invasive alien plants in the St. Lucia Heritage Site, the Working for Water organisation requires data on the extent and distribution of invasion not only of its operational areas but also of the surrounding areas. Information on extent and distribution requires the compiling data from multiple sources. Multi source data integration has been shown to be a troublesome exercise due to differences in methods in data acquisition, representation, coding, classification, scales, map projections, and mapping formats. Such data integration can only be made possible when organizations with a common goal adopt a common approach that would facilitate sharing data and information. In this study it has also been shown that multi-source alien species data integration and comparability of data amongst organizations was made possible through the adoption and successful implementation of data formats, data exchange, and user design standards. It has also been shown that mapping the extent and distribution of invasive alien plants requires the development of a geographic information system known as the Alien Plants Management Information System (APMIS). APMIS integrates data from multiple sources through the development and adoption of standards known as Geocode. Geocode is made up of the Land Mapping Unit (known as NBAL) and a date field so that both the spatial and temporal state of alien distribution and eradication can be recorded. This coding standard does not only indicate which location and time a species was found but also ensures comparability over time on how the distribution and eradication was progressing. The Geocode (NBAL + Date) can be very useful in modelling both the spatial and temporal variability of land mapping units and can be applicable in the areas of agricultural weed eradication programmes and in complex humanitarian environments where multiple organizations need to share data.

The development of an Automated Subgrade Monitoring System (ASMS) represents an important means of advancing the digitalization and intelligent operation and maintenance of high-speed railway (HSR) subgrades. However, current systems exhibit considerable constraints in managing multi-source heterogeneous data and effectively integrating engineering information. This study addresses these limitations through the design and implementation of a Building Information Modeling (BIM)-integrated Data Management and Analysis Subsystem (DMAS) founded on digital twin theory. The system establishes an engineering data management architecture optimized for the efficient integration and governance of heterogeneous multi-source data. Through the implementation of the Entity-Relationship (E-R) model, complex engineering data are transformed into an atomic-level storage structure, enhancing data consistency and retrieval efficiency. The system incorporates a hybrid sharding strategy and hierarchical access control mechanism to enhance scalability and security. Furthermore, a BIM attribute extension and logical mapping engine facilitates seamless integration between the physical model and sensor data, effectively addressing the frequently overlooked challenge of data coupling. The integration of a lightweight WebGL rendering engine enables cross-platform model visualization, dynamic data binding, real-time interaction, and precise alarm localization. Focusing on the response layer’s core functions, DMAS establishes a structured and functionally coordinated data support system, demonstrating robust engineering adaptability in multi-source data governance, data modeling, and visualized interaction. Through its modular and extensible architecture, DMAS enhances system performance while providing capacity for future intelligent evolution toward advanced digital twin capabilities, thereby establishing a solid technical foundation for intelligent infrastructure monitoring and management.

With continuous innovation of the “Internet+” and development of related industries, huge amounts of data need to be integrated on the basis of spatial data analysis and processing. Therefore, efficient integration of multi-source heterogeneous spatial data and data interoperability requirements has become more and more urgent. The design of a multisource heterogeneous vector spatial data integration scheme on WebGIS has been proposed by GeoJSON as the universal transformation format, based on the analysis of the existing multi-source heterogeneous spatial data conversion method. Finally, a prototype of the online prototype system was developed with. Net platform to verify the feasibility and practicability of the scheme. The scheme has the characteristics of high efficiency, easy to expand and low cost, and provides a new way to realize the data integration of “Internet+”.

Python web crawler technology, which automatically and massively getting information from the Internet by mimicking net users' browsing behavior, is a basic supporting technique to extract and integrate multi-source heterogeneous data in the field of Infodemiology. There are two types of Python web crawler: simple and massive-scale, both collect information simultaneously from the database establishment. Advantages of this technique are characterized as: being simple syntax, in high flexibility and low cost in learning and maintenance. Contents of the current application scenarios include surveillance, implementation and evaluation of health intervention programs on public health issues, as well as on smart doctor seeking. For the last two years, the Chinese government started to encourage the integration and utilization of multi-source heterogeneous data including internet information. Hence, the number of application scenarios for Python web crawler technology are bound to increase in the foreseeable future. Corresponding matched talent cultivations and technical innovations are suggested to add to the current education and research systems on public health issues.

A novel image-based transfer learning framework for cross-domain HVAC fault diagnosis: From multi-source data integration to knowledge sharing strategies

Objective:To provide effective information resource for military medical support during strategic war,so as to provide a basis for the evidence-informed policy making in military medical service.Methods: A large quantity of multi-source and isomerism data were integrated and organized by the techniques of data warehouse and geography markup language.Multi-dimension subject models for decision-making in military medical commanding were constructed.Results: The integration of multi-source and isomerism data was achieved and comprehensive database was established for optimal decision-making in military medical service,which includes space data warehouse and its multi-dimension model for decision-making.The design of a 6-layer structure of data warehouse was also put forward.Conclusion: The database provides a comprehensive data platform for the optimal decision support of military medical service,and can achieve the decision support for the evidence-informed decision making in military medical service.

In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model’s robust capability to address the unique challenges presented by the language’s structural intricacies.

This paper aims to build a new fault diagnosis system based on big data and artificial intelligence technology. This study first expounds the application value and prospect of big data technology and intelligent algorithm in the fault diagnosis of ship power system, and realizes the effective integration and mining of massive, multi-source and heterogeneous ship power system operation data through in-depth analysis of the characteristics of ship power system operation data. Secondly, in view of the nonlinear and time-varying characteristics of ship power system faults, this study designs and implements a fault diagnosis model that integrates intelligent algorithms such as machine learning and deep learning. The model can automatically extract key fault features, realize early warning and accurate identification of potential faults, and improve the accuracy and response speed of fault diagnosis through continuous learning and optimization. Finally, this study applies the big data intelligent system for fault diagnosis of computer-aided ship power system to a practical case to verify its effectiveness and practicability in improving fault diagnosis efficiency, reducing maintenance costs, and ensuring navigation safety. The research results have important theoretical significance and practical value for improving the level of ship operation and maintenance management and ensuring the stable operation of ship power system.

With the growing importance of the fuzzy spatiotemporal data in information application, there is an increasing need for researching on the integration method of multi-source heterogeneous fuzzy spatiotemporal data. In this paper, we first propose a fuzzy spatiotemporal RDF graph model based on RDF (Resource Description Framework) that proposed by the World Wide Web Consortium (W3C) to represent data in triples (subject, predicate, object). Secondly, we analyze and classify the related heterogeneous problems of multi-source heterogeneous fuzzy spatiotemporal data, and use the fuzzy spatiotemporal RDF graph model to define the corresponding rules to solve these heterogeneous problems. In addition, based on the characteristics of RDF triples, we analyze the heterogeneous problem of multi-source heterogeneous fuzzy spatiotemporal data integration in RDF triples, and provide the integration methods FRDFG in this paper. Finally, we report our experiments results to validate our approach and show its significant superiority.

The method by the integration of multi-source data including remote sensing data, ground spectral measurement data and other in situ monitoring data, is presented in this paper to construct a quantitative water quality inversion model. The upper region of Whangpoo River in Shanghai is selected as a study area and the dissolved oxygen is chose as a typical water quality indicator. We first process the remote sensing imagery and field spectrometer data to obtain the accurate water reflectance. An inversion model of dissolved oxygen is then derived from the modeling analysis between the water quality data and the reflectance. The accuracy of the model is further confirmed, and this model is applied to invert the dissolved oxygen distribution in Whangpoo River upper region. The inverted water quality distribution has a high consistency with the practical case. This proves that the method by integration of multi-source data is an effective way to monitor the water quality.

With the rapid development of domestic power grid technology, it is difficult for traditional power grids to solve the low-voltage distribution end multi-source data comprehensive processing efficiency and poor cross-data fusion effect. At present, with the vigorous development of edge computing, cloud containers and 5G technologies, new methods are provided for the processing and integration of multi-source data in low-voltage distribution networks. This paper analyzes edge computing and cloud container technologies to design a reasonable framework for low-voltage power distribution data processing to help the construction of smart grids.

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%.