Industrial big data platform FSpaceX

The FSpaceX industrial big data platform is a data processing and analysis platform designed based on the Hadoop distributed architecture. The platform can provide functions such as multi-source heterogeneous data collection and storage, data cleaning and filtering, data warehouse construction, application of multiple types of algorithms, comprehensive analysis visualization, etc., to solve various data application pain points that arise in various stages of the entire product lifecycle from customers to manufacturing for enterprises, in order to enhance their economic benefits and competitiveness.

Licong Technology has a professional big data application team to provide users with comprehensive analysis of business needs, sorting out solution plans for business applications, data collection, data storage, data processing and calculation, analysis and mining, and visualization display, clarifying construction goals, setting construction principles, formulating construction plans, and conducting platform development, testing, and deployment. By utilizing data and combining it with production pain points, we continuously learn and evolve, improve decision-making models, and optimize decision-making recommendations.

The overall architecture of the industrial big data platform is divided into three layers: data factory, algorithm engine, and data visualization.

The data factory mainly realizes the collection, audit, and storage of heterogeneous data. Data collection can achieve the integration of various heterogeneous database data based on industrial communication gateways, real-time databases, relational business databases, etc; Data auditing mainly involves verifying the received data, verifying its authenticity, null values, data types, and accuracy; The data storage adopts a combination of HBase, Greenplum, Redis, and other database applications for data storage and analysis, building a distributed storage architecture that can dynamically increase storage nodes and has characteristics such as scalability, multiple replicas, high fault tolerance, and high throughput.

The algorithm engine provides the Spark distributed computing framework, which enables efficient resource utilization based on Hadoop clusters and enables real-time data computation and massive data analysis and mining. Support machine learning library MLib, including notification learning algorithms and tool classes such as classification, clustering, collaborative filtering, and dimensionality reduction.

Data visualization provides modeling functions such as dataset, dynamic and static value set components, data filtering, and data format conversion. It supports drag and drop operations, rich charts, chart linkage, rolling up and down, personalized layout, scheduled emails, GIS and multimedia integration, and other functions.

• Can access heterogeneous data, including time series, images, files, and other data.

• Data cleaning, providing diverse data cleaning and processing capabilities.

• Data archiving, building enterprise data warehouses and storage solutions based on big data.

• Artificial intelligence algorithm models provide intelligent algorithms (machine learning, genetics, etc.) for production scheduling optimization.

• Enrich chart components and use visualization tools to flexibly conduct multidimensional analysis.

• Private deployment plan to ensure the security and reliability of core data assets within the enterprise.