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Data Resource System

Build an ecological environment big data center that integrates data collection, integration, exchange, management, analysis, and collaborative sharing of pollution sources and environmental quality data. It unifies the planning and management of scattered environmental data, improves the standardization level and availability of data, and provides diversified data display forms. By integrating business and professional threads, it fully integrates the big data system of environmental protection business and environmental protection spatial data to form a dual-driven "Environmental Brain" to support the construction of multidisciplinary and diversified data cross-integration analysis applications.

Platform Support System

Based on MapGIS full-space intelligent GIS platform, it integrates the environmental protection industry big data governance platform and IoT platform to support the integrated organization and management of all environmental elements and spatial data resources, providing assurance for multi-dimensional visual analysis and application of multi-source heterogeneous environmental thematic data.

Application Service System

Focusing on cross-topic data integration, cross-business management collaboration, and cross-professional fusion visualization, it supports the construction of integrated environmental protection information applications, precisely matching various user needs, and providing full-space integrated application scenarios of different professional types and business lines.

Diversified Achievement Expression

Achieve integrated display of environmental pollution monitoring and control information, analysis results data, and other thematic content (geological models, geochemical survey data, etc.), creating diversified achievement display scenarios through multi-dimensional integration.

Integrated Monitoring and Control

·Specialized Dashboard

Achieve visual environmental management operations by providing ecological intelligent specialized dashboards and comprehensive environmental business displays. It combines basic geographic information data and environmental protection business data using big data, geographic information, and "Internet+" technologies to realize a cross-professional business collaboration application system.

·Pollution Control Work Supervision

Support viewing the progress of environmental pollution control work through comparison of multi-period environmental survey data and remote sensing image data, intuitively understanding the achievements of pollution control work, supporting government managers in supervising and managing pollution prevention and control work of pollution responsible parties.

Intelligent Business Decision-Making

Closely surrounding the core business processes of environmental protection monitoring and control, it realizes data and environmental protection business management collaboration, and provides intelligent decision-making services:

·Environmental Analysis and Judgment

Integrating professional numerical simulation models to achieve trend analysis and prediction technology of real-time monitoring data, simulating environmental changes under single or multi-factor fusion influences, supporting professional staff in quickly producing environmental pollution investigation reports, identifying key regulatory objects, and conducting analysis and investigation of potential environmental pollution hazard points.

·Environmental Pollution Prediction

Based on environmental pollutant survey data and real-time monitoring data, conduct environmental pollution prediction analysis, simulate and predict pollutant migration changes and aggregation characteristics under different external factor influences, supporting environmental protection professionals in assessing pollution situations, analyzing potential impacts, and providing support for governance work and process control management.

·Pollution Source Tracing

Based on 3D models to extract spatial distribution data of potential pollution sources in the work area, the possible spatial distribution range of pollution sources can be obtained. By comparing the spatial attributes of corresponding monitoring equipment, devices, and locations, and through spatial data retrieval, pollution sources with higher probability of causing pollution incidents can be identified.