According to recent data released by the National Bureau of Statistics, China’s urbanization rate has reached 67%, marking a new phase of urban development shifting from "incremental expansion" to "high-quality development."

As the lifeline of urban operation and development, the "urban lifeline" refers to the core infrastructure system that maintains normal city functioning, safeguards residents’ basic living needs, and ensures urban safety. With continuous urban expansion, urban lifelines have rapidly grown and diversified, posing challenges to operational management and safety prevention and control.

Pain Points of Urban Lifelines: Interwoven Risks and Growing Challenges

Urban Disaster Coupling

Urban safety risks exhibit new characteristics, such as disaster coupling, accident chains (e.g., gas leaks triggering explosions, drainage blockages exacerbating waterlogging). Traditional single-risk monitoring methods are inadequate to address these complexities.

Information Collaboration and Sharing Barriers
Urban lifelines involve multiple departments and domains. Data across departments remain isolated, with inconsistent formats and standards, making data sharing and collaboration difficult. This severely limits the safety operation and emergency response capabilities of urban lifelines.

Lagging Operational Management
Traditional urban lifeline management relies on manual inspections and empirical judgments, lacking advanced technical support and scientific management methods. Hidden dangers are easily overlooked or delayed, preventing proactive prevention and control. This results in sluggish responses to emergencies by municipal authorities.

Sefon Software Large Models: Breaking Barriers for Urban Lifelines
Leveraging its self-developed SDC LM Large Model Development and Service Platform, Sefon Software integrates technologies such as the Internet of Things (IoT), cloud computing, and big data. Through pre-trained large model development and application scenario construction, it focuses on urban safety operation scenarios, injecting strong momentum into the digital construction of urban lifelines.

Platform Features:

End-to-End Large Model Development

Provides a complete toolchain from multi-source data ingestion and data annotation to knowledge graph enhancement tools. Establishes an end-to-end training system integrating pre-training, SFT, LoRA, and other technologies to deliver one-stop services from model training and optimization to deployment and inference. Combined with self-developed quantization compression and inference acceleration engines, it enhances service response speed and delivers a more efficient and stable inference experience.

Plug-and-Play Diverse Models

Includes built-in pre-trained models for multiple domains (e.g., Q&A, NL2SQL, multimodal interaction), reducing model training and development costs. Flexibly adapts to business needs through customizable Prompt templates and supports rapid onboarding, fine-tuning, and one-click deployment of third-party large models, building an open ecosystem. Enables efficient model maintenance and expansion to meet diverse scenario requirements.

Efficient Application Choreography

Utilizes low-code large model application orchestration tools to quickly build and deploy large model business applications and services. Optimizes multimodal intent task execution with vector database technology. Features a rich plugin ecosystem for seamless integration with business systems and tools, enabling complex high-level Q&A responses and command task issuance through simple configuration.

Digital Construction: Extending Urban Safety and Development Lines

To address the complex challenges of interwoven risks in the new era, Sefon Software centers on an industry-level large model platform to create an end-to-end solution for urban lifeline safety engineering, helping urban safety governance step into a new era of "proactive prevention and control."

Urban Lifeline Data Center

Constructs an urban lifeline data center to achieve comprehensive management and analysis of urban lifeline data through standardized integrated management, element-level dynamic updates, and automated parameter modeling.

Ensures data accuracy and timeliness through data collection systems, online extraction of business data, real-time IoT sensing, and online entry of paper-based data into databases.

Leadership Cockpit Integrated Early Warning

Establishes a leadership cockpit integrated early warning system, displaying risk information such as monitoring, early warnings, alarms, and disposal on a single map, providing comprehensive situational awareness of urban lifeline operations.

Enables real-time monitoring and early warning of urban lifeline operations through comprehensive risk assessment, operational situational awareness, and in-depth analysis and determine.

Specialized Mini-Scenario Integrated Monitoring

Bulds specialized mini-scenario integrated monitoring systems, including gas safety, water supply safety, utility tunnel safety, waterlogging safety, and manhole safety, enabling precise positioning and real-time monitoring of underground pipelines.

Facilitates real-time monitoring and early warning of key links in urban lifelines through mid-screen displays and analysis meetings.

Data Standardization System

Establishes a data standardization system addressing three major data standardization issues—resource aggregation and database construction, resource integration and sharing services, and resource operation and maintenance—through data standards, service standards, and operation standards. Ensures efficient resource aggregation, collaborative sharing, and value mining.

Scenario-Based Applications: Precisely Addressing Pain Points

Sefon Software deeply aligns large model applications with urban lifeline construction, covering scenarios such as urban pipeline network management, waterlogging early warning, and intelligent inspections. Precisely locates urban hidden dangers,apperceive hazardous entities, and resolves complexities,Overall improvement urban lifeline governance levels.

Drainage and Waterlogging Early Warning

The model incorporates dynamic data such as real-time rainfall and underground pipeline network models, combined with historical data like and flood-prone area distribution. Monitors urban waterlogging risks in real time, precisely locates flood-prone areas, and enhances urban waterlogging early warning and handling capabilities through intelligent decision support.

Intelligent Pollution Traceability Analysis

The model integrates pollution mass spectrometry data, hydraulics knowledge bases, and multi-source data such as microbial characteristics and pollution concentrations to accurately analyze pollutant types and sources. Updates pollution source information in real time, guides emergency response efforts, and evaluates pollution traceability and disposal effects to continuously optimize the model and traceability strategies.

Intelligent Cleaning Task Planning

The model performs intent recognition and command parsing, extracting keywords such as area size and operation type to provide foundational information for cleaning task planning.

Generates equipment configuration plans, optimizes cleaning paths, and dynamically adjusts cleaning schedules through multi-objective optimization task decomposition and dynamic condition adaptation to meet the cleaning needs of different areas.

Gas Safety Monitoring

The model analyzes data such as gas pipeline pressure and flow rates, combined with historical leakage cases, to accurately predict leakage risks and provide early warnings.

Through the intelligent inspection system of large models, it automatically identifies pipeline abnormalities, pushes alerts in real time, and quickly generates emergency response plans during gas leakage incidents to guide rescue personnel.

Bridge Health Assessment

The large model analyzes monitoring data such as bridge vibrations, displacements, and stresses to assess bridge health and identify defects in a timely manner. Combined with bridge design parameters and historical maintenance records, it predicts the remaining service life of bridges, improving bridge safety management efficiency.

Water Supply Network Management

The large model analyzes water quality data of the supply network in real time, monitoring indicators such as residual chlorine and turbidity to promptly detect abnormalities. Combined with water source quality changes and pipeline water transmission processes, it predicts water quality trends and issues early warnings.

By analyzing pipeline pressure and flow data, it predicts pipe bursts, blockages, and other faults in advance. It also simulates fault scenarios to provide references for pipeline network renovation and optimization.

Sefon Software continuously enhances large model technology to improve the monitoring, early warning, and emergency response capabilities of urban lifelines, providing more reliable guarantees for urban safety operations. In the future, Sefon Software will further expand the application scenarios of urban lifeline construction, promoting the development of urban lifeline management toward intelligence, precision, and efficiency.