Shenmou · Visual Intelligence Workshop
Focus on general security scenarios such as smart communities, campus surveillance, and public safety. Multimodal visual analysis enables real-time perception and precise early warning of personnel behavior, vehicle trajectories, and abnormal risks, efficiently building end-to-end monitoring and alerting workflows and strengthening citywide perception and edge response.
Algorithm orchestration and multimodal analysis
No-code construction · Cross-modal retrieval · Cloud-edge collaboration
Intelligent retrieval
Precise cross-modal retrieval from text and images. Multidimensional filtering locates massive video-synthesis data in seconds.
Rapid annotation and training
Retrieval-assisted annotation quickly accumulates positive and negative training samples. Start from zero-shot and define detection tasks in natural language.
Visual operator orchestration
Drag-and-drop packaging of algorithms and business logic into standard operators. A rules engine and task orchestration form a detection-and-alert closed loop.
Multi-level inference
Training materials flow through for delivery, and models can be imported directly into orchestration. Supports multiple engines and edge boxes for multi-level distributed deployment.
Solution overview
Shenmou Visual Intelligence Workshop is a next-generation AI infrastructure product for general security scenarios. It deeply integrates computer vision (CV) models with multimodal foundation model capabilities and uses a flexible, configurable algorithm orchestration engine to jointly analyze and intelligently reason over heterogeneous data from sources including video, images, and text. The platform supports on-demand task orchestration for specific business scenarios, enabling end-to-end detection workflows for complex environments. It also provides cross-modal semantic understanding and efficient retrieval for applications such as text-based search and event tracing. With high scalability, low-code orchestration, and a cloud-edge collaborative architecture, the platform can quickly adapt to diverse security requirements and significantly improve perception accuracy, response efficiency, and overall system intelligence.
Application scenarios
Quickly build deployable intelligent applications for public security, emergency response, transportation, energy, and other security scenarios.
Public security
For entertainment venues and public-area prevention, rapidly deploy behavior analysis for fights, falls, and abnormal behavior, with alerts going directly to the police-incident bus. Law-enforcement case handling can launch tactics with low code, covering diverse behavior analysis to support in-event response and after-the-fact tracing.
Emergency response
Integrate regional monitoring resources and orchestrate analysis templates such as crowd gathering and abnormal trajectories on demand, strengthening intelligent perception and early warning for emergencies. Connect pre-incident warning, in-incident response, and post-incident review to shorten the cycle from discovering major risks to responding.
Transportation
Run multi-source joint analysis on road and station video to detect abnormal behavior and trajectory risks in time and issue graded alerts. Cloud-edge collaboration delivers algorithms in batches, enabling second-level event search and review and improving traffic order and emergency dispatch efficiency.
Energy
Deploy intelligent patrols at plants and work areas to identify non-compliant operations and abnormal states, enabling risk analysis and graded alerts. Combined edge training and inference with rapid model delivery reduces manual screen monitoring and keeps high-risk areas continuously manageable.
Solution advantages
Combine cloud-edge collaboration, closed-loop data, and low-code orchestration to build visual intelligence infrastructure that keeps evolving.
Integrated cloud-edge collaboration
Build upper- and lower-level cascading and multi-engine primary-secondary synchronization systems to centrally manage edge boxes and their connected devices, support batch model delivery and targeted material return flows, and use heartbeat self-healing to keep distributed environments stable and efficient.
Closed-loop training data
Import, clean, and send business materials for training with one click, connect the entire workflow from alert retrieval to AES training, and continuously feed results back into model optimization to improve generalization in real-world scenarios.
Multi-source intelligent analysis
Support batch deployment of individual tasks and coordinated analysis across multiple tasks, deliver precise tiered alerts, locate targets in seconds through natural-language cross-modal retrieval, break down information silos, and enable global logical analysis.
Independent, controllable vision foundation
Built on a proprietary vision foundation model pretrained on massive datasets for general visual understanding, with support for industry customization and incremental learning to deliver a domestic foundation that evolves on demand.
No-code visual orchestration
Combine CV models, multimodal modules, rules engines, and post-processing logic through drag-and-drop workflows to quickly build algorithm templates and end-to-end detection processes without code, greatly improving deployment efficiency and scenario adaptability.
Lightweight edge training and inference
Deploy the training platform on site and use built-in foundation-model-powered automatic annotation to complete few-shot fine-tuning and model delivery with simple operations, making training fast, deployment practical, and management controlled.
Solution architecture
Connect central training, algorithm orchestration, edge inference, and business applications on a visual foundation model.
Solution value
Reduce the cost of intelligent infrastructure and ongoing operations through better compute utilization, deployment efficiency, and response speed.
Intelligent analysis, improved response efficiency
Use alert filtering, classification, and priority-event delivery to reduce manual screen monitoring and ineffective investigation. One public-space project launched 324 analysis tasks and identified and delivered 26 abnormal events within three months, improving response efficiency by more than 40%.
Cloud-edge collaboration, improved deployment efficiency
Support batch delivery of central models and incremental synchronization with edge nodes, shortening deployment from tens of minutes to just a few minutes while reducing multi-node operations and maintenance costs.
Closed-loop data, improved adaptation efficiency
Filter and clean alert and retrieval samples before returning them to the training platform, reducing manual screening costs by 70% and improving model adaptation efficiency by 50%.
Rapid launch, shorter deployment cycles
Use zero-shot startup and low-code orchestration to launch 10 scenario rules in half a day, shortening the path from requirement configuration to active alerts to the minute level.