人工智能+工具方案目錄

WISIO 是一款智能多媒體（相片和影像）AI 分析和模型管理系統，提供端到端解決方案的無程式碼平台，用於建立和部署行業特定的視覺 AI 模型。它能無縫整合資料、訓練模型並進行智能識別，賦能資料驅動的決策，同時大幅降低技術門檻和成本。

無需 AI 專業知識，讓使用者也能有效率地管理整個模型的生命週期，從而獲得新的營運洞察，並提升各種應用的生產力。

WISIO is an intelligent multi-media (image and video)， AI analysis and model management system that provides an end-to-end, no-code platform for building and deploying industry-specific vision AI models. It seamlessly integrates data, trains models, and performs intelligent identification to empower data-driven decision making, all while drastically reducing technical barriers and cost.

This allows users without AI expertise to efficiently manage the entire model lifecycle, unlocking new operational insights and driving productivity across a wide range of applications.

全面的智能視覺AI管理系統透過自動化勞動密集和關鍵監控任務，大幅提升公共服務水準並降低營運成本。例如，它可以透過城市車輛行車記錄器自動識別基礎設施缺陷，例如坑洞、人行道開裂或路牌損壞，從而在問題惡化和成本上升之前主動進行修復，從而提升公共安全並延長資產使用壽命。在公共衛生和安全領域，它可以分析閉路電視錄像，檢測公共區域過度擁擠、無人看管的行李或跌倒等異常情況，從而加快緊急應變速度。

此外，它還透過快速分析圖片和影像尋找違規行為，簡化了從公立學校食品安全到環境監測等合規性檢查流程，使有限的檢查人員能夠專注於最高優先順序的案件。這種自動化技術減少了對人工重複監控的需求，並實現了從昂貴的被動維護轉向高效、數據驅動的主動管理，從而達致成本節約，同時提供更安全、更可靠、更高質素的公共服務。

A comprehensive intelligent vision AI management system significantly improves public service and reduces operational costs by automating labor-intensive and critical monitoring tasks. For instance, it can automatically identify infrastructure defects like potholes, cracked sidewalks, or damaged street signs from city vehicle dashcams, enabling proactive repairs before problems worsen and become more expensive to fix, thereby enhancing public safety and extending asset lifespans. In public health and safety, it can analyze CCTV footage to detect anomalies like overcrowding, unattended bags, or falls in public areas, triggering faster emergency response times.

Furthermore, it streamlines regulatory compliance inspections—from food safety in public schools to environmental monitoring—by quickly analyzing images and videos for violations, allowing a limited inspector workforce to focus only on the highest-priority cases. This automation directly translates into massive cost savings by reducing the need for manual, repetitive monitoring and enabling a shift from costly reactive maintenance to efficient, data-driven proactive management, all while delivering a safer, more reliable, and higher-quality public service.