Urban governance multi-modal large model construction method based on target area
By building a multimodal model of urban governance, using visual and text feature extractors and language models to generate detailed scene descriptions, the problem that existing models cannot accurately identify illegal scenarios is solved, and more accurate urban management is achieved.
CN120472292APending Publication Date: 2025-08-12CHENGDU ZHIHUI HENENG CITY TECHNOLOGY CO LTD
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Patent Information
- Application Number
- CN202510365952.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-26
- Publication Date
- 2025-08-12
AI Technical Summary
Technical Problem
The existing urban management model cannot accurately distinguish violations in urban management, such as outdoor hanging clothes and outside store business problems, and cannot give accurate descriptions and positioning.
Method used
Build a multimodal model of urban governance based on the target area. By obtaining the training set, establish and train a multimodal model of urban governance. Use visual and text feature extractors, language models and multimodal model to generate detailed scene descriptions and qualitative judgments and select the optimal model.
Benefits of technology
It realizes more accurately identifying violation scenarios in urban management, provides detailed scenario descriptions and qualitative judgments, and improves the accuracy of urban governance.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses an urban governance multi-modal large model construction method based on a target area, and relates to the technical field of smart cities. The method comprises the following steps: S1, acquiring a training set; s2, establishing an urban governance multi-modal large model; s3, training the urban governance multi-modal large model in the step S2 through the training set obtained in the step S1; a trained urban governance multi-modal large model is obtained; s4, repeatedly executing the step S3 to obtain a first number of trained urban governance multi-modal large models; and S5, evaluating the first number of trained urban governance multi-modal large models, and selecting the optimal urban governance multi-modal large model as the urban governance multi-modal large model constructed by the method. According to the urban governance multi-modal large model constructed by the urban governance multi-modal large model construction method based on the target area, violation scenes in urban management can be identified more accurately.
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