CN-122024475-A - Intelligent traffic platform based on multi-mode AI intelligent agent

CN122024475ACN 122024475 ACN122024475 ACN 122024475ACN-122024475-A

Abstract

The invention discloses an intelligent traffic platform based on a multi-mode AI intelligent agent, and relates to the technical field of intelligent traffic. The multi-mode interaction system comprises a multi-mode interaction layer, an AI model layer, an agent cooperation layer and an agent cooperation layer, wherein the multi-mode interaction layer is used for accessing text, image, voice, sensor and document data to generate standardized data, the AI model layer is used for carrying out semantic understanding traffic condition analysis through a dual-mode architecture and combining RAG and knowledge graph correction results to obtain data conversion results, the agent cooperation layer is used for carrying out task disassembly and cross-department cooperation on the data conversion results output by the AI model layer based on an MCP protocol and an n8n workflow engine, and the agent cooperation layer is connected with the multi-mode interaction layer to display the multi-mode results to a user. According to the invention, through the overall architecture of the multi-mode interaction layer, the AI model layer and the agent cooperation layer, multi-source data are integrated, and the traffic state accurate perception, the event rapid handling, the resource intelligent scheduling and the trip efficient service are realized by means of the dual-mode reasoning and the multi-agent cooperation.

Inventors

GUO XIAOWEI
ZHAO CHANGYING
WANG MENG
HANG YANG
LUO YULIN

Assignees

中咨泰克交通工程集团有限公司

Dates

Publication Date: 20260512
Application Date: 20260129

Claims (8)

1. Intelligent transportation platform based on multimode AI agent, its characterized in that includes: the multi-mode interaction layer is used for accessing text, image, voice, sensor and document data, and uniformly analyzing the data through the standardized interface to generate standardized data; The AI model layer is used for carrying out semantic understanding traffic condition analysis through a double-model architecture based on standardized data, and combining RAG and knowledge graph correction results to obtain data conversion results; The intelligent agent cooperation layer is used for constructing a perception intelligent agent, a decision intelligent agent, an operation intelligent agent and a maintenance intelligent agent based on an MCP protocol and an n8n workflow engine and is used for carrying out task disassembly and cross-department cooperation on the data conversion result output by the AI model layer; the intelligent agent cooperative layer is connected with the multi-mode interaction layer, and multi-mode results are displayed to a user.
2. The intelligent traffic platform based on the multi-modal AI agent of claim 1, wherein the traffic condition analysis for semantic understanding is performed through a dual-model architecture, comprising a dual-model architecture employing a generic large model for semantic understanding, logical reasoning, and multi-modal data fusion, and a traffic vertical model for predictive analysis of traffic flow and equipment life.
3. The intelligent traffic platform based on the multimode AI intelligent agent according to claim 2, wherein a mixed network structure based on LSTM and a transducer is adopted, and a space-time attention mechanism is fused to conduct predictive analysis on traffic flow; and adopting a mixed network structure of LSTM-SAE to predict and analyze the service life of the equipment.
4. The intelligent transportation platform based on multi-modal AI agents of claim 3, wherein the LSTM and fransformer based hybrid network architecture and incorporating a spatio-temporal attention mechanism comprises: The LSTM time sequence feature extraction module adopts a 3-layer bidirectional LSTM network, wherein the number of hidden units in each layer is 256, 128 and 64 respectively, and is used for capturing the time sequence dependence of traffic flow, learning future trend through forward LSTM and outputting time sequence feature vectors through reverse LSTM mining history association; the transducer space association modeling module adopts a 2-layer encoder, and each layer comprises 8 self-attention mechanisms and is used for modeling traffic flow space association among different road segments and outputting space feature vectors; And the space-time attention fusion module adopts weighted attention fusion and learns weight coefficients of time sequence features and space features through a full-connection layer.
5. The intelligent transportation platform based on multi-modal AI agents of claim 3, wherein the hybrid network architecture of LSTM-SAE comprises: The SAE feature extraction and anomaly detection module is used for processing the input features through the encoder and the decoder to obtain the health state features of the equipment; And the LSTM life trend prediction module is used for learning the equipment performance attenuation trend according to the extracted equipment health state characteristics and outputting a life prediction value and a fault risk score.
6. The intelligent traffic platform based on the multi-modal AI agent of claim 1, wherein the perception agent integrates standardized data of the multi-modal interaction layer, and traffic flow changes, equipment operation anomalies, and road event characteristics are monitored in real time by a space-time fusion algorithm; The decision-making agent receives traffic flow prediction and equipment life prediction results of the AI model layer and combines a knowledge base to generate a customized decision-making scheme; the operation intelligent agent automatically composes an operation business flow based on an n8n workflow engine; and the maintenance agent automatically performs maintenance task planning, execution and effect evaluation based on the equipment life prediction result and the road monitoring data.
7. The intelligent traffic platform based on the multi-mode AI intelligent agent according to claim 1, wherein the core entity, the relationship among the entities and the entity attribute are extracted from the structured data and the semantic segment text by entity extraction, relationship identification and attribute labeling technology, an industry knowledge graph is constructed, and the structured knowledge verification is provided for the AI model by the industry knowledge graph.
8. The intelligent traffic platform based on the multi-modal AI agent of claim 1, wherein the RAG retrieves relevant information from an external knowledge base before the large language model generates the answer, and combines the retrieved result with the original question as a contextual input to generate the answer with a factual basis.

Description

Intelligent traffic platform based on multi-mode AI intelligent agent Technical Field The invention relates to the technical field of intelligent traffic, in particular to an intelligent traffic platform based on a multi-mode AI intelligent agent. Background The intelligent traffic is an important support for urban traffic management, is widely applied to a plurality of key scenes such as intersection flow regulation, traffic event handling, travel service guiding and the like, and plays a certain role in relieving traffic jams and improving management efficiency. Whether the real-time traffic monitoring of the urban arterial road or the emergency response of the expressway is realized, the platform provides a visual basic support for the traffic management department through data acquisition and preliminary integration, so that the traffic management takes an important step from traditional manpower patrol to data supervision. However, with the increase of urban traffic flow and the upgrade of travel demands, the short board of the existing intelligent traffic platform is prominent, and the demands of fine management and high-quality service are difficult to meet. Firstly, multi-mode data (video, voice, sensor and document) fragmentation lacks a unified cooperative processing mechanism, such as video event detection and meteorological sensor data fragmentation, which leads to decision delay, secondly, a perception-decision-treatment process depends on manual intervention, such as scheduling resources after manual analysis of traffic events, thirdly, a user interaction threshold is high, keyword retrieval is relied on, non-professional operation efficiency is low, and thirdly, business insight, analysis, processing wisdom and automation degree are insufficient. Therefore, in view of the drawbacks of the prior art, how to provide a multi-modal AI agent-based intelligent transportation platform is a problem that needs to be solved by those skilled in the art. Disclosure of Invention In view of the above, the invention provides an intelligent traffic platform based on a multi-mode AI intelligent agent, which integrates multi-source data such as texts, images, voices and sensors through the overall architecture of a multi-mode interaction layer, an AI model layer and an intelligent agent cooperation layer, and realizes accurate sensing of traffic states, rapid event handling, intelligent resource scheduling and traveling efficient service by means of dual-mode reasoning and multi-intelligent agent cooperation. In order to achieve the purpose, the intelligent traffic platform based on the multi-mode AI intelligent agent adopts the following technical scheme that the intelligent traffic platform comprises: the multi-mode interaction layer is used for accessing text, image, voice, sensor and document data, and uniformly analyzing the data through the standardized interface to generate standardized data; The AI model layer is used for carrying out semantic understanding traffic condition analysis through a double-model architecture based on standardized data, and combining RAG and knowledge graph correction results to obtain data conversion results; The intelligent agent cooperation layer is used for constructing a perception intelligent agent, a decision intelligent agent, an operation intelligent agent and a maintenance intelligent agent based on an MCP protocol and an n8n workflow engine and is used for carrying out task disassembly and cross-department cooperation on the data conversion result output by the AI model layer; the intelligent agent cooperative layer is connected with the multi-mode interaction layer, and multi-mode results are displayed to a user. Preferably, the traffic condition analysis of semantic understanding is performed through a dual-model architecture, comprising a dual-model architecture adopting a general large model for semantic understanding, logical reasoning and multi-mode data fusion and a traffic vertical model for predictive analysis of traffic flow and equipment life. Preferably, a mixed network structure based on LSTM and a transducer is adopted, and a space-time attention mechanism is fused to conduct predictive analysis on traffic flow; and adopting a mixed network structure of LSTM-SAE to predict and analyze the service life of the equipment. Preferably, the hybrid network structure based on LSTM and transducer, and the fusion of the spatio-temporal attention mechanism, comprises: The LSTM time sequence feature extraction module adopts a 3-layer bidirectional LSTM network, wherein the number of hidden units in each layer is 256, 128 and 64 respectively, and is used for capturing the time sequence dependence of traffic flow, learning future trend through forward LSTM and outputting time sequence feature vectors through reverse LSTM mining history association; the transducer space association modeling module adopts a 2-layer encoder, and each layer comprises 8 self-attention m