Method and device for determining cargo scheduling strategy, and nonvolatile storage medium
By using deep learning models and the DeepFM algorithm to accurately match vehicle and cargo characteristics and generate optimal scheduling strategies, the problem of inaccurate matching of vehicle and cargo characteristics in existing technologies is solved, thereby improving the generation rate of scheduling orders and transportation efficiency.
CN115879845BActive Publication Date: 2026-06-19CHINA TELECOM CORP LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA TELECOM CORP LTD
- Filing Date
- 2022-12-21
- Publication Date
- 2026-06-19
AI Technical Summary
Technical Problem
Existing technology cannot accurately match vehicle characteristics with cargo characteristics, resulting in the generated dispatch order not being the optimal dispatch solution.
Method used
A pre-trained deep learning model is used to match the feature factor sets of vehicles and goods. The DeepFM algorithm model is used to generate the optimal matching result. The best matching result is calculated by combining the activation function, and the scheduling strategy of goods is determined.
Benefits of technology
It achieved precise matching of vehicle and cargo characteristics, determined the optimal cargo scheduling strategy, and improved the generation rate of scheduling orders, vehicle utilization rate, and material transportation demand conversion rate.
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Smart Images
Figure CN115879845B_ABST
Abstract
The application discloses a kind of freight scheduling strategy determination method and device, nonvolatile storage medium.Therein, the method includes: the first characteristic factor set is matched with the second characteristic factor set, and the matching result set is obtained, wherein the first characteristic factor set is the characteristic factor set corresponding to vehicle information in vehicle set, and the second characteristic factor set is the characteristic factor set corresponding to freight information, and the vehicle is used to load and transport freight;Using the deep learning model trained in advance to determine the target matching result from the matching result set;According to target matching result, determine the scheduling strategy of freight.The application solves the technical problem that the optimal scheduling strategy for freight cannot be determined due to the inability to accurately match the characteristics of vehicles with the characteristics of freight.
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