Anti-cross-labeling intelligent atlas identification method and device, electronic equipment and storage medium
Through the intelligent graph recognition method, machine learning algorithms are used to build a synchronous behavior recognition model, solving the problems of inefficient and error-prone traditional manual review, and achieving efficient and accurate automatic detection of synchronous behavior.
CN119646448APending Publication Date: 2025-03-18SHANGHAI ZHONGSHI CONSTRUCTION CONSULTING CO LTD
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Patent Information
- Application Number
- CN202411815028.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2024-09-03
- Filing Date
- 2024-12-11
- Publication Date
- 2025-03-18
AI Technical Summary
Technical Problem
In the field of bidding, collusion seriously affects fair competition in the market. The traditional collusion identification method relies on manual review, is inefficient, prone to errors, and is difficult to deal with complex means.
Method used
Using intelligent graph recognition method, through data collection, preprocessing, graph construction, feature extraction and model training, machine learning algorithms are used to build a serial behavior recognition model to achieve automatic detection.
Benefits of technology
It realizes high degree of automation of cross-standard behavior detection, improves work efficiency, reduces the requirements for the professional ability of auditors, and has high accuracy, reducing the false alarm rate.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to an anti-cross-labeling intelligent atlas identification method and device, electronic equipment and a storage medium. The anti-cross-bidding intelligent atlas identification method comprises the following steps: collecting related data in a bidding and tendering process; the data is cleaned, integrated and formatted, so that subsequent graph construction and analysis are facilitated; a bidding data atlas is constructed based on the collected data, nodes represent data entities including bidders and bidding files, and sides represent the relationships including bidding relationships, association relationships and bidding file repetition similarity degrees among the entities; extracting features related to the trademark behavior from the atlas; training the extracted features by using a machine learning algorithm, and constructing a string mark behavior recognition model; evaluating the trained model, and performing parameter adjustment and optimization according to an evaluation result; and the trained model is used to carry out automatic detection of the string marking behavior. According to the anti-cross-labeling intelligent atlas identification method, cross-labeling behaviors can be automatically, efficiently and accurately identified.
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