To solve the problem of technology docking negotiation of technology innovation platform, the Agent-based multi-issue negotiation algorithm and strategy were analyzed and designed with reference to the technology of IntelliSense Agent. In the practical application environment of technology docking on technology innovation platform, the data of history technology docking proposals were fully used and the technology docking benefits of both the technology trading sides was fully considered, and then the Agent-based multi-issue negotiation algorithm was designed. Based on this algorithm, the technology docking strategy and the proposed solution in technology docking negotiation were designed and proposed. Thus, the optimality of comprehensive benefits was ensured and the "win-win" benefits situation was reached by the two trading sides in the technology docking negotiation. Through practical examples of technology docking on the technology innovation platform, the applicability, rationality, feasibility and effectiveness of this negotiation algorithm and negotiation strategy in its practical application environment were exemplified.

Most existing methods for detecting plane in point cloud cost long operation time, and the result of detection is susceptible to noise. To address these problems, this paper put forward a kind of multi-plane detection algorithm based on geometric statistical characteristics of the point clouds. The proposed method coarsely segmented point clouds according to the change rate of the volume density firstly, then used the Multi-RANSAC to fit planes, at last the authors proposed a new merge-constraint condition to combine and optimize the initial fitted planes. The experimental results show that the method in this paper is easy to realize, can effectively reduce the influence of cumulative noise to the detection results, improve the plane detection accuracy and also greatly reduce the computing time.

The existing vanishing point detection methods mostly remove outliers by statistical analysis of the vanishing point's candidates, and do not make full use of the straight line's parameter information, which leads to low precision and large calculation. In the paper, a robust vanishing points detection method with line parameter information was proposed. Firstly, the algorithm extracted and analyzed the line parameter information at Manhattan direction, and proved them with linear relation. Secondly, the parameters' linear model was established with robust regression algorithm, and then the outliers were removed to get effective lines. Finally, it estimated the optimal vanishing point at Manhattan direction from the obtained effective lines. The experimental results show that the average error of the focal length, which is calibrated by the vanishing points detection algorithm, is 1.05 pixel. Therefore, the detected vanishing points can be effectively applied to the camera calibration.