In order to improve the effect of distributed 3D interior design, a distributed 3D interior design method based on color image model is proposed in this paper. In this paper, the author uses the distributed feature information fusion method to construct the color image model of the distributed 3D interior design, and carries out edge contour detection and feature extraction for the distributed 3D interior spatial distribution image. The RGB color decomposition method is used to decompose the color pixel features of the three-dimensional indoor spatial distribution image. Combined with the three-dimensional point cloud feature reorganization method, the color space reconstruction of the distributed three-dimensional interior design is realized, and the optimal combination of color features of the distributed three-dimensional interior design is realized. Based on the design of image and color processing algorithm, the development and design of distributed 3D interior design system is carried out based on virtual reality and visual simulation technology. The 3D modeling of distributed 3D interior design is carried out using 3ds max, and the indoor hierarchical structure design is realized using the modeling software Multigen Creator. The experimental results show that, according to the color information fusion results, the distributed 3D interior design optimization is realized. Compared with the traditional manual design method, the root mean square error of this method is greatly reduced from 0.232 to 0.023, and the time cost is 82.8% faster. This method has a better visual effect and strong feature expression ability. Conclusion. The visual effect of distributed 3D interior design with this method is good, the error is small, and the visual expression ability is strong.
In a multiagent system, the semantic interaction between agents is an important aspect affecting multi-intelligence. The purpose of interaction is to reasonably arrange task objectives and behaviors through information sharing and communication interaction, so as to maximize the overall performance of multiagent system. This paper analyzes the communication and interaction process between agents from the perspective of semantic layer and introduces the BDI (belief, desire, intention) model of agent’s thinking state into the communication and interaction process. Furthermore, we propose a multiagent semantic interaction strategy model based on a large-scale intelligent sensor network, which supports various types of negotiation and interaction on the basis of basic interaction behavior to solve the problem of information operational conflicts. In addition, this paper limits the scale of historical information through the definition of equivalence and the merging theorem of history, and it uses reinforcement learning algorithm to detect possible conflicts and delay communication and makes rational use of limited resources to improve system revenue and coordination efficiency. The experimental results show that compared with the previous methods such as debate and negotiation, the strategy model can realize the flexible interaction based on scene and is more practical. At the same time, the existence of reinforcement learning improves the efficiency analysis and the convergence performance of semantic interaction strategy.
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