A light-adaptive image fusion algorithm based on gradient enhancement and text guidance was developed to address the limitations of existing fusion algorithms that cause loss of detailed information， edge degradation， and unclear salient feature under complex lighting environments. Firstly， a feature extraction module based on gradient enhancement and linear spatial equations was constructed to extract global feature with linear computational complexity along with enhancing edge gradient information. Secondly， scene description text was embedded to guide the fusion network to generate fused images of different styles in different lighting environments， so that the robustness of the fusion algorithm in complex lighting environments was improved. Finally， a Gradient Enhanced Fusion Module （GEFM） based on cross-attention mechanism was designed to achieve gradient enhancement and fusion of multimodal information. Experimental results on three benchmark datasets including TNO， MSRS （MultiSpectral Road Scenarios）， LLVIP （Low-Light Visible-Infrared Paired） demonstrate that the proposed algorithm outperforms comparative algorithms such as LRRNet（Low-Rank Representation Network）， CAMF（Class Activation Mapping Fusion）， DATFuse（Dual Attention Transformer Fusion）， UMF-CMGR（Unsupervised Misaligned Fusion via Cross-Modality image Generation and Registration） and GANMcC（GAN with Multi-classification Constraints） in five quantitative metrics. Specifically， the Spatial Frequency （SF） and Visual Information Fidelity （VIF） metrics were improved by 22%， 59%， 61% and 31%， 53%， 37%， respectively. The algorithm effectively reduces edge blurring and ensures that fused images maintain high clarity and contrast under different lighting environments.

Aiming at the problem of poor denoising effect and long training period in image denoising， an image denoising model based on approximate U-shaped network structure was proposed. Firstly， the original linear network structure was modified to an approximate U-shaped network structure by using convolutional layers with different strides. Then， the image information of different receptive fields was superimposed on each other to preserve the original information of the image as much as possible. Finally， the deconvolutional network layer was introduced for image restoration and further noise removal. Experimental results show that on Set12 and BSD68 test sets： compared with Denoising Convolutional Neural Network （DnCNN） model， the proposed model has an average increase of 0.04 to 0.14 dB on Peak Signal-to-Noise Ratio （PSNR）， and an average reduction of 41% on training time， verifying that the proposed model has better denoising effect and shorter training time.

For the order allocation problem of industry cluster with multiple manufacturers and multiple suppliers, considering the suppliers' current available manufacturing resources, an order allocation model for supply chain with multi-suppliers to multi-manufacturers based on production load equilibrium strategy was proposed in this paper. The simulation case study of an industry cluster of injection molding parts manufacturers was used to verify that the model can not only narrow the supplier's production load fluctuation, but also can balance suppliers' production load in each cycle. The production load equilibrium strategy can not only guarantee the order's lead time, but also the equilibrium utilization of the suppliers' manufacturing resources, leads to promote the whole supplier group's operation level and efficiency and to realize the optimization of the entire supply chain.

To improve the data storage performance and utility efficiency of 3D model on military simulation entities, categories of the models were proposed. Rules of 3D models on level of details, grade of damage, texture and grouping were constituted. Data storage framework for 3D model, hierarchical architecture of the models, and data structures for each level architecture were constructed. Finally, the algorithm for reconstructing 3D models was introduced.