8results about How to "Effective migration" patented technology

The invention discloses a preparation method of a Zn0. 3Cd0. 7S (at) CFR heterojunction photocatalyst, which comprises the following steps: firstly, preparing a Zn0. 3Cd0. 7S catalyst by a hydrothermal method, and dispersing the Zn0. 3Cd0. 7S catalyst in ultrapure water; then, catechol, ammonia water and formaldehyde are sequentially added into the dispersion liquid, CFR evenly grows on the surface of Zn0. 3Cd0. 7S through a hydrothermal reaction, a coating layer is formed, and a final product is obtained after washing and drying. The preparation method is simple, the process is easy to control, and the prepared photocatalyst shows excellent H2O2 yield and good cycle stability under the condition that a sacrificial agent is not added.

The application discloses a mechanical equipment fault diagnosis method, device and equipment and a readable storage medium. Known fault information multi-source frequency domain signals of same or same type mechanical equipment under a first working condition are acquired, and the known fault information multi-source frequency domain signals under the first working condition are taken as source domain data sets. Unknown fault information multi-source frequency domain signals of the mechanical equipment to be diagnosed under a second working condition are acquired, and the unknown fault information multi-source frequency domain signals under the second working condition are taken as target domain data sets. A multi-source dense adaptive adversarial network model is trained by using the source domain data sets and the target domain data sets, and a mechanical equipment fault diagnosis model is obtained. The real-time acquired multi-source frequency domain signals of the mechanical equipment to be diagnosed under the second working condition are input into the mechanical equipment fault diagnosis model, and a fault diagnosis result is output. The application can better utilize the multi-source information of the mechanical equipment in data level fusion and feature level fusion.

This invention belongs to the field of photocatalysis technology, specifically relating to a microbial-MOF photosensitizing material, its preparation method, and its application. The microbial-MOF photosensitizing material comprises antibiotic-degrading bacteria and a metal-organic framework shell encapsulating the surface of the antibiotic-degrading bacteria. The metal-organic framework in the microbial-MOF photosensitizing material provided by this invention has a porous structure, enabling the encapsulated microorganisms to receive sufficient light, enhancing light absorption capacity, and facilitating rapid carrier migration. Ultimately, this gives the microbial-MOF photosensitizing material superior photoelectrocatalytic activity, lowers the photoelectrocatalytic reaction energy barrier, and increases the kinetics of antibiotic degradation.

This invention relates to the fields of image processing and artificial intelligence, and particularly to a few-shot object detection method based on support feature diffusion generation. The method includes steps such as constructing a few-shot object detection base class meta-learning training set and grouping image data within the training set. This invention introduces a support feature diffusion generation module, applying a diffusion model to the generation and optimization of support features. During the training phase, the inverse process of adding noise to features is learned, enabling the model to recover robust support feature representations from noise. During the testing phase, through progressive reverse diffusion, a more stable and diverse set of support features can be generated from a small number of support samples, effectively mitigating the feature instability and overfitting problems caused by few samples.

The application discloses a kind of based on frozen semantic embedding guide's generative recommendation supervision fine-tuning data construction method.The application avoids the distortion of semantic space and noise interference of collaborative training by freezing the semantic embedding obtained externally as a stable anchor, effectively protecting the content priori knowledge. At the same time, on the basis of maintaining semantic consistency, the collaborative neighborhood and sequence transfer preservation loss are introduced, so that the item representation can still completely retain the behavior patterns and interest evolution law in the original collaborative filtering space during the alignment process to the semantic space, obtaining high-quality fusion representation with semantic enhancement and collaborative authenticity. Based on the fusion representation, it is discretized into multi-level semantic identifiers through residual clustering, and instruction-response samples suitable for large language model supervision fine-tuning are constructed, realizing the effective migration of traditional recommendation knowledge to generative recommendation paradigm, enabling large language model to have sequence recommendation capability, and significantly improving the recommendation effect in cold start scenario.

The application discloses a low-code integrated development system based on multi-path intelligent recommendation, stores code modules through a code module warehouse subsystem, and records quality information and version information of the code modules and user permission information, thereby reducing dependence of the system on business scenarios and improving usability and flexibility of the system. Modification and update of the code modules are realized through a low-code development subsystem; a multi-path intelligent recommendation algorithm of the code module warehouse subsystem is used to search and recommend code modules for the low-code development subsystem, selective elimination of custom code modules can be realized, hierarchical clustering of the code modules is realized, search and recommendation of the code modules are realized according to a quality level coefficient, and higher-quality code modules can be provided for users. Process tracking and quality assurance documents are established through an automatic process management subsystem, thereby improving quality assurance of the custom code modules and reducing process management overhead caused by introduction of the low-code development platform.

This invention discloses a multi-level domain adaptive intelligent inspection method for railway catenary with cloud-edge collaborative real-time detection, belonging to the field of rail transit technology. The method includes generating a dynamic weight adjustment factor based on a cloud dataset; extracting image features from the cloud dataset and generating distilled features based on the dynamic weight adjustment factor; performing multi-scale compression on the image features of the cloud dataset to obtain distilled compressed features; generating environmental features based on the dynamic weight adjustment factor and distilled features; concatenating the environmental features and distilled compressed features to obtain cloud-based large-scale model features; obtaining weather features based on weather data; performing conditional convolution on edge data and concatenating sparse features to generate sparse features; concatenating the sparse features, weather features, and cloud-based large-scale model features to obtain fused features; and performing data transformation to obtain prediction results. This invention solves the problems of low detection accuracy, poor adaptability, and difficulty in achieving efficient cloud-edge collaborative real-time detection in existing technologies.

The application provides a pre-training language model continuous learning method based on general sub-network pruning. The method sets zero to redundant neurons in the network through general sub-network pruning, thereby compressing original knowledge, and then pre-trains and updates the idle neurons set to zero on new professional field data, performs field collaborative continued pre-training, increases the field knowledge contained in the model under the condition that the size of the model remains unchanged, and the different field knowledge can work collaboratively, thereby achieving the effect of knowledge transfer.