6results about How to "Avoid homogenization" patented technology

This invention relates to the field of image processing technology, specifically disclosing a zero-shot text attribute graph learning method based on prototype mutual prompting enhancement. The method calculates instance-aware contrast loss and constrains the similarity of the same node's embedding in two views. It performs PCA dimensionality reduction on the token embedding of the LLM, extracts the first P principal components to form an aligned coordinate system C, maps the components to this coordinate system, and calculates the informative-aware contrast loss. It freezes the pre-trained GNN and the LLM backbone network, extracts node structural embeddings Z from the pre-trained GNN, generates K prototype embeddings through K linear projectors, and injects them as soft prompts into the LLM instructions. The LLM outputs K expert prompts. It calculates the weights of each expert prompt through a router model. It freezes the LLM training of related projectors and routers, freezes the GNN training of the LLM prompt adaptation layer, and iterates and optimizes 1-3 times. This method can cover complex cross-modal information in text and images while reducing the accumulation of zero-shot transfer bias.

The application provides a special management method and system for an education research platform, which comprises the following steps: checking the personnel identity of each logged-in person respectively, issuing corresponding research tasks and research permissions, obtaining the permission exercise data generated when each logged-in person executes the research task, constructing the research archives of each logged-in person in combination with the corresponding research permissions, constructing the research access path of the corresponding logged-in person in the research platform according to the permission exercise data, obtaining the corresponding research results of this time and transmitting them to the corresponding login page for display, analyzing the research reports of all the logged-in persons under different disciplines, and transmitting them to the platform homepage for display. The learning plan for each student in the education research platform is formulated, and the learning effect is analyzed, so that the learning effect of the student in the education research platform is effectively improved.

This invention discloses a data analysis and prediction system based on deep learning, belonging to the fields of data analysis and artificial intelligence technology. The system includes a data acquisition module, a data preprocessing module, a feature extraction module, a deep learning prediction module, a result output module, a model optimization module, a data storage module, an anomaly monitoring module, a human-computer interaction module, and a permission management module. Through a unique multi-source data fusion preprocessing algorithm, an improved deep learning prediction model, and a dynamic optimization mechanism, it achieves accurate analysis and efficient prediction of multi-domain and multi-type data. Its prediction accuracy is improved by more than 15% compared to existing technologies, and the response latency is controlled within 500ms. It also possesses strong environmental adaptability and data security, and can be widely applied to data analysis and prediction scenarios in finance, industry, healthcare, and other fields.

This invention discloses a vehicle management method and platform system based on user profiles. It acquires real-time vehicle driving behavior and vehicle body change information; aggregates a large amount of vehicle risk control information to establish a vehicle risk control database and a statistical model; sets user profile preferences based on the statistical model and vehicle risk control information; selects specific vehicle risk control information from the vehicle risk control database based on user profile preferences; and provides specific vehicle risk control information. By comprehensively considering both user driving behavior and vehicle body changes, it helps screen users, thereby accurately matching vehicle management product positioning, enabling competitor comparison, revenue risk control, user recommendation, and precise matching. This truly reflects the matching of car insurance with accident rates, accurately reflects customer segmentation, helps avoid homogenization of car insurance products, helps optimize the relative ratio of operating costs and charges, and can also help users improve their driving habits to a certain extent.

The application provides a bedside respiratory evaluation and individualized treatment system and method based on AI intelligent interpretation, which comprises: a respiratory evaluation data acquisition unit, a scale module integrated with a built-in multi-dimensional evaluation system, and a device interface module compatible with multiple brands of trainers; a bedside workstation core interaction unit for multi-source data integration; an AI intelligent interpretation engine unit carrying a lightweight deep learning model, which extracts features through a CNN-RNN fusion model and an attention mechanism, and generates diagnostic opinions in combination with a dynamic threshold library and a diagnostic reasoning module; an individualized treatment plan generation unit which generates a treatment plan containing training parameters and evaluation nodes through a multi-objective optimization algorithm; and an efficacy tracking feedback unit which acquires reevaluation data in real time and dynamically adjusts the scheme according to the efficacy analysis results, forming a closed-loop diagnosis and treatment mode. The application realizes the bedside integration of multi-source data fusion, AI intelligent interpretation and individualized treatment, and improves the diagnosis and treatment efficiency, consistency and accuracy.

The embodiment of the invention discloses a training method and device of a content understanding model, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining a content sample and a query sample; inputting a plurality of sequence fragments of the query sample and the content sample into a to-be-trained model, predicting a plurality of groups of output samples in combination with global memory, each group of output samples including candidate memory and candidate answers; calculating composite rewards and advantage scores corresponding to the multiple groups of output samples; performing temperature sampling on the multiple groups of output samples to obtain a target output sample, and updating the global memory into candidate memory included in the target output sample; calculating a gradient corresponding to the sequence fragment; and accumulating the gradient corresponding to each sequence fragment, and updating the model parameters of the to-be-trained model to obtain a trained content understanding model. According to the scheme, the content understanding model with excellent performance can be efficiently trained at low cost.