10results about How to "Reduce training" patented technology

The application provides a location-based search processing method, device and equipment and a storage medium. The method comprises the following steps: in response to a location search request of a user, inputting search information into a preset multi-task search model; simultaneously processing the search information based on a city jump decision task and a search suggestion task in the multi-task search model to obtain target city location features corresponding to the city jump decision task and search suggestion features corresponding to the search suggestion task; obtaining a search processing result based on the target city location features and the search suggestion features; obtaining a search suggestion list with the highest matching degree with the search processing result in a preset search suggestion library, and displaying the search suggestion list on a search interface. Through the above method, the training and development and maintenance costs of multiple models can be effectively reduced.

The application discloses a Transformer oil well production prediction method and device based on gating constraints and Kalman filtering, and a medium, which comprises the following steps: first, obtaining oil well historical production data and preprocessing; then, using a mutual information method to screen input features related to target production, and constructing conditional gating constraint features representing the opening and closing states of the oil well; then, inputting the processed time sequence samples into a Transformer prediction model to obtain initial prediction results of the oil well production; then, performing state constraint on the initial prediction results according to the conditional gating constraint features, and performing causal recursive residual correction on the prediction results of subsequent time based on the current time prediction residual by using Kalman filtering to obtain final prediction results; further, performing grid search on Kalman filtering parameters Q and R for optimization, and determining the optimal parameter range by combining a heat map. The application can effectively reduce the prediction error, and improve the accuracy, stability and robustness of the oil well production prediction.

The application discloses a high-efficiency beam training method, device, medium and product under near field communication, relates to the technical field of communication, and comprises the following steps: selecting code words from the first layer of a stage one codebook according to the index of optimal code words; finding out the optimal code words from the selected code words and updating the index; feeding back the updated index to a base station; determining whether the first cycle parameter is greater than the first maximum cycle parameter, executing the above steps when the first cycle parameter is greater than the first maximum cycle parameter, and setting the second cycle parameter otherwise; selecting code words from the first layer of a stage two codebook according to the index of the current optimal code words; finding out the optimal code words from the selected code words and updating the index; feeding back the updated index to the base station; determining whether the second cycle parameter is greater than the second maximum cycle parameter, outputting the code word at the optimal code word index after updating when the second cycle parameter is greater than the second maximum cycle parameter, and returning to execute the above steps otherwise. The application can effectively improve the reachable rate performance in near field communication and significantly reduce the training and feedback overhead.

The invention belongs to the technical field of garbage cleaning, and particularly relates to a sweeper garbage collecting and leading-in mechanism which comprises a mounting frame, a sweeping cavity, a garbage collecting and leading-in device and a garbage collecting and leading-in device, the mounting frame is internally provided with a conveying cavity, the right side wall of the conveying cavity is provided with a discharging port, and the conveying cavity is internally provided with a conveying belt; a collecting and guiding-in assembly is arranged in the sweeping cavity and used for collecting and guiding in garbage. Garbage of different sizes can be guided into the sweeping mechanism without difference, the size and the type of the garbage do not need to be perceived and recognized through an AI technology, and efficient sweeping is achieved; in addition, the research, development and manufacturing cost of equipment is saved, and the cost of large model training, vehicle-mounted computing power and sensing parts is saved.

This application discloses a cross-domain multimodal recommendation method and system based on multi-agent attribute-driven architecture. Embodiments of this application can be applied to product recommendation scenarios in an e-commerce platform. The method includes: receiving multimodal features; processing the multimodal features using a first operation to generate reconstructed original multimodal features; the reconstructed original multimodal features include attribute-related representations and attribute-independent representations; then, processing the reconstructed original multimodal features using a second operation to generate a modal cue vector; then, processing the modal cue vector and the attribute-related representation to generate a new feature vector; and finally, processing the new feature vector using a sequence encoder to generate attribute-level multimodal sequence states. By sharing basic embeddings, the parameter overhead is significantly reduced, solving the problem that the parameter size of traditional cross-domain multimodal encoders increases linearly with the number of domains.

The application discloses a kind of based on knowledge graph's cancer cell line drug response prediction large language model construction method.The method fuses the structured information of biological knowledge graph and the semantic reasoning ability of large language model, realizes the multi-level modeling and explainable prediction of drug-cell reaction mechanism.The method comprises the following steps: first, integrate multi-source data to construct multi-modal dataset;Second, establish the drug response knowledge graph containing drug, gene and other entities;Then, fine-tune the model using LoRA technology and inject graph embedding, realize cross-modal alignment;Further, dynamic knowledge retrieval and enhancement are carried out using RAG technology;Finally, output drug sensitivity prediction and natural language explanation.The application significantly improves the prediction accuracy and explainability, and can reveal the key gene pathway, providing an efficient intelligent tool for precision cancer treatment.

The invention discloses a dynamic routing method and device based on a self-purification expert pool, and the method comprises the steps: firstly, constructing an expert feedback pool supporting dynamic extension, driving a group of heterogeneous domain expert models to carry out the reasoning of a cross-domain data set problem, and storing a result; secondly, implementing a double-layer self-purification mechanism, performing data cleaning and expert model cleaning on a feedback pool based on real answers, and automatically eliminating low-quality data and low-performance experts; further, on the basis of the purified high-quality data, a lightweight BERT model is trained to serve as a routing learning device, so that the routing learning device learns an expert which predicts the most adaptive expert according to the semantic features of the problem; and finally, in practical application, the route learner is used for performing rapid reasoning and routing on a new input question, and the new input question is distributed to the predicted optimal expert model to obtain an answer. Through dynamic self-purification of data and resources and data-driven routing decision, intelligent management and efficient utilization of heterogeneous expert model resources are realized, routing accuracy and interpretability are ensured, and meanwhile, the capability of a system for processing complex cross-domain problems in a scene with limited computing power is remarkably improved.

The application relates to the technical field of artificial intelligence, in particular to an AI model full life cycle collaborative management method and system. The method is characterized in that: a plurality of source heterogeneous data are acquired, dynamic data management is performed on the plurality of source heterogeneous data, a standardized data set is formed, a metadata graph is constructed, and unified correlation management of data, models and training processes is realized. AI model construction and configuration are completed according to a preset development mode, and model training is performed based on the configuration to schedule computing resources, so that a target AI model is generated. After the model completes training, deployment and inference service publishing are performed, and the model running performance is continuously monitored, and data management updating or model retraining is automatically triggered when the performance changes, so that closed-loop collaborative management of the AI model full life cycle is realized. The application effectively guarantees the stability, accuracy and business adaptation capability of the long-term operation of the AI model, and improves the automation level and overall efficiency of the AI model full life cycle management.

The application provides a prompt learning method based on unsupervised knowledge distillation, comprising: a supervised fine-tuning stage, taking a first visual language model as a teacher model, freezing first pre-training parameters of the teacher model, and performing supervised fine-tuning on the teacher model through labeled samples to optimize first learnable prompt parameters of the teacher model; an unsupervised distillation stage, taking a second visual language model as a student model, freezing second pre-training parameters of the student model, aligning inference results of the student model and the teacher model on unlabeled samples, and migrating discriminative knowledge of the teacher model to second learnable prompt parameters of the student model. The application also provides a prompt learning device based on unsupervised knowledge distillation, a storage medium and an electronic device. Therefore, the application significantly improves the adaptation effect of the visual language model on downstream tasks, improves the generalization performance of the visual language model, and has low training and inference costs.

The application provides a railway sectional insulation device arc extinction angle defect identification method, belonging to the technical field of intelligent defect identification. The method obtains a sectional insulation device image to be detected; inputs the sectional insulation device image to be detected into a pre-trained arc extinction angle defect identification model, uses a backbone network of the model to extract a local feature map and a global feature map of the sectional insulation device in parallel; uses a bridging fusion module in the backbone network to fuse the global feature into the local feature and output a first feature map, and simultaneously fuse the local feature into the global feature and output a second feature map; inputs the first feature map and the second feature map into a classifier of the arc extinction angle defect identification model for defect identification classification, and outputs an arc extinction angle defect identification result. The application improves the identification effect of the model on the arc extinction angle defect, enhances the local and global perception ability of the model, simultaneously realizes parallel calculation of local feature extraction and global feature extraction, and thus reduces the delay of training and inference.