7results about How to "Improve data utilization efficiency" patented technology

This invention discloses a method for diagnosing self-discharge anomalies in energy storage battery packs based on short-term charging data, belonging to the field of battery safety monitoring technology. Addressing the shortcomings of existing methods that rely on complete charge-discharge cycles and are difficult to adapt to multi-stage varying operating conditions, this invention extracts features from multi-stage charging data, combines an autoencoder to achieve adaptive extraction of features under multiple operating conditions, and constructs a data-driven model fused with GCN-BiLSTM (Graph Convolutional Network-Bidirectional Long Short-Term Memory) to accurately estimate the RCC (Remaining Charging Capacity) of individual battery cells. Finally, based on the size, distribution, and changes of the RCC of each individual cell in the battery pack, we can diagnose overall battery pack inconsistencies, individual cell SOC inconsistencies, and self-discharge faults. This provides a guarantee for the safe operation of energy storage systems.

The application discloses an aircraft cluster cooperative non-cooperative target capturing method driven by entropy regularization, applies the entropy regularization thought to reinforcement learning, optimizes the updating method of the parameters of an evaluation network and a policy network, so that a UAV can obtain a more optimal maneuvering strategy, and the UAV performs actions according to the maneuvering strategy, and realizes the interception of an attacking aircraft. The aircraft cluster cooperative non-cooperative target capturing method driven by entropy regularization can not rely on a deterministic strategy in the interception process of multiple UAVs, but adopts a random strategy, avoids the training from falling into a local optimal point, and the actions of the aircraft are randomized as much as possible while the aircraft completes the task, so that the exploratory and robustness can be practically improved.

The invention provides an unmanned aerial vehicle controller model establishment method and device based on an equivariant network and geometric symmetry. Determining an observation vector of the unmanned aerial vehicle based on the real-time state of the unmanned aerial vehicle and the current stunt instruction; inputting the observation vector into a characterization linear modulation layer, generating a modulation parameter corresponding to the real-time state according to the current stunt instruction, and modulating the real-time state; inputting the modulated observation vector into an irreducible representation conversion layer, and generating irreducible representation of an SO (2) group corresponding to the real-time state; and inputting the irreducible representation into an equivariant multi-layer perceptron, predicting and generating a control instruction corresponding to the current stunt instruction, and obtaining the output of the unmanned aerial vehicle controller model. Based on reinforcement learning of one network, an unmanned aerial vehicle controller model with high decision-making efficiency and strong generalization ability is obtained, and the unmanned aerial vehicle control cost is greatly reduced.

The application discloses a chest radiograph report generation and lesion positioning method and system based on reinforcement learning, and belongs to the technical field of cross between artificial intelligence and medical image analysis. The method first uses a plurality of source chest radiograph images and query texts to supervise and fine-tune a basic model; then a GRPO reinforcement learning framework is used to optimize a strategy model, and an indicator function guided by token entropy is introduced into a target function, and finally, the optimized strategy model is used to realize end-to-end parallel generation of chest radiograph report texts and lesion boundary box coordinates. The method improves the quality of chest radiograph report generation while realizing the visualization and positioning of lesions, and the integrated output form is more in line with the actual clinical workflow.

The invention provides a multi-modal data fusion characterization method and a multi-modal data fusion characterization system for commercial vehicle frame performance prediction, which are applied to the technical field of vehicle frame data modeling. Extracting structure topological parameters, section construction parameters, material attribute parameters, connection parameters, P-S-N curve data and field response data, and forming a target domain data set and an independent test set; then establishing a frame full-parameterization finite element model, combining actual measurement load spectrum statistics and derivation to generate a virtual load sample, extracting a two-dimensional load damage characteristic matrix, and constructing a source domain data set; and finally mapping the geometric topology feature, the parameter feature, the connection feature and the load feature to a unified three-dimensional voxel space to generate a three-dimensional multi-channel engineering data tensor. According to the method, the commercial vehicle frame sample construction efficiency, the multi-modal data utilization efficiency and the generalization ability of the performance prediction model can be improved.

The application provides a kind of commercial vehicle frame performance prediction multi-modal data fusion representation method and system, applied to vehicle frame data modeling technical field, the method first carries out automatic mining to multi-source heterogeneous engineering data, extracts structure topological parameter, section configuration parameter, material attribute parameter, connection parameter, P-S-N curve data and field response data, and forms target domain data set and independent test set;Then establish the full parameterization finite element model of frame, combined with the virtual load sample generated by statistical derivation of measured load spectrum, extract two-dimensional load damage feature matrix, and construct source domain data set;Finally, the geometric topological feature, parameter feature, connection feature and load feature are mapped to a unified three-dimensional voxel space, generating a three-dimensional multi-channel engineering data tensor.The application can improve the efficiency of commercial vehicle frame sample construction, multi-modal data utilization efficiency and the generalization ability of performance prediction model.

The invention relates to the technical field of distributed computing, in particular to an intelligent iterative knowledge base management system and method for on-site technical services, and the system comprises an edge layer which is used for collecting multi-modal data of on-site equipment in the operation process; the data acquisition layer is used for collecting multi-modal data and preprocessing the multi-modal data to obtain preprocessed multi-modal data; the distributed storage layer is used for carrying out distributed storage and deduplication storage on the preprocessed multi-modal data; the intelligent analysis layer is used for processing the preprocessed multi-modal data and constructing a technical service iteration knowledge graph; performing causal reasoning on the technical service iteration knowledge graph, and updating a pre-constructed technical iteration tree; the application service layer is used for providing field technical services for technicians according to the technical service iteration knowledge graph and the technical iteration tree, and the problems that multi-modal data fusion is low in efficiency, technical iteration manual dependence is high, knowledge and operation collaboration is poor and the like can be solved.