16results about How to "Improve data utilization efficiency" patented technology

The invention relates to the technical field of lithium battery charging and discharging management, and discloses a lithium battery charging and discharging detection and management method, device and equipment and a storage medium, and the method comprises the steps: carrying out the data collection of the charging and discharging process of a lithium battery pack, applying consistent Gaussian noise, and obtaining an electrode-level expansion data set and a battery-level expansion data set; performing feature mapping and feature mixing by minimizing the maximum average difference loss function to obtain a mapping feature vector; estimating the state of charge and the state of health of the battery through a fusion model of a recurrent neural network and a physical information hybrid neural network to obtain a state of charge estimated value and a state of health estimated value; and constructing a battery pack electric heating model of the lithium battery pack, and generating a corresponding charging and discharging control instruction sequence. Self-adaptive adjustment can be carried out according to the real-time state of the battery, potential safety hazards such as over-charging and over-discharging and out-of-control temperature are effectively avoided, and the use safety of the battery pack is remarkably improved.

The invention belongs to the technical field of Tokamak plasma rupture prediction, and discloses a Tokamak plasma rupture prediction model construction method and application, and the method comprises the steps: employing a training data set to train a multi-mode convolution self-coding neural network, and obtaining a rupture prediction model; wherein one training sample in the training data set is a slice of different diagnosis signals at the current moment; the multi-modal convolution self-coding neural network comprises a convolution feature extraction module, a splicing module, a feature self-coding module, a segmentation module and a deconvolution feature reconstruction module. According to the method, the constructed prediction model can process data of various modes and automatically perform feature extraction; and the model can be trained by only using non-fracture samples, and also can use fracture precursor samples when fractures occur, so that the model can adapt to a data environment in which the sample proportion on a future device is uncertain.

The invention provides a dynamic analysis method for integrated processing of business data and financial data. Comprising the steps of determining a target data source through a data structure and business requirements in an enterprise, and obtaining a preliminary screening data source set; selecting a data source meeting the requirement from the primarily screened data source set as a target data source by adopting a coverage check algorithm; indexing the data set as a target business and financial data set according to the attribute of the preliminary data set, and importing the target business and financial data set into an analysis database for data fusion; and carrying out dynamic analysis based on time, and carrying out static analysis, comparative analysis and periodic dynamic analysis on the target data set and displaying. Through a multi-level data processing and fusion method, the comprehensive analysis capability of the business data and the financial data is effectively improved, particularly in the aspect of correlation analysis between the business and the finance, an efficient data analysis tool is provided for an enterprise, and enterprise decision and management can be supported.

The invention discloses an energy storage configuration optimization method based on multi-source data fusion, which relates to the technical field of multi-source data fusion. The method collects comprehensive resource data of each energy storage device in an electric power system; processes the comprehensive resource data to obtain electric power operation parameters and a load characteristic curve diagram, analyzes the load characteristic curve diagram to obtain a peak segment and a low value segment; sets decision indicators, target threads and restriction conditions according to the obtained electric power operation parameters, peak segments and low value segments; generates dynamic parameters according to the decision indicators and combines them to obtain a random initial solution, obtains candidate individuals according to the random initial solution, the target thread and the restriction conditions, cross-replaces the candidate individuals to obtain cross-individuals, and cyclically compares the cross-individuals to obtain excellent individuals; effectively enhances the configuration efficiency of the electric power system, improves the operation efficiency and stability of the electric power system, and improves the data utilization efficiency and the response speed of the energy storage device.

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 application provides a casing running process comprehensive quality evaluation method, comprising the following steps: step 1, collecting casing data and equipment data to be operated and real-time data of the operation site; step 2, inputting the data into a bidirectional time sequence model to monitor the process data in real time and evaluate the data content; step 3, monitoring the hook static time during the casing operation in real time; step 4, monitoring the lowering speed during the hook activity in real time under the condition that the hook static time during the operation is less than the standard value; step 5, monitoring whether the hook moves the casing according to the regulation during the grouting under the condition that the lowering speed during the hook activity is less than the standard value; and step 6, judging whether the casing running process comprehensive quality is satisfied in real time according to the obtained detection information and the actual data. The casing running process comprehensive quality evaluation method can cope with the variable conditions of the operation site through multi-layer judgment, and solves the problem that the traditional method is difficult to accurately determine under complex conditions.

The invention discloses a cloud service operation and maintenance management platform for an intelligent mining area, and the platform comprises the steps: system carding and analysis: carrying out the comprehensive carding and analysis of an existing production management system, a safety monitoring system, a geological exploration system and an equipment management system of the mining area, determining the functions and characteristics of all systems, finding out the generality and difference, and laying a foundation for the subsequent integration; data integration and platform establishment: big data and cloud computing technologies are adopted, a unified data platform is established, data intercommunication and sharing among systems are realized, and a complete data resource pool is formed; the system has the beneficial effects that comprehensive integration and sharing of various information systems and data resources in the mining area are realized, and the data utilization efficiency is improved; real-time and comprehensive information support is provided for mining area managers, and scientificity and accuracy of decision making are improved; and through automatic and intelligent management, the production efficiency is improved, and the operation cost is reduced.

The present invention relates to the technical field of maternal and infant content recommendation, and in particular to a personalized content push analysis system and method for maternal and infant groups based on AI scenarios. The steps of the method include: obtaining the historical conversation content and current conversation content of the user, and obtaining the historical purchase record of the user; estimating a first set of infant demand products based on the historical conversation content and the historical purchase record; extracting infant products in the current conversation content to form a second set of infant demand products; generating product recommendation content based on the first set of infant demand products and the second set of infant demand products. The present invention improves data utilization efficiency and product recommendation accuracy by correlating user conversation content with user purchase behavior in AI scenarios.

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 invention provides a physical asset state evaluation method and system for a material management platform, and belongs to the technical field of asset management, and the method specifically comprises the steps: obtaining the data size of storage data of physical assets of the material management platform, obtaining the number of data deviation assets in the material management platform and data deviation state values of different data deviation assets when it is determined that the data utilization efficiency of the material management platform meets requirements in combination with historical call data of different physical assets; when the data utilization efficiency in the material management platform is combined to determine that the material management platform needs to be optimized, the optimization target and the optimization mode of the physical assets are determined according to the deviation condition between the platform data of the information items of different physical assets in the material management platform and the real data and the historical calling data; therefore, the data utilization efficiency of the material management platform is 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.

The application provides a trifunctional terahertz super surface and a free topology reverse design method thereof, which are sequentially arranged from bottom to top as an Au metal back plate layer, a PTFE dielectric layer and a top layer tunable mixed pattern layer; the top layer tunable mixed pattern layer is composed of a fixed function skeleton and a top layer silicon tunable region; the fixed function skeleton comprises a non-complete silicon arc-shaped open ring, a nested silicon square resonator and a plurality of VO2 stepped pixel blocks distributed at the edge positions of the top layer tunable unit; the nested silicon square resonator comprises a first silicon resonant unit located at the opening direction position of the non-complete silicon arc-shaped open ring and two second silicon resonant units located at the diagonal positions of the non-opening direction of the non-complete silicon arc-shaped open ring; and the top layer silicon tunable region comprises a second local control area located at the lower half of the non-complete silicon arc-shaped open ring, a third local control area located on the first silicon resonant unit and a first local control area located on the second silicon resonant unit.