87results about How to "Realize the structure" patented technology

The invention discloses an academic information conflict resolution method based on evidence intensity index grading. According to the method, all conflict viewpoints and traceability chains thereof are reserved by adopting a'full-amount retention, non-merging and non-deleting 'strategy; calculating a multi-dimensional weighted evidence intensity index of a comprehensive journal influence factor, an introduced frequency and an author academic influence for each literature; judging the conflict severity according to the difference value of the evidence intensity indexes of the two conflict parties, wherein the smaller the difference value is, the higher the severity is; modeling a conflict cluster into an auxiliary node layer in a graph database, and superposing the auxiliary node layer on a core graph without interfering with an ontology framework; conflict severity ranking results drive calibration reduction of downstream weight calculations. The method can be used for information conflict management in academic knowledge graph construction, and interpretable conflict severity grading reference is provided.

The application provides a multi-agent cooperative task state management and abnormal backtracking method and system, relates to the technical field of artificial intelligence, and comprises the following steps: obtaining interaction data, state data and execution logs, constructing an initial task knowledge graph based on an ontology model, establishing a bidirectional causal chain by using a Bayesian network to form a dynamic knowledge graph, analyzing state migration rules when an abnormality is detected, identifying a propagation node, constructing an abnormal propagation subgraph, generating an influence chain, determining a recovery priority and triggering state rollback. The method can quickly trace the abnormal source, effectively control abnormal diffusion, and improve system robustness and task recovery efficiency.

The invention discloses an interactive load prediction method based on fusion of prior knowledge and data driving, and the method comprises the steps: reconstructing a load sequence into a trend load sequence and a disturbance load sequence through modal decomposition, and constructing a prior knowledge base of tag-feature weight according to SHAP interpretability analysis; and through a prior knowledge base and feature contribution analysis, channel-level weight learning is carried out by using an automatic correlation determination module, and key input features are obtained. And inputting the reconstructed load sequence and the key input features into an improved Transform model, mapping a priori knowledge base into a priori knowledge matrix, embedding the priori knowledge matrix into the model, and generating a load prediction result. And carrying out interpretability analysis on a prediction result, and feeding back and updating an analysis result to a priori knowledge base to realize dynamic collaborative updating of prediction and the knowledge base. According to the invention, organic combination of expert knowledge and a data-driven model is realized, and the precision and stability of load prediction are improved.

The application discloses a hospital department layout generative intelligent planning method and system, relates to the technical field of hospital department layout data processing, and comprises the following steps: acquiring hospital layout digital data, and generating hospital layout condition coding. A hospital department hard constraint graph is constructed based on the hospital layout condition coding, and a hospital department constraint residual error vector is calculated. The hospital department constraint residual error vector is input into a generative layout model, and a hospital department digital layout scheme is output. Through the closed-loop processing of the hospital layout condition coding, the hospital department hard constraint graph, the hospital department constraint residual error vector and the generative layout model, the hospital department layout generation is converted into a data processing flow that is codeable, traversable, feedbackable and iterative, and the beneficial effects of improving the layout generation constraint consistency, the data traceability and the digital scheme output property are achieved.

The invention relates to the technical field of high polymer materials, in particular to a high-flexibility polyester fluoropropylene dispersion and a preparation method thereof.The preparation method comprises the steps that linear saturated polyester diol is taken, an activating agent is added to conduct end group activating treatment on a polyester main chain, then a catalyst is added to conduct grafting treatment, and bridged polyester is obtained; introducing fluoropropylene gas into the reaction kettle of the bridged polyester, and carrying out copolymerization treatment to obtain a copolymer; cooling the copolymer to a preset value, adding a surfactant to perform coating treatment on the copolymer, and slowly adding deionized water to perform shearing treatment to obtain an aqueous dispersion; and adding tetraethoxysilane into the aqueous dispersion, and carrying out hydrolysis treatment under the condition that the pH value is 4.3-4.6, so as to obtain the high-flexibility polyester fluoropropylene dispersion. According to the invention, the elongation at break, bending tolerance and cracking resistance of the dispersion after film formation are improved, and the hidden danger of material failure under high-flexibility scenes such as flexible electronic base materials and bendable coatings is solved.

This application discloses a method and system for determining pollution causes based on the correlation between atmospheric diffusion conditions and emission contributions, relating to the field of atmospheric pollution cause identification. The method includes: aligning meteorological, emission, and receptor observations to a unified spatiotemporal reference to obtain multi-source baseline data; calculating a diffusion stability index characterizing vertical mixing and horizontal transport using meteorological monitoring, generating time-varying diffusion condition weights; generating a time-response diffusion kernel from the emission source to the receptor point using a diffusion model under unit emission conditions, and constructing diffusion-contribution correlation information with observed concentrations to characterize the temporal coupling strength; establishing a diffusion-contribution dynamic optimization model, using diffusion condition weights to constrain the coupling relationship, and iteratively solving for time-varying contribution coefficients based on the convergence target of simulated and observed concentrations, outputting the dynamic contribution of each source and the pollution cause. Thus, time-varying diffusion conditions are explicitly introduced into the source contribution inversion framework, achieving stable separation and reliable attribution of source contributions.

The invention relates to the technical field of industrial equipment monitoring, in particular to a power equipment fault online monitoring system for polyester staple fiber production, which comprises a feature extraction module, a dynamic analysis module, an evolution prediction module, an anomaly recognition module, a feature construction module and a diagnosis matching module. The system collects operation data of power equipment in real time, extracts time sequence features and calculates a dynamic offset vector, and further predicts a state evolution track and identifies preliminary abnormality. The feature construction module traces back to extract feature fragments associated with the anomaly, fault component signals of different physical meanings are obtained through multi-level signal deconstruction and input into a fault feature spectrum generator, and a three-dimensional fault feature spectrum reflecting the fault evolution law is constructed. And the diagnosis matching module performs mode matching on the three-dimensional map and a preset fault knowledge base to determine a specific fault type and an evolution stage thereof. According to the method, early discovery of fault germination, accurate identification of fault types and dynamic judgment of fault evolution stages are realized.

This application provides a method for regenerating ternary cathode materials based on acid etching and molten salt lithium replenishment, as well as the regenerated electrode material and battery. The method includes the following steps: pretreating the failed ternary battery cathode material with an organic acid solution; mixing the pretreated failed ternary battery cathode material, dopant, and lithium-containing molten salt, and performing a first-stage low-temperature molten salt heat treatment at a first specified temperature; further subjecting the material after the first-stage low-temperature molten salt heat treatment to a second-stage high-temperature heat treatment at a second specified temperature for 2-4 hours; and washing and drying the product after the second-stage high-temperature heat treatment to obtain the regenerated ternary cathode material. The wet acid etching process selectively removes rock salt phase impurities and contaminants from the electrode material surface, achieving interface cleaning and activation; the molten salt method for lithium replenishment using multi-element synergistic doping introduces multiple beneficial elements for bulk phase doping while replenishing lithium, thereby simultaneously strengthening the material's crystal structure and interface stability.

The present application relates to the technical fields of air filtration material and nanocomposite material, and discloses a PVA / chitosan nanofiber air filter element prepared by electrospinning and a preparation method thereof.The filter element comprises an electrospun nanofiber felt, which is composed of a PVA matrix and chitosan nanofibril (CSF) dispersed in the PVA matrix, and the mass ratio of CSF to PVA is 0.5wt% to 10wt%.The present application introduces positively charged rigid CSF, which not only gives the filter element a particle removal efficiency as high as 97% and an extremely low pressure drop, but more importantly, CSF effectively improves the structural stability of the hydrophilic PVA matrix in a high humidity environment as a rigid skeleton, effectively solves the pain point of the traditional filter element that is easy to collapse after absorbing water, and can be quickly biodegraded in the natural environment after being discarded, and has environmental protection and commercial application value.

The application relates to the field of artificial intelligence and computer vision, and provides a three-dimensional point cloud scene target positioning method based on a natural language instruction, which comprises the following steps: screening a relation triple containing an object category in a three-dimensional visual scene; determining a hierarchical attribute of the object category and constructing a hierarchical knowledge graph; analyzing a given natural language description to obtain a plurality of two-order semantic triples; adding the hierarchical attribute to the object in the two-order semantic triple to obtain a hierarchical two-order semantic triple; determining an initial visual feature of an object in a three-dimensional point cloud scene to be recognized; outputting a prediction score of the object; screening the objects corresponding to the prediction scores from high to low to obtain candidate objects; outputting a matching score of the candidate objects and a first high-order semantic triple; and determining a target object in the three-dimensional point cloud scene to be recognized. The application improves the prediction accuracy and has stronger robustness.

The invention relates to the technical field of knowledge maps, in particular to a ship design reference data intelligent recommendation method and system based on a knowledge map, and the method comprises the steps: constructing a multi-dimensional ship knowledge map; analyzing the received design task to obtain a design basic parameter and a target performance index, constructing a task related sub-graph, extracting a task constraint condition, and generating a target index semantic vector; carrying out graph structure representation learning on the task related sub-graphs by utilizing a graph neural network coding model, and carrying out feature fusion on the task related sub-graphs and target index semantic vectors to generate comprehensive design reference vectors; and based on the comprehensive design reference vector and the task constraint condition, matching historical cases in the knowledge graph to obtain a design reference recommendation result. According to the method, by constructing the multi-dimensional ship knowledge graph, design basic parameters, performance indexes and constraint conditions are systematically managed; historical design cases can be intelligently matched, high-correlation design reference results are provided, and design efficiency and accuracy are improved.

This invention relates to a fully localized hybrid retrieval and knowledge graph construction method and system for standard gas R&D and production, belonging to the interdisciplinary field of artificial intelligence and industrial control. The method includes: acquiring multi-source heterogeneous documents and performing text parsing, mixed Chinese and English word segmentation, and terminology standardization; employing a hybrid retrieval strategy that weights and fuses BM25 keyword retrieval and vector semantic retrieval to obtain fused retrieval results; automatically extracting entities and relations from the fused retrieval results to generate triplet data; constructing a knowledge graph based on the triplet data, with nodes bearing source document identifiers and version tags; and deploying the embedded model, text units, semantic vectors, and knowledge graph on a local server. This invention achieves the fusion of precise matching and semantic recall, ensures core data security, transforms unstructured knowledge into a computable and traceable knowledge network, and significantly improves the efficiency and intelligence level of knowledge retrieval in standard gas R&D and production.

The invention relates to a power transmission and transformation reduction verification method and system based on a knowledge graph, and belongs to the technical field of power transmission and transformation project review. The method comprises the following steps: acquiring review file data of a power transmission and transformation station, extracting text content according to a natural language processing technology to construct a knowledge graph sub-graph, and generating a design review standard file list; performing text word segmentation based on the design review standard file list to obtain a structured review template; inputting the review file data of the power transmission and transformation station into a model, and verifying the review key points of the structured power transmission and transformation project in combination with the file integrity and the structured review template; and taking the review key points as cue words, writing the cue words into an intelligent review verification model, and generating a power transmission and transformation review report.

This invention provides a method, system, device, and storage medium for smart contract vulnerability detection, belonging to the fields of blockchain security and artificial intelligence technology. It includes extracting an abstract syntax tree, control flow graph, and data dependency graph from the original smart contract code; obtaining a composite graph object based on the abstract syntax tree, control flow graph, and data dependency graph; processing the composite graph object based on node semantics and function call relationships to obtain a hierarchical heterogeneous graph; constructing a heterogeneous graph Transformer network; using a heterogeneous graph Transformer encoder to process the hierarchical heterogeneous graph for encoding node and edge information, obtaining node representation vectors that fuse semantic and structural information; extracting multi-scale features from the node representation vectors; fusing the extracted multi-scale features using learnable weights; and processing the fused multi-scale features using a base classifier cluster to obtain vulnerability type prediction results and confidence levels. This invention achieves full-process optimization from structural modeling and semantic fusion to decision reasoning, significantly improving the accuracy and interpretability of smart contract vulnerability detection.

The invention relates to the technical field of new energy and intelligent scheduling, in particular to a shared electric vehicle solar power conversion network optimization system, which comprises a data acquisition module, a prediction module, an available energy and inventory constraint generation module, an inventory state management module, a linkage scheduling module and an execution and update module. According to the invention, the system collects and predicts solar power generation data, battery replacement demand data and battery state data in a rolling time window, constructs an available energy constraint including an energy lower bound, an energy expectation and an energy upper bound, and generates an inventory constraint based on a battery replacement demand; a battery state is divided and updated to form an inventory state, a charging scheduling instruction and an operation and maintenance supply scheduling instruction are generated under a constraint condition, operation and maintenance supply arrival time is associated with inventory constraint establishment time, and then rolling updating is performed according to an execution result, so that collaborative scheduling of a battery switching network under a solar energy supply condition is realized.

This invention belongs to the field of engine turbocharger testing technology and discloses a method and system for evaluating the transient response of engine turbochargers. The method includes: test preparation, operating condition setting, transient excitation and data acquisition, calculation of speed increment time, calculation of intake pressure to establish efficiency indicators, and two-dimensional diagnostic evaluation. This invention effectively solves the technical problem that existing evaluation parameters cannot distinguish the root cause of response lag, and has advantages such as accurate positioning and strong guidance. It is particularly suitable for the research and optimization of turbocharger systems with stringent transient response requirements, such as hydrogen fuel cell internal combustion engines.

The invention discloses an intelligent talent recruitment screening method based on multi-source data fusion, and relates to the technical field of big data management, and the method comprises the steps: collecting candidate multi-source heterogeneous data, carrying out the feature extraction, and generating a multi-modal initial feature vector; fairness related attribute data and historical recruitment result data of candidates are collected and combined with the skill confidence feature set, and a recruitment decision causal graph model is constructed; and based on a recruitment decision causal graph model, performing causal effect estimation and anti-fact reasoning by taking the multi-source verification confidence as a weight, performing fair correction calculation on candidate ability evaluation represented by the skill confidence feature set, and outputting a fair correction talent sorting list. According to the method, the multi-source evidence cluster is constructed in the shared semantic space, and the multi-source verification confidence is calculated, so that the unified management and credibility quantification of the candidate multi-source heterogeneous data in the skill dimension are realized, and the controllability and traceability of a skill assessment result in a big data management scene are improved.

The utility model discloses a kind of anti-deformation supporting devices of wheel hub mould, belong to the technical field of wheel hub mould supporting structure, including shade cover, upper pressure rod, lower bearing rod, lower cross pipe, end connecting plate and locking bolt.Shade cover top is equipped with dehumidification structure, both sides are equipped with upper pressure rod and form top shade structure, upper pressure rod is pressed on the casting surface side slope of wheel hub side module, lower bearing rod is equipped below, lower cross pipe is equipped between two lower bearing rods and forms bottom supporting frame, top shade structure and bottom supporting frame two ends are equipped with end connecting plate.The utility model is convenient to install and can be reused, can provide effective support and protection to these key parts such as wheel hub side module's casting surface and casting surface joint line after wheel hub side module production is completed, to effectively reduce the risk of physical deformation or corrosion in transportation and storage process.

This invention relates to the field of engineering design technology, and in particular to a method and system for auxiliary design of nuclear power plant engineering modifications. A method for auxiliary design of nuclear power plant engineering modifications includes: acquiring multi-format design documents and structuring the content of these documents to obtain structured data with consistent format; establishing a hybrid engine using a first language model and preset rules; extracting nuclear power plant engineering documents using the hybrid engine to construct a tag library; segmenting the structured data and saving it in association with the tag library to construct a nuclear power knowledge base; training a second language model based on the nuclear power knowledge base to establish a domain-specific vertical model; acquiring design instructions and generating auxiliary information using the domain-specific vertical model. This invention significantly improves knowledge utilization by leveraging structured data and a nuclear power knowledge base; ensures professionalism and accuracy through the combination of a vertical domain model and the nuclear power knowledge base; and creates an intelligent auxiliary design process using design instructions, improving design efficiency and document quality.

The invention relates to the technical field of information management and data visualization, in particular to a natural resource compliance management system with a visual internal control flow, which comprises a budget management module, an expenditure management module, a purchase management module, a contract management module and a compliance visualization module. Wherein the budget management module is used for outputting a budget allocation table; the expenditure management module is used for outputting audited expenditure execution records; the purchasing management module is used for outputting purchasing result information associated with budget and expenditure; the contract management module is used for matching a corresponding contract text template and generating a contract performance tracking chain; and the compliance visualization module is used for outputting a compliance visualization map of a natural resource internal control whole process. According to the method, the compliance and transparency of internal control of the natural resources are effectively improved by constructing full-process association of budget, expenditure, purchase and contracts and realizing visual display.

The application relates to the technical field of production quality control, and discloses a silver powder production quality control method and system fusing multiple technologies and a storage medium. The method comprises the following steps: collecting silver powder production data through a distributed sensor to obtain a quality characteristic correlation matrix, adjusting a gamma distribution hyperparameter to obtain an adaptive parameter by using a self-adaptive Bayesian algorithm based on a defect rate parameter, generating a device cooperative control instruction set by optimizing a control strategy through a decision fusion algorithm, performing linkage control on a ball mill, an atomization drying tower and a grading screening device to obtain an execution result, evaluating the execution result to obtain a performance index, and correcting the control instruction set according to the performance index. The application solves technical problems such as performance degradation of a traditional Bayesian control under a large defect rate parameter condition in a silver powder production process, insufficient control precision caused by a lack of cooperative decision fusion of multiple quality characteristics, and an influence on overall quality control effect caused by a lack of cooperative linkage control among production devices.

The invention relates to the technical field of inorganic non-metal insulating materials, in particular to an insulating material resistant to high voltage breakdown at high temperature and a preparation method thereof.The preparation method comprises the steps of raw material pretreatment and homogeneous mixing, forming and low-temperature pre-sintering, high-temperature densification sintering, gradient cooling annealing and finish machining. By optimizing the combination of the base material, the reinforcing phase material and the functional modifier, the insulating material has excellent high voltage breakdown resistance and high volume resistivity in a high-temperature environment, and the reliability and stability of the material under extreme conditions are ensured; by adopting the preparation method, high densification and microstructure optimization of the material are realized, so that the mechanical strength and electrical performance consistency of the insulating material are improved.

The invention relates to a power report generation method and device, computer equipment and a readable storage medium, and relates to the technical field of power. The method comprises the following steps: extracting a report theme and data description information of a to-be-generated power report from a power report generation demand text; determining at least one initial analysis item matched with the report theme from a plurality of power report analysis items of a pre-constructed power report knowledge base, and obtaining and displaying an initial report outline; in response to an editing operation and a confirmation operation on the initial report outline, obtaining a target report outline; the target report outline comprises at least one target analysis item; obtaining target power data corresponding to each target analysis item from a power database based on the target report outline and the data description information; and according to the target report outline and the target power data corresponding to each target analysis item, generating and displaying a target power report. By adopting the method, the writing quality and writing efficiency of the power report can be improved.

The invention provides a large-scale generation method and system for interpretable question and answer pairs for the vertical field, and relates to the technical field of data processing, and the method comprises the steps: 1, constructing a cross-modal heterogeneous meteorological knowledge graph; heterogeneous graph neural network modeling is carried out on the cross-modal heterogeneous meteorological knowledge graph, an embedded vector of each node is learned, a knowledge graph with node embedded vectors is obtained, semantic community division is carried out on an entity based on the embedded vectors, and an optimized knowledge graph is generated. According to the method, through knowledge graph optimization, conical semantic space construction, structured evidence chain retrieval and pre-training large language model generation, large-scale generation of interpretable meteorological question and answer pairs in the vertical field is realized, and interpretability and credibility of question and answer results are improved.

The invention belongs to the technical field of fan equipment maintenance management, and relates to an intelligent maintenance management system and method for fan equipment, and the system comprises a data access unit which generates a unified maintenance data set according to a maintenance data exchange standard; the standard library unit is used for storing an operation instruction standard, an equipment fault model library, an acceptance standard, an evaluation standard and a maintenance file package template; the diagnosis decision-making unit is used for outputting diagnosis results containing phenomena, reasons, conclusions and disposal suggestion fields and one-to-one maintenance schemes; the work order management and control unit is used for generating a work order and a maintenance file package and forming a node constraint acquisition process record; the archiving updating unit is used for generating an archiving data set and updating a fault model through consistency verification during closed loop; and the evaluation unit is used for generating an evaluation result after closed loop. According to the technical scheme, the cross-system data connection consistency and evaluation evidence support stability can be improved, the reusability of the maintenance decision basis is enhanced, and the management requirements of maintenance operation on closed-loop treatment and quality management and control are met.

The invention relates to the technical field of pipeline design, and discloses a large-scale water delivery pipeline deepening design method based on a visual programming language, and the method comprises the steps: extracting key data from a design drawing of a water delivery pipeline, and carrying out the preprocessing to obtain standard key data; determining spatial information of the plurality of control points based on the standard key data, and constructing a three-dimensional pipe section model according to the spatial information; preset section parameters are obtained, a three-dimensional entity pipeline model is generated in combination with the three-dimensional pipe section model, and key nodes are added for the three-dimensional entity pipeline model in combination with preset key node parameters; and engineering drawings and part expansion drawings of the components and the parts are generated based on the numbers. Through the cross-platform data engine, full-link automatic processing of design drawing-three-dimensional model-construction drawing-manufacturing drawing data is achieved, the parameterization degree of pipeline design is improved, the deepening design efficiency and precision are improved, and quick response can be achieved by means of the architecture of standard key data and a parameterization model in the face of design change.

The invention discloses an underwater image enhancement method and system based on physical prior and target features, and belongs to the technical field of image processing. In order to solve the technical problem that an existing underwater image enhancement method is difficult to consider imaging physical consistency, visual quality and downstream target identification task availability at the same time, physical prior information is extracted based on an underwater image, and the physical prior information comprises a transmissivity image and a background light image; obtaining target features based on the underwater image; extracting image features of the underwater image through an enhancement network, and fusing and reconstructing the image features, the physical prior information and the target features to obtain an enhanced image; and training the enhancement network, and processing the underwater image by using the trained enhancement network to obtain an enhanced image. According to the method, the purposes of realizing high-quality visual restoration on the premise of meeting underwater imaging physical constraints and remarkably improving underwater target recognition precision and related downstream task performance can be achieved.

This invention discloses a big data analysis-based method and system for art teaching management, relating to the field of educational information technology. The method includes: acquiring multi-source teaching big data for a target art teaching unit; constructing a pattern graph extraction unit and a teaching pattern graph based on the multi-source teaching big data; collecting stage-specific student assignment data and stage-specific student process data for the target art teaching unit according to preset stage window constraints; extracting pattern graphs from the stage-specific student assignment data through the pattern graph extraction unit to obtain a student pattern graph set; comparing the student pattern graph set with the teaching pattern graph; making evaluation decisions based on the comparison results; and implementing stage-specific management of the target art teaching unit based on the evaluation decision report. This invention solves the problems in existing teaching management where teacher evaluation is subjective and difficult to quantify, teaching evaluation lacks process analysis, and lacks the ability to deeply analyze the content of assignments themselves.

The invention provides a method and a system for measuring and calculating a cargo communication volume. The method comprises the following steps: acquiring total freight data of a target area; the total freight data comprises highway freight data and common highway freight data; based on the total freight data, determining the expressway inter-region freight volume ratio of the expressway freight volume in the total expressway freight volume; based on the expressway freight volume and the expressway inter-region freight volume ratio, calculating an initial cargo exchange volume between the target region and other regions; based on the historical transportation data, determining a correction coefficient corresponding to the cargo exchange volume; the correction coefficient is used for representing the transportation characteristics of the transit vehicle in the target area; and according to the correction coefficient, correcting the initial cargo exchange volume to obtain a corrected cargo exchange volume. In the mode, the total cargo exchange amount corresponding to the regional road reflecting the real cargo interaction condition between the regions can be calculated, and the accuracy and reliability of a statistical result are improved.

The invention discloses an edge deployment-oriented neural network video coding model lightweight method, which comprises the following steps of: constructing a video compression model as a reference model to be optimized; inserting a dry mask layer which can be trained into a preset position of a reference model to be optimized, and performing joint training on mask parameters by utilizing a sparse loss function and rate distortion loss to obtain mask parameter distribution; when the proportion of the mask parameter compressed to be zero in any mask layer reaches a preset target sparsity, sparse updating of the corresponding mask layer is stopped, and a mask layer state reaching a sparsity target is obtained; performing fusion operation on each mask layer and the adjacent network layer to obtain a neural network model after structure compression; and removing all mask layers to obtain a lightweight video coding model, and deploying the lightweight video coding model on edge equipment to execute a video compression task. According to the method, the model complexity can be greatly reduced, and the reasoning speed and the deployment efficiency of the edge equipment are improved.