27results about How to "Realize the structure" patented technology

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.

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 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.

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.

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.

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 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 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.

This invention relates to the field of intelligent manufacturing technology and discloses an artificial intelligence-based intelligent manufacturing production line collaborative control system, including a multi-source data sensing module, a digital twin virtual-real mapping module, a multi-agent collaborative decision-making module, and a human-machine interaction module. The multi-source data sensing module collects and preprocesses multi-source heterogeneous data across the entire production line, constructing a real-time database. The digital twin virtual-real mapping module builds a 1:1 high-precision digital twin model of the production line based on real-time data, enabling bidirectional real-time data interaction between the virtual and real worlds. It also determines the disturbance level of the production line by calculating multi-dimensional disturbance coefficients and introducing a time decay function to accumulate the effective disturbance influence coefficient. The multi-agent collaborative decision-making module matches and executes hierarchical collaborative control strategies according to the disturbance level, generating a comprehensive collaborative control scheme. This achieves accurate quantification and hierarchical collaborative response to production disturbances, significantly improving the adaptive capability and operational stability of the intelligent manufacturing production line.

The application belongs to the technical field of building construction, and particularly relates to a prefabricated elevator shaft inner wall anti-falling noise reduction plate and a mounting method, which comprises a noise reduction plate, cast-in-place concrete and an anti-falling connecting piece, the anti-falling connecting piece is horizontally arranged and embedded in the cast-in-place concrete, the noise reduction plate and the cast-in-place concrete are integrally formed through the anti-falling connecting piece, the noise reduction plate comprises a convex block plate and a concave groove plate, a plurality of convex block portions are arranged on the side surface of the convex block plate close to the concave groove plate, a plurality of concave groove portions corresponding to the convex block portions are arranged on the side surface of the concave groove plate, and the side surface of the concave groove plate away from the convex block plate is fixedly connected with the cast-in-place concrete through the anti-falling connecting piece; compared with the prior art, the application has the advantages and positive effects that the cost is low, the wall cost is reduced, the building wall noise reduction and structure integration can be realized, the building wall heat preservation and structure have the same service life, and the problems of high maintenance cost and high maintenance frequency of the traditional sound absorption and noise reduction system are solved.

This invention relates to the field of traditional Chinese medicine (TCM) manufacturing technology, and discloses a large-scale feature classification algorithm and system for detecting anomalies in the TCM processing process. The algorithm includes: acquiring a multimodal real-time dataset, parsing it in parallel to obtain information emergence points, and reconstructing them into a feature respiration spectrum; using the feature respiration spectrum to construct a process semantic graph and process manifold, performing vector decomposition to obtain the semantic orthogonal decomposition distance between representative traits and progress deviations; constructing a virtual correction force field to obtain active intervention instructions, driving risk inference to generate a structured anomaly diagnosis report. This invention achieves accurate capture and digital reconstruction of the processing sensitivity window, transforming qualitative anomaly judgment into quantitative diagnosis, solving the problems of difficult identification of latent anomalies and lack of correction suggestions, and improving the inherent safety, batch quality uniformity, and reliability of quality traceability in the TCM processing process.

This invention provides a single-motor two-speed hybrid system and vehicle, belonging to the field of new energy vehicles. It includes an engine, an input shaft, an intermediate shaft, a motor connected to the input shaft, a two-speed shift assembly, a power on / off assembly, and a drive axle. The power on / off assembly connects the engine and the input shaft. The engine and motor are located at opposite ends of the input shaft. The motor establishes a first transmission path with the drive axle through the input shaft, and a second transmission path through the input shaft and the intermediate shaft. When the power on / off assembly connects the engine and the input shaft, the engine establishes a third transmission path through the input shaft and the drive axle, and a fourth transmission path through the input shaft and the intermediate shaft. The two-speed shift assembly can simultaneously disconnect and periodically engage the first and second transmission paths, as well as the third and fourth transmission paths. This invention solves the problems of poor adaptability of existing single-motor single-speed hybrid systems and the complex structure of multi-speed hybrid systems.

The present application belongs to the technical field of transformer cable head construction quality, and particularly relates to a transformer cable head quality detection method based on target detection and a multi-modal large model. A transformer cable head quality detection component is set; a statement related to the installation quality of the transformer cable terminal is parsed and converted into a rule card executable by the multi-modal large model; the collected electric room construction site image is input into the multi-modal large model, the image is preliminarily globally semantically analyzed according to the predefined rule card, a multi-modal structured preliminary report is generated, if the overall confidence of the result of the target detection model is lower than the preset multi-modal semantic threshold, the image area is marked as a semantic uncertain area; based on the semantic uncertain area, the target detection model is used for directional visual detection on the semantic uncertain area, the result of the target detection model is obtained, and the result of the target detection model is fed back to the multi-modal large model for confidence reasoning review to obtain the final comprehensive judgment.

This invention discloses a lightweight traffic video processing and multimodal intelligent perception system and method, comprising: a streaming media preprocessing module for performing availability detection and parameter configuration on the accessed video stream and outputting a stable video stream; a video polling and frame extraction module for establishing a video polling queue, extracting key frames from the video stream, and splicing them into a lightweight video stream; a deep learning algorithm module for using a lightweight visual model to perform preliminary traffic event detection on the lightweight video stream; a multimodal large model service module for secondary confirmation of detected traffic events, extraction of key information, and generation of event reports; and a perception and analysis platform module for realizing the visualization, hierarchical dispatch, and closed-loop management of traffic events. Compared with existing technologies, this invention reduces the computational power requirements for video processing through video stream polling and key frame extraction mechanisms, enabling lightweight access and processing of large-scale road network videos.

This application relates to a method, apparatus, and computer equipment for predicting the operational status of a distribution network, belonging to the field of computer technology. The method includes: obtaining a current power grid hierarchical tree and a current power grid knowledge graph based on current operational data of the distribution network; extracting abnormal paths from the current power grid knowledge graph through the matching relationship between tree nodes in the current power grid hierarchical tree and graph nodes in the current power grid knowledge graph; determining the current operational status of the distribution network based on the distribution of matching tree nodes of abnormal graph nodes in the abnormal paths within the current power grid hierarchical tree; the current operational status is used to characterize the operational state of entities in the distribution network; and predicting the operational status of the distribution network based on the current operational status, the current power grid hierarchical tree, and the current power grid knowledge graph, obtaining the predicted operational status result of the distribution network. This method can improve the accuracy of the predicted operational status.

The invention discloses an intelligent inspection system and method for state maintenance of power plant equipment, and relates to the technical field of intelligent inspection. The system comprises a conduction chain construction module, an abnormity identification module, a root node tracing module and a maintenance execution module. According to the method, verification and data archiving are completed by constructing an equipment-level directed working condition topological conduction chain and a structured conduction relation library, collecting measurement point parameters, identifying abnormal measurement points and locking downstream nodes, upward tracing and determining fault root nodes, generating a conduction path and executing targeted maintenance. According to the invention, accurate traceability of faults is realized, false alarm and missing alarm are reduced, inspection efficiency and maintenance pertinence are improved, and stable operation of power plant equipment is guaranteed.

This invention relates to the fields of data processing and industrial internet technology, and discloses information management methods, systems, electronic devices, and storage media. The methods include receiving heterogeneous data from multiple sources such as carbon emissions and energy consumption, mapping them into holographic data packets containing a unified spatiotemporal reference; dynamically constructing a spatiotemporal causal correlation spectrum based on the correlation weights between data nodes; calculating the topological potential energy value of a physical region, and generating hardware control commands when the potential energy value exceeds a warning threshold to adaptively adjust the sampling frequency of the front-end device; monitoring the correlation entropy of data nodes, and when a phase transition condition is met, serializing and migrating a stable subgraph to a second storage layer and generating a hash index; retrieving data based on the index and performing integrity verification. This invention achieves dynamic adjustment of the sampling frequency through topological potential energy feedback and uses correlation entropy to drive storage evolution, optimizing hardware resource configuration while ensuring the spatiotemporal correlation and reliable traceability of data.

The application provides a medical information processing method, system and medium, and an edge computing device. The medical information processing method comprises the following steps: analyzing an input question as a structured query; searching the structured query in a database by using a dual-mode retrieval engine to obtain a candidate evidence literature set; analyzing each candidate evidence literature in the candidate evidence literature set to generate an evidence quality matrix; and using a large language model to perform long-chain reasoning and self-check iteration processing on the structured query, the candidate evidence literature set, the evidence quality matrix and patient individual characteristics to generate a clinical decision support report. The medical information processing method of the application uses multi-agent cooperation and self-checking strategies to improve the accuracy and reliability of medical information processing.

The application discloses a network security situation awareness system and method based on deep learning, comprising the following steps: collecting and processing multi-source heterogeneous network security data in a target network security scene; constructing an event semantic unit set and an event semantic coupling relationship set; performing constraint filtering and attack chain fragment arrangement processing on discrete security events; constructing a heterogeneous time sequence relationship topology and performing time sequence embedding coding and correlation fusion; extracting a behavior evolution sequence and mapping to an attack chain stage atlas; generating a network security situation representation vector set through an improved TGN model; identifying complex attack behaviors, confirming a propagation path and performing situation risk grading to generate a network security situation awareness result. The application fully utilizes a deep learning method, constructs a multi-source data attack chain analysis model, realizes network situation awareness, and has the advantages of high accuracy, strong risk prediction and timely response.

The application discloses a kind of metal mine black lamp factory intelligent design method and system, it is related to computer aided design technical field, including, acquisition working condition data and process structure data, extract the relationship between crushing, grinding, classification, flotation and thickening dehydration, construct process topology;According to beneficiation knowledge graph and process mechanism element set, mechanism corrector is constructed, mechanism corrector is used to the whole process design prediction execution section projection and constraint solution, output correction prediction, and register inconsistent event, form correction rule log;Corresponding interactive feedback data is obtained with design configuration set, and incremental update is carried out to beneficiation knowledge graph and process mechanism element set, obtain interaction baseline, and the next round of design iteration is completed by interaction baseline.The application carries out section projection and constraint solution to the whole process design prediction by constructing mechanism corrector, and the structured correction and constraint consistent expression of design prediction result are realized.

This invention relates to the field of computer technology and discloses a personnel allocation decision-making method, system, electronic device, and storage medium based on working hour data. The method includes acquiring working hour data, classifying and structuring it, and performing multi-dimensional statistical analysis on the classified working hour data to generate a working hour distribution map; calculating the maximum number of personnel at full capacity based on the total annual working hour demand and the standard working hours available per person per year; acquiring data on the difference between actual working hours and statutory standard working hours, as well as unused leave, and calculating the equivalent personnel shortage based on an overtime compensation model; establishing a resource balance model that includes constraints such as shift work, skill level matching, time period distribution, and management redundancy, calculating the resource balance coefficient, and correcting the maximum number of personnel at full capacity to obtain an executable personnel allocation base; and determining the personnel allocation plan by combining the equivalent personnel shortage, the personnel allocation base, and future working hour demand. This invention can improve the executability of personnel allocation decisions.

The application discloses a power industry dynamic knowledge base construction method and system based on a large language model, relates to the field of electric digital data processing, and comprises the following steps: receiving a power field document imported by a user, and converting the power field document into an electronic document; calling a large language model to analyze the electronic document, extracting professional term definitions, chart position information and document logical levels in the electronic document, and generating knowledge units containing multiple contexts; connecting the knowledge units into directed knowledge links, calculating semantic similarity between adjacent knowledge units in the directed knowledge links, segmenting the directed knowledge links, and obtaining multiple knowledge topics; receiving a knowledge retrieval instruction, determining a target knowledge topic corresponding to the knowledge retrieval instruction; extracting a target knowledge unit corresponding to the target knowledge topic, and generating a knowledge retrieval result based on a target context of the target knowledge unit. The application can optimize knowledge base construction and guarantee the integrity of knowledge base data.

This invention discloses a cloud-based method for the integrated supervision of cross-border e-commerce, comprising the following steps: S1, real-time collection of cross-border e-commerce transaction data, logistics and distribution data, warehousing data, and customs supervision data, and standardization processing using a distributed cleaning method; S2, construction of a multi-source heterogeneous graph based on the standardized data; S3, annotation of node and edge types, relationship types, and timestamps in the graph, and extraction of high-dimensional features using a relation-enhanced graph Transformer; S4, local and global aggregation using a multi-layer attention mechanism to generate an aggregated view; S5, construction of a multi-objective supervision task model and generation of a resource scheduling scheme; S6, identification of transaction, logistics, and supervision anomalies, and generation of risk warning and intervention strategies; S7, dynamic optimization of the relationship structure and Transformer parameters based on execution feedback. This invention, based on high-dimensional graph representation and optimized scheduling mechanism, achieves integrated supervision and risk warning of cross-domain multi-source data in cross-border e-commerce.

The present application relates to the field of three-dimensional surveying and mapping technology, and particularly relates to a real scene point cloud completion surveying and mapping method, which comprises the following steps: obtaining a three-dimensional laser point cloud of a survey area, constructing a voxel evidence field and a boundary evidence field, and determining a target evidence object; determining an evidence deficiency object and a supplementary survey pose according to evidence completeness, performing supplementary survey scanning and updating evidence completeness; constructing a real scene entity based on the target evidence object satisfying a threshold value, and performing rigid body registration to obtain a real scene entity library; performing counter-evidence review on a building information model based on the real scene entity library, generating a dynamic deviation field, and outputting a completion topographic map, a parking space map and an external facade map. The present application can improve the completeness of key object acquisition, the explainability of model verification and the consistency of result map output.

This invention relates to the field of marine ecological monitoring technology, specifically to a method for monitoring planktonic larvae communities of *Sinocyclocheilus spp.* in nearshore waters. The method includes the following steps: S1, using a continuous plankton sampler with layered trawls to obtain samples containing *Sinocyclocheilus spp.* larvae and microscopically acquiring raw image data; S2, preserving the samples at a constant temperature and protected from light, and generating digital atlases containing texture features, edges, and confidence masks under microscopy; S3, inputting a multi-stage feature network to construct embedding vectors and outputting classification labels and instance masks; S4, performing morphological comparison and confidence backtracking correction based on the labels and masks; S5, combining filtered water volume to calculate abundance vectors and stage structure matrices to output ecological monitoring data. This invention achieves accurate identification, quantity statistics, and community abundance expression of *Sinocyclocheilus spp.* planktonic larvae, improving the intelligence and data assetization level of nearshore marine ecological monitoring.