75 results about "Hierarchical decomposition" patented technology

The invention provides a network security policy automatic execution method and system based on an artificial intelligence large model, and relates to the technical field of network security, and the method comprises the steps: firstly obtaining a network threat chain data set comprising a threat initiating node, an intermediate propagation node, a target attacked node and propagation path description; calling a pre-trained threat chain analysis large model to carry out hierarchical disassembly and variation trend prediction, outputting a threat chain disassembly map, then extracting a matching strategy gene segment from a preset security strategy gene pool, generating candidate security strategies through model gene recombination and cross-strategy collaborative adaptation, and carrying out threat chain analysis on the candidate security strategies; inputting the candidate strategies, the current network topology and the equipment resource load data into a strategy execution deduction system, outputting an executable security strategy set adaptive to the current network state, finally issuing the executable security strategy set to network security equipment, and collecting execution feedback data for updating the threat chain analysis large model. And automatic, efficient and accurate generation and execution of the network security policy are realized.

The invention discloses a multi-scale linear array camera splicing method and system based on point cloud. The method comprises the following steps: completing acquisition and preprocessing of point cloud data and image data of a target area; determining an overlapping region range between adjacent images; extracting spatial structure characteristics in the point cloud data, and performing multi-scale hierarchical decomposition on the point cloud through a multi-scale segmentation method; meanwhile, multi-scale image feature extraction is carried out on the images of the linear array camera; solving gradients in X and Y directions by adopting an optical flow method aiming at any pixel in the overlapping region, and calculating a motion vector between the pixels; fusing the optical flow information obtained under each scale, and constructing a globally consistent optical flow vector field; according to the fused optical flow vector, calculating to obtain a geometric transformation matrix of the whole overlapping region; and after image transformation and alignment are completed through the transformation matrix, fusion processing is carried out on overlapped areas. And the unification of the visual effect and the spatial integrity of the spliced image is ensured.

The invention relates to the technical field of environmental noise monitoring, and discloses a road traffic noise intelligent monitoring and three-dimensional sound field reconstruction system, which comprises an acoustic sensor module, a data collection and storage module, a data analysis and evaluation module, a three-dimensional sound field construction and display module and a traffic flow feature library. According to the system, a differential geometry principle is adopted, a sound field is regarded as a Riemannian manifold with a local microstructure, and accurate description of an irregular sound field is realized through a curvature self-adaptive sound field manifold construction technology; introducing a covariant derivative in Riemannian geometry, and constructing a sound propagation model adapted to a complex road environment; and realizing hierarchical decomposition and reconstruction of the sound field by using a multi-scale analysis theory. According to the method, the sound source positioning precision and the calculation efficiency are improved, seamless analysis from microcosmic to macroscopic is realized, an innovative solution is provided for traffic and noise collaborative management, and intelligent traffic and environmental noise management are effectively supported.

The invention relates to the technical field of embedded data storage, and discloses a data storage method and system for an embedded storage chip. The method comprises the following steps: acquiring a to-be-stored original data stream through a data acquisition interface and performing segmentation marking; performing hierarchical decomposition on the data segments by adopting a dynamic decomposition algorithm to generate a plurality of data components; calculating an information density index of each data component, and screening key data components and non-key data components according to the information density index; key data components are stored in a high-speed cache region, and non-key data components are stored in a conventional storage region; performing redundancy check on the key data components in the cache region and generating a check result; after correcting the key data component based on the verification result, recombining the key data component with the non-key data component to generate an optimized data stream; writing the optimized data stream into a physical storage medium and recording a storage position; and monitoring the storage load state, and dynamically adjusting the distribution proportion of the two storage areas.

The invention relates to the technical field of electrochemical energy storage systems, and discloses an electrochemical energy storage system battery charging effect evaluation method, which comprises the following steps: acquiring energy storage system operation data and battery charging records, extracting time sequence characteristics, equipment state characteristics and charging parameter characteristics, and calculating the charging effect of an electrochemical energy storage system battery. Constructing a multi-dimensional feature vector and generating an initial charging effect feature library; the evaluation model is dynamically adjusted based on feature library feedback, potential effect features are identified through mode clustering analysis, feature priorities are optimized, and model parameters are updated; and in combination with system configuration information and target model parameters, determining an optimal power compensation effect evaluation result by adopting a hierarchical disassembly and classification statistical method. According to the method, through multi-dimensional feature fusion, dynamic model optimization and refined statistical analysis, the accuracy and adaptability of electricity supplement effect evaluation are remarkably improved, and a scientific basis is provided for efficient operation and maintenance of the electrochemical energy storage system.

The invention provides an intelligent customer service query system and method based on natural language understanding, and relates to the technical field of natural language processing. A query intention and a core entity of a natural language query request input by a user side are extracted; performing hierarchical deconstruction on the query intention, and constructing a query semantic tree according to a hierarchical deconstruction result and a semantic association relationship between core entities; generating a decision tensor representing a query path priority according to a node semantic weight of the query semantic tree and an access delay feature of the multi-source database; further adjusting an access sequence and a connection strategy of the multi-source data through a decision tensor and a dynamically updated query cost model to obtain a query execution link adaptive to the current query request; and calling a corresponding data source interface according to the query execution link to execute data retrieval and integration operation to obtain a target result set responding to the query demand of the user. By the adoption of the scheme, multi-source data optimal cost retrieval based on the natural language can be achieved.

The invention discloses a manufacturing capacity trend prediction method based on AI visual analysis, and relates to the technical field of industrial big data analysis, and the method comprises the following steps: building a cross-time scale alignment baseline, generating a phase consistency evidence set of a continuous time structure through timestamp reconstruction and sampling calibration, and obtaining a phase consistency evidence set; extracting a high-confidence time anchor point as a unified time reference; and constructing a nonlinear disturbance spectrum diagram based on the time anchor points, and carrying out hierarchical decomposition on energy mutation, slow drift and pseudo-stable fragments in a continuous time period to generate a traceable disturbance fingerprint set. Through time sequence alignment, disturbance modeling, weight repair and dynamic regulation and control mechanisms, stable convergence and trend consistency prediction of the artificial intelligence model under nonlinear disturbance are realized, the accuracy of productivity prediction and the reliability of decision making are improved, and a manufacturing enterprise is assisted to realize intelligent productivity management.

The invention provides a script modification iterative optimization method and system and electronic equipment. The method comprises the following steps: analyzing an original script into a structured script data object; performing hierarchical decomposition and feature dimension distribution on the script optimization task; performing macroscopic analysis on the script data object based on a task decomposition scheme, generating a global evaluation result containing an improved strategy, and converting the global evaluation result into scene-level operation guidance; analyzing each scene based on operation guidance to generate modification suggestions, and integrating the modification suggestions to form a unified modification scheme; performing routing classification on the modification suggestions according to content types based on the scheme; executing modification operation for different elements of the script according to the suggestion distribution instruction; and evaluating the quality of the optimized script, and iteratively optimizing the script until a quality evaluation result meets a convergence condition. The problem of low generation quality caused by insufficient complex task processing capability, incoherent narration, difficulty in context consistency maintenance and the like in a single generation process and a normal form of an existing long script generation method is solved.

The invention provides a double-head ultraviolet laser cutting control method and system, and relates to the technical field of laser cutting, and the method comprises the following steps: obtaining geometrical characteristics of a workpiece, constructing a three-dimensional constraint model, and forming a multi-dimensional hierarchical decomposition tree; generating an initial cutting path and evaluating the priorities of the subtasks; in the execution process, a dynamic interference trend is pre-judged, and an obstacle avoidance rule set is established for path fine adjustment and working area reconstruction; and according to the machining difficulty, partition planning is conducted, partition coupling relation parameters are derived, and cutting parameters and movement speed are configured. Intelligent cooperative control of the double-end ultraviolet laser is achieved, the cutting efficiency and quality are improved, and interference and collision are avoided.

The invention relates to the technical field of spacecraft assembly, and provides a spacecraft assembly process template generation method and device. The method comprises the following steps: carrying out hierarchical disassembly based on spacecraft assembly process elements, and packaging each hierarchical element into a corresponding process structure body; defining a filling function corresponding to each process structure body to serve as a template function; user parameter input is received, the spacecraft assembly process template is generated based on the configured multi-class code building blocks according to the template function, and in the process of generating the spacecraft assembly process template, displayable debugging is conducted on the spacecraft assembly process template based on the building block identification. According to the method, the generation process of the spacecraft assembly process template is optimized, and the reuse efficiency of the template is improved.

The application discloses a hierarchical simulation geological modeling method under anisotropic tensor and uncertainty constraint, relates to the technical field of geological modeling, and comprises the following steps: inputting selected profile sample grade data, fitting a variation function and defining simulation parameters; performing conditional simulation according to the simulation parameters to generate a plurality of simulation implementations reflecting spatial anisotropy; controlling a hierarchical decomposition process through checking a termination condition, performing threshold smoothing processing on a sub-region after termination, saving an optimal threshold value and a class probability field of the sub-region, updating a global probability field by using a back transmission mechanism, outputting a grid of a final class probability field, and forming a rasterized ore body shape. The application obtains local threshold optimization through continuous hierarchical simulation and iteration, replaces unoptimized results, and forms optimal simulation results of the ore body, so that the uncertainty transmission of a traditional method is effectively reduced, and the accuracy and rationality of geological domain division are improved.

The embodiment of the invention relates to the technical field of vehicles, and discloses a dynamic total path sound insulation performance uncertainty optimization method and device, equipment and a medium. The method comprises the following steps: constructing a hierarchical decomposition framework of acoustic package sound absorption and insulation performance based on a power assembly; based on the power assembly, constructing a hierarchical decomposition framework of acoustic package sound absorption and insulation performance; constructing an ear noise prediction model based on a preset learning algorithm, the hierarchical decomposition architecture and the test parameter set; and on the basis of the basic parameter set, the ear noise prediction model, a Monte Carlo strategy and a preset optimization algorithm, completing global uncertainty optimization in a forward design stage. The problem of low accuracy in the acoustic packet performance prediction and optimization process is solved, the prediction accuracy is improved, and the engineering adaptability is improved.

The invention discloses a layered simulation geologic modeling method under anisotropic tensor and uncertainty constraints, and relates to the technical field of geologic modeling, and the method comprises the steps: inputting selected profile sample grade data, fitting a variation function, and defining simulation parameters; performing condition simulation according to the simulation parameters to generate a plurality of simulation implementations reflecting spatial anisotropy; and controlling a hierarchical decomposition process by checking a termination condition, performing threshold smoothing processing on the terminated sub-region, storing the optimal threshold and the class probability field of the sub-region, updating the global probability field by using a return mechanism, outputting a grid of the final class probability field, and forming a rasterized ore body form. According to the method, local threshold optimization is obtained through continuous layered simulation and iteration, an unoptimized result is replaced, and an optimal simulation result of the ore body is formed, so that uncertainty conduction of a traditional method is effectively reduced, and the accuracy and rationality of geological domain division are improved.

This invention discloses a language-conditional generalization learning method for deformable robot operations. Addressing the challenges of high-dimensional state space, dynamic complexity, and difficulties in cross-task transfer in deformable robot operations, it constructs a generalization framework integrating language understanding, skill decomposition, and cross-modal learning. The method collects multimodal demonstration data such as image sequences and natural language commands. It utilizes a large language model to perform semantic parsing and hierarchical decomposition of tasks, forming reusable basic skill units. Then, through a Transformer-based cross-modal skill learning model, it combines images and skill descriptions to predict operation target points and execution parameters, establishing a general skill library. When faced with a new task, the large language model generates skill sequences and calls upon the skill library for step-by-step execution, thereby achieving generalized operation of unseen tasks in both simulation and real robots, significantly improving the system's generalization, stability, and scalability.

An example computer-implemented method for temporal data analysis and forecasting utilizes topological hierarchical decompositions to process historical and future time windows. The method receives temporal data and generates multiple sets of historical time subsets with varying lengths, where information in shorter subsets is duplicated in longer ones. Future time windows are also generated in a similar manner. Future time windows are chronologically after a given initial time. The method creates past and future topological hierarchical decompositions and directed graph adjacency arrays. Customer attention matrices are generated for past and future windows, and matrix multiplications are performed to create self-attention arrays. These arrays are then multiplied together. The method culminates in providing a dashboard for forecasting demand after an initial time point, enabling comprehensive temporal data analysis and prediction.

The application belongs to the technical field of embodied intelligent robots, and particularly relates to an embodied intelligent robot long-term behavior generation method based on thought chain strategy decomposition; the method comprises the following steps: constructing a primitive action unit library of a robot operation scene; establishing a coordinate system model based on three-dimensional atomic action semantics; performing hierarchical task planning based on a thought chain for a target task; generating a subtask sequence with a unified interface; converting action instructions according to a corresponding strategy generation module according to a subtask type; performing real-time state feedback and dynamic adjustment in an execution process; and continuously optimizing the model by using online reinforcement learning. The application realizes hierarchical decomposition of long-term tasks through thought chain reasoning, decouples high-level strategy planning and low-level action execution, retains the advantages of large model reasoning while avoiding the uncertainty of directly intervening in low-level control, and realizes adaptive, safe and reliable robot operation in a complex scene.

The embodiment of the invention provides a cooperative task complexity analysis method and system based on task network characteristics, and the method comprises the steps: carrying out the hierarchical decomposition of a command and control task into atomic tasks according to four dimensions, constructing a task network model, traversing the task network model, extracting network scale characteristics, structural relation characteristics and uncertainty characteristics, and carrying out the calculation of the complexity of the atomic tasks. Complexity calculation is carried out on the network scale feature, the structural relationship feature and the uncertainty feature, a corresponding complexity quantification result is determined, and optimization adjustment is carried out on the task network model according to the complexity quantification result, so that the task network complexity of the task network model is reduced, and the task network complexity of the task network model is improved. The accuracy and efficiency of command and control task complexity measurement can be improved.

The application discloses a kind of multi-scale line array camera splicing method and system based on point cloud, method includes: the acquisition and pre-processing of target area point cloud data and image data are completed;Determine the range of overlapping area between adjacent images;And extract spatial structure features in point cloud data, carry out multi-scale hierarchical decomposition to point cloud by multi-scale segmentation method;While multi-scale image feature extraction is carried out to line array camera image;For any pixel in the overlapping area, solve X, Y direction gradient by using optical flow method, and calculate the motion vector between pixels;And the optical flow information obtained under each scale is fused, to construct global consistent optical flow vector field;According to the fused optical flow vector, the geometric transformation matrix of the whole overlapping area is calculated;After image transformation and alignment are completed by transformation matrix, the overlapping area is fused. Guarantee that the image after splicing is uniform in visual effect and spatial integrity.

The invention discloses a product quantization method based on hierarchical fuzzy clustering, and the method comprises the steps: carrying out the spatial decomposition of a to-be-compressed high-dimensional vector of image or video data into a plurality of low-dimensional subspaces through employing a hierarchical decomposition strategy; in each low-dimensional subspace, a hierarchical fuzzy clustering method is adopted, so that each data point belongs to a plurality of fuzzy clustering centers at different membership degrees; and on the basis of a hierarchical fuzzy clustering result, a product quantization coding mechanism is adopted, so that the fuzzy clustering center of each subspace forms a corresponding codebook, data points are coded and compressed according to membership degrees of the data points in different clustering centers, and data compression of a high-dimensional vector of the image or video data is realized. The invention provides a compression method combining a hierarchical structure and a fuzzy clustering technology, and the efficiency, the accuracy and the robustness of vector compression are remarkably improved.

The present application relates to the technical field of composite material detection, and specifically discloses a kind of composite material workpiece internal defect detection method and system, the method comprises: by obtaining defect detection feature and calculating feature overlap, judging modal feature degradation and constructing hierarchical decomposition model to separate thermal stress coupling signal, difference analysis area is required to be applied gradient humid heat load to generate dynamic response matrix, extract mutation direction angle and filter anisotropic interference by time-frequency coupling analysis, determine layered hidden boundary and boundary hidden area based on mutation direction angle, then image extraction geometric parameter is optimized by error correction matrix after model;System includes: overlap extraction module, confusion analysis module, difference analysis module, mutation identification module and feedback correction module, the present application is favorable to reduce the boundary fuzzy and small defect missed detection problem in traditional detection, realize the positioning and closed loop optimization of defect under multi-field coupling, improve composite material quality control efficiency.

This disclosure provides a fault diagnosis and self-healing control method for a pump cluster in a thermal power plant, relating to the field of intelligent automatic control technology for thermal power plants. The method includes: acquiring full-band acoustic signals from the pumps using an array of microphones; performing hierarchical decomposition, adaptive filtering, target acoustic band separation, feature extraction and dimensionality reduction, and principal component dimensionality reduction to obtain preprocessed target feature data; fusing the target feature data with pump operating parameters to reconstruct the state space of the target network and optimizing the reward function; outputting the probability distribution of fault types of the pump cluster based on the state space through a decision network in the target network; evaluating the output of the decision network through an evaluation network in the target network; and updating the decision network and evaluation network in conjunction with the reward function; and realizing fault diagnosis and self-healing control of the pump cluster based on the updated target network. This disclosure achieves automatic fault diagnosis and corresponding control in thermal power plants, ensuring the safe and efficient operation of thermal power plants.

This invention belongs to the field of enterprise resource planning (ERP) technology and discloses an intelligent matching method and system for policy application conditions based on enterprise resource analysis. By constructing a dedicated extraction model for policy application elements, it performs structured parsing and industry-customized four-level hierarchical decomposition of policy texts, transforming natural language application conditions into executable logical expressions. It accurately handles complex rules such as AND, OR, non-nested logic, numerical thresholds, and temporal constraints. A four-level progressive matching and verification mechanism is adopted, prioritizing matching according to the order of prohibition (one-vote veto), rigid access, core scoring, and auxiliary bonus points. It incorporates industry-specific rules and enterprise lifecycle adaptation logic to avoid invalid matching and screening biases, upgrading policy matching from superficial textual association to deep compliance verification, thus improving matching accuracy and adapting to the application and screening needs of various complex preferential policies for enterprises.

The invention relates to the technical field of microfluidic control, and discloses a microfluidic droplet forming detection, regulation and control method and system.The method comprises the steps that an ideal concentric annulus geometric model is constructed by obtaining microcapsule target parameters, a force-shape coupling response matrix is established, and a preset flow velocity triad is solved according to the force-shape coupling response matrix to generate initial droplets; then collecting a liquid drop image sequence, fitting a real-time concentric circle group, carrying out geometric superposition on the real-time concentric circle group and the ideal concentric circle geometric model to generate a deviation annular domain, and carrying out hierarchical decomposition to obtain geometric deformation characteristic components; and finally, calculating a collaborative flow velocity regulation vector by combining the force-shape coupling response matrix, constructing a composite fluid correction field, and iteratively executing a deviation ring ablation cycle until geometric convergence is realized. According to the method disclosed by the invention, the problem of oscillation caused by strong coupling of parameters in a multiphase flow field is effectively solved by constructing a quantitative mapping and reverse decoupling mechanism of the fluid and the geometrical morphology, and collaborative precise control of the external dimension and the internal structure of the microcapsule is realized.

The invention provides a general characteristic and performance flow design method for a solid engine, and relates to the technical field of solid engine design, and the method comprises the steps: decomposing the whole design process of the solid engine into a plurality of design subtasks; constructing an initial design structure matrix; converting the initial design structure matrix into a standard design structure matrix; identifying and dividing a coupling task set and a non-coupling task set based on the standard design structure matrix; the design subtasks in the coupling task set are mutually dependent; sequencing the design sub-tasks in the uncoupled task set to obtain a first sequence; sequencing the design sub-tasks in the coupling task set to obtain a second sequence; based on the first sequence and the second sequence, recombining the overall design process of the solid engine to obtain an optimal design process sequence; graded disassembly and ordered carding of the complex nested coupling relation of the solid engine are achieved, and the overall design efficiency and design quality of the solid engine are remarkably improved.

This invention discloses an underwater image enhancement method and system based on hierarchical decomposition and fusion, comprising: constructing an HDNet model; training the HDNet model; and performing underwater image enhancement using the trained HDNet model. The HDNet model, according to the image processing procedure, sequentially includes: a first point convolutional layer for expanding the channels of the input image; a first channel-specific color correction module for splitting the channels of the input image and adjusting the color distribution of each channel individually; a first feature fusion module for fusing the colors of each channel; a pixel-specific detail adjustment module for representing pixels using various wave functions and modulating the signal; a second feature fusion module for further fusion; a second channel-specific color correction module for adjusting the color distribution of each channel to achieve a normal distribution; and a second point convolution for compressing channels to output the enhanced image.

The invention provides a station dense passenger flow detection method based on occlusion hierarchical adaptive decomposition, and the method comprises the steps: carrying out the adaptive hierarchical decomposition of similarity gradient drive, a cross-hierarchical feature transmission and constraint mechanism, boundary attention enhancement and occlusion boundary precise positioning, and a hierarchical self-verification and restoration mechanism. And accurate detection of multi-layer shielding pedestrians in a high-density people stream environment is realized. According to the method, the boundary attention mechanism of the adaptive parameter beta and the hierarchical consistency evaluation function of spatial position weighting are introduced, so that the sensitivity of the occlusion boundary area is remarkably improved, the omission ratio in the occlusion scene is reduced, and the basic requirements of station safety monitoring are met.

The invention discloses a hierarchical multi-level power distribution network distributed photovoltaic reactive power regulation method. The method comprises the following steps that a power distribution system operator trains a depth deterministic strategy gradient model and sends the depth deterministic strategy gradient model to a central server; the edge server collects the voltage of a bus connected with the photovoltaic entity; the edge server uploads the bus voltage data to a central server, calls a trained depth deterministic strategy gradient model, generates a bus reactive power output plan, and issues the bus reactive power output plan to the edge server; and the edge server controls the photovoltaic entity to output corresponding reactive power according to the bus reactive power output plan issued by the central server, and feeds back an execution result to the central server. According to the method, deep reinforcement learning is combined with a hierarchical decomposition mechanism, a reactive power scheduling scheme of a distributed photovoltaic entity in a node is rapidly calculated, scheduling delay and system cost are remarkably reduced through the mechanism, meanwhile, stable control over key node voltage is ensured, and unification of power grid safety and economical efficiency is achieved.

This invention relates to the field of data retrieval technology, specifically a supply chain data retrieval and governance method based on multimodal deep learning. The method includes: classifying the input supply chain data according to its business processes to construct a set of business processes containing multiple business nodes; determining the patent entities and enterprise entities of different business processes through patent technology queries based on the patent technologies corresponding to the business process sets, and locating the associated retrieval network; performing vertical hierarchical decomposition and horizontal path decomposition on the associated retrieval network to clarify the retrieval scope of the current supply chain data; determining the minimum number of associated units during retrieval and configuring industry feature clusters based on the semantic extension of each associated unit; and determining a condition configuration list for each acquired industry feature cluster. This method achieves high efficiency and scenario adaptability in data retrieval.

According to the multi-modal personalized content generation and interpretation method and device for interaction between the user and the expert agent, the medium, the program product and the terminal provided by the invention, a traditional single text output mode is broken through, personalized generation of multi-modal contents such as texts, voices and videos is realized in combination with user knowledge preferences and user cognitive features, and the user experience is improved. The multi-modal personalized content adapts to receiving habits of different users. According to the method, the domain knowledge graph and the interaction context are fused, hierarchical disassembly, cross-domain association and personalized application suggestion of knowledge are realized, a user is helped to quickly master core knowledge and application scenes through deep interpretation, and the problem that interpretation of a traditional scheme is shallow is solved. According to the method, a closed-loop feedback optimization mechanism of user feedback, model optimization and strategy adjustment is established, it is ensured that knowledge generation and interpretation ability is dynamically upgraded along with user requirements, the method adapts to continuously-changing user requirements and domain knowledge updating, and therefore the service quality is continuously improved.

The invention relates to the technical field of artificial intelligence, in particular to an AI-based construction quality prediction method and system, and the method comprises the following steps: obtaining a task level characteristic association constraint process, recognizing constraint conflicts, screening consistent paths, extracting a parallel optimization fusion cooperation strategy, comparing, executing and correcting conflicts, collecting periodic data, evaluating consistency, and judging convergence. And outputting a collaborative construction convergence robust state identifier. According to the method, a dynamic decision chain is constructed by combining hierarchical decomposition characteristics and semantic analysis association, so that synchronous carding and conflict identification of flow constraint and variable relationships are realized, consistency check and direction screening of execution paths are enhanced, a control instruction system with collaborative characteristics is formed, the association perception capability of multi-cycle execution is effectively improved, and the reliability of the multi-cycle execution is improved. The real-time correction capability of mass offset is enhanced, the consistency trend judgment of the period is optimized, the operation of the construction process in a convergence interval is ensured, and decision deviation accumulation and risk diffusion are reduced.